Case Studies2023-06-12T07:13:39+00:00


This listing contains select case studies from our various solution offerings. If you need similar solutions or more information about our track record, contact us or email us at to start a conversation.


The client is a startup by the co-founder of one of the largest video game developers in the world, with a focus on social 3D computing. As its first market offering, the client developed a Metaverse where real people love to meet, collaborate, create, or just talk, using its proprietary OS.

The proprietary OS is a software layer providing an abstraction of reality and computing resources through virtualization technologies, unifying access to the virtual world by replicating reality — not only graphically and including groundbreaking visual quality and effects, but also through incorporation of physics and additional characteristics further enriched by sensor data from various real-world elements, as well as simulation and AI services.

The client was looking for a partner to develop the application layer for its market offering and chose Sakha based on our innovative track record.

The Challenge

  • With in-person meetings looking implausible during Covid and people suffering from Zoom fatigue, the client wanted something with the feeling of a physical event.
  • Building an application wrapper around the proprietary OS which takes care of the entire user flow & processes.
  • User login management, sessions management and monetization management.
  • Conversion of the renderings from proprietary OS to OpenGL in real-time in the app.
  • Integrations with work tools such as Google Workspace and Microsoft Teams.
  • Work with teams in multiple geographies and time zones against agile sprints with tight deadlines.


Sakha developed the 3D space app – a new solution that takes video conferencing to the next, more powerful, and personal level. The communications platform breaks with the typical video conference boxed layout and seats participants in rooms of all sizes to meet, learn, and work in a gamified way.

Users are transported into a virtual space as a cut-out video stream with no need for VR equipment or avatars – a spatially immersive meeting experience no other platform offers. The 3D space app is the only Metaverse that users can enter with a real presence. There are various 3D immersive settings for meetings – including a yacht, tavern & even the moon!

Apart from login management & sessions management, we integrated the app for meeting invites with tools such as Google Workspace and Microsoft Teams. We also integrated payment gateways for purchase of various room settings & number of participants. The app was also developed to be cross platform compatible.

The app was showcased in prestigious events such as Virtual Experience by the Jacques Rougerie Foundation under the high patronage of HSH Prince Albert II of Monaco, and the South by Southwest (SXSW) Conference 2021.


The client is a leading entertainment company that nurtures, creates, and develops content studios that create fabulous content aimed at audiences worldwide and build sustainable businesses. The client had an idea to launch a marketplace to sell content as digital assets. The client wanted to employ NFT technology to transfer the right of ownership to a buyer and thus justify an asset’s uniqueness and value. The NFT market in the content space was relatively new so the client was looking to accelerate to take a niche position. With the idea in mind, the client was looking for a competent team to plan out and develop the Blockchain-based solution. The client commissioned Sakha as its partner to prepare the PoC, roadmap and the development project taking into account our expertise in the development of Blockchain-based apps.

The Challenge

  • As the Customer’s vision of the marketplace to-be was vague, our team built a business case to understand the marketability of the Customer’s idea and uncover possible risks.
  • Preparation of the technology stack, the feature map, architecture and infrastructure design for the marketplace.
  • User journeys for marketplace sellers & buyers and a detailed specification of needed functionality.
  • Multiple login options (via email and password, social media, digital wallets).
  • Custom catalog widgets helping in navigation, like new or most expensive items on sale.
  • Flexible payment via a crypto wallet, credit cards, mobile payment.
  • NFT minting capabilities for non-technical people.
  • Integration with numerous Blockchains.
  • Auction bidding for NFT.
  • Intuitive navigation and a Web3 compatible interface.


The platform offers IP holders a full suite of features to take control of their product licenses and data by being able to collect revenue from any source, platform, or distributor around the world.

Key features of the Blockchain platform include streamlined onboarding, custom royalty models per asset via smart contracts, recoupable expense options, one-click deployment, receive payments, automatic coin conversions and transparent & automatic reporting.

A user-facing web application integrated with web3 and multiple crypto wallets. The integration simplified the payment step for users who could instantly convert a fiat currency into crypto assets to pay for NFTs. The front end of the web app was planned in React.js and the back end – in Golang.

LevelDB was used to store the binary data of Ethereum nodes while AWS S3 and IPFS were intended to store digital assets.

Smart contracts were used to automate the verification of Blockchain transactions. To speed up the development of smart contracts, our team offered to use ready libraries of OpenZeppelin.

An Ethereum-based Blockchain network was built on Polygon framework. Polygon was employed to bypass high transaction costs and slow processing of Ethereum.


Client specializes in dialectal speech technology solutions for Dialectal Arabic and other under-resourced languages. Automatic speech recognition & text-to-speech are some of the areas the client focuses on. The client is part of the Mohammed Bin Rashid Innovation Fund (MBRIF), an initiative launched by the UAE Ministry of Finance to support innovation in the UAE. The client set out to find a partner that would meet its solution development & scaling needs and immediately found traction with us.

The Challenge

  • Arabic is considered as one of the challenging languages to be used in speech recognition systems due to its large lexical variety and complicated morphology.
  • One of the significant challenges is the automatic detection & conversion of over 19 Arabic dialects.
  • Building lexicons for various use cases such as media, call centers & education.
  • Support for multiple file types – wav, mp3, mp4, acc, and more.
  • Ability for both real-time as well as batch processing.


We developed the product based on automatic speech recognition, machine translation, and Natural Language Processing (NLP). The product can be deployed on the cloud, on premise as well as hybrid model. The Arabic speech recognition models have the leading accuracy across the board. We also built speech to text features such as speaker detection, language switching, time stamps, and diarization.

The solution was winner of the 2021 GITEX Future Stars’ Supernova Challenge held in Dubai and was named as the Best AI innovator for its cutting-edge Arabic speech and voice technology. It won from over 700 entries.

The solution can provide highly trained and tailored transcriptions to the clients’ customers with greater than 90% accuracy levels. By pushing our models to perform under complex, real-life conditions with background noise, multiple speakers and diverse accents, the clients’ customers achieve vastly improved accuracy rates without compromising on the speed of the transcription.

The clients’ customers use built-in reporting to look for keywords and phrases in collected audio data rather than a faulty outputted transcript, enabling them to seamlessly pinpoint specific timestamps and gather helpful insight.

Having Sakha as a technology partner that innovates rapidly and delivers quality, the partnership with the client continues to open up new opportunities at scale.


The client is one of the fastest growing Big Data Automation software companies in the world, with over $71 M in funding. The client has developed a product to accelerate cloud migration and modernize its customers’ cloud data operations – automatically. The product installs on its customers’ cloud and automates the migration of data, metadata, and workloads from Enterprise Data Warehouses and Hadoop Data Lakes to the cloud faster, with fewer resources, and at a fraction of the cost of traditional approaches. The platform simultaneously establishes scalable and agile modern cloud data operations to automate the creation and deployment of new analytics use cases.

The Challenge

Though the client had a large sales team, it was not growing to its full potential owing to these challenges:

  • Lack of adequate in-house expertise: The client lacked the in-house expertise required to perform data and metadata ingestion, build data pipelines, or migrate workloads to a new Big Data environment. This was leading to delays, errors, and other problems that can negatively impact the migration process.
  • Resource constraints: The client did not have enough resources available to dedicate to a Big Data migration project. This included issues with staffing, budget, or competing priorities.
  • Complexity of migration: Big Data migration projects can be incredibly complex, particularly when dealing with large volumes of data, complex data structures, or a variety of data sources. Without the right expertise and tools, it can be difficult to successfully migrate all of the necessary data and ensure that it is properly integrated and accessible.
  • Time constraints: Many companies may need to migrate their Big Data environments quickly, particularly if they are facing regulatory or other compliance issues. Without the right resources and expertise, it can be difficult to meet these deadlines.
  • Need for ongoing support: Migrating to a new Big Data environment is just the first step. Companies also need ongoing support to ensure that their data is properly managed, secured, and accessible over time. An expert partner can help provide this ongoing support and ensure that companies can get the most value from their Big Data environment.

To address these challenges, the client partnered with Sakha for their customer engineering tasks and also to develop customized product extensions through a dedicated offshore center.


Sakha participated in customer engineering tasks both at the client’s customers’ locations as well as through a dedicated offshore team to provide both time & cost benefits for the client. The tasks performed included:

  • Data onboarding
    • Data and metadata ingestion
    • Synchronization between on-premises and cloud
    • Data governance and lineage
  • Preparing data for analytics and optimizing for performance
    • Data pipelines
    • Data transformation
    • Data models
    • Workload migration
  • Running analytics at scale and realizing the value of the customers’ data
    • Promote to production
    • Hybrid multi-cloud deployment
    • Orchestration
    • Workload migration

Through the dedicated offshore team, Sakha developed several native data connectors, covering categories such as File & API, Marketing, NoSQL, RDBMS, Storage, Streaming, EDW, E-Commerce and more.

Sakha also developed an end-to-end automated solution for seamless Hadoop data and metadata migration that can continuously replicate across clusters in a background process.

The client realized several benefits from partnering with Sakha for customer engineering and product extension services:

  • Increased revenue: By offering a more comprehensive solution that includes both software and services, the client could increase their revenue from their customers.
  • Improved customer satisfaction: By providing customers with access to Sakha’s expert services and support, the client improved on its customer satisfaction and retention.
  • Reduced workload: By outsourcing some of the customer engineering services and extensions development services to a trusted services provider, the client could continue to focus on their core product development.
  • Access to specialized expertise: By partnering with Sakha that specializes in data engineering, the client could gain access to specialized expertise that they did not have in-house.
  • Increased scalability: By leveraging Sakha’s resources and expertise, the client could more easily scale their offerings and support larger customer bases.
  • Competitive advantage: By offering a more comprehensive solution that includes both software and services, the client could differentiate themselves from competitors and gain a competitive advantage.
  • Better feedback loop: With Sakha in the loop, it became easier for the client to get feedback and suggestions for improvement from their customers.

In conclusion, partnering with Sakha for customer engineering services proved to be a win-win situation for the client. The client could leverage the expertise and resources of Sakha to deliver seamless and efficient migration services to their customers, while also enjoying benefits such as increased revenue, enhanced customer satisfaction, and improved competitive edge. By addressing the complex challenges and providing end-to-end solutions for their customers, the client established itself as a reliable and trusted partner in the Big Data ecosystem.


A content developer for learning programs in the areas of vocational training, K-12, etc wanted to scale up in the market by providing a learning management system which could easily be populated with its content for various market use cases. This was to be positioned as an enabler of the Indian government’s Skill India mission. Sakha was the tech partner for the client for over 6 years, in the development of a content-agnostic learning management system.

The Challenge

Many institutions and organizations face challenges in delivering effective education or training programs. These challenges include the lack of personalization and flexibility, inadequate assessment and feedback, and difficulty in measuring learning outcomes. In addition, institutions and organizations face difficulty in keeping up with the rapidly evolving digital technologies and teaching methods.

The client wanted to tackle these challenges by providing a content-agnostic learning management system in the market for various use cases. Since the client was already a leading content developer, it could easily bundle the content for various scenarios such as vocational training.


To address these challenges, Sakha developed a content-agnostic learning management system that enables institutions to offer a personalized, flexible, and interactive learning experience for their students or trainees. The system is designed to incorporate various types of content, such as vocational training, K-12, teacher training, and other specialized content.

The Indian government’s Skill India mission aims to equip millions of Indians with job-relevant skills to enhance their employability and contribute to the country’s economic growth. In line with this mission, Sakha developed the content agnostic learning management system that facilitates self-paced and interactive learning, both in virtual and physical environments.

The solution addresses several challenges that learners and educators face in traditional classroom-based learning, including limited access to high-quality educational resources, lack of personalized learning experiences, and limited opportunities for feedback and assessment. By leveraging digital technologies, the learning management system provides an intelligent learning experience that is tailored to the needs and interests of individual learners.

One of the key features of the learning management system is its content agnostic nature, which allows it to be populated with various types of educational content, including vocational training, K-12 education, teacher training, and more. This makes it a versatile solution that can be used across a wide range of industries and educational contexts.

The system also incorporates a range of interactive and multimedia elements, such as video lectures, simulations, and gamified quizzes, to enhance engagement and retention. To further facilitate personalized learning, the system leverages artificial intelligence and machine learning algorithms to recommend learning pathways and content based on individual learner preferences and performance.

In addition to facilitating learning, the solution also includes a scientific assessment mechanism that enables educators to measure learner cognition and retention. This helps educators to identify areas where learners may require additional support and to tailor their teaching approaches accordingly.

Features of the solution include:

  • Self-paced and interactive learning: The system allows learners to learn at their own pace and interact with the digital content in various ways, such as quizzes, simulations, and gamification.
  • Intelligent digital content: The system includes a variety of digital content types, such as videos, audio, text, and images, to cater to different learning styles.
  • Delivery in virtual and physical environments: The system enables the delivery of content in various formats, such as online, blended, or in-person, to suit different learning environments.
  • Scientific assessment mechanism: The system provides various types of assessments, such as formative and summative assessments, to measure learner cognition and retention and provide feedback to the learners and instructors.
  • Personalization: The system allows learners to customize their learning journey based on their interests, learning preferences, and skill level.
  • Flexibility: The system allows instructors to customize the content and learning activities to meet the unique needs of their learners and adjust the pace & difficulty level of the content as needed.
  • Integration: The system can be integrated with other educational technologies and tools to enhance the learning experience, such as student information systems, learning analytics, and assessment tools.

The content agnostic learning management system provides a comprehensive and flexible solution for learners and educators in the context of the Skill India mission. By leveraging digital technologies, it enables learners to acquire job-relevant skills in a self-paced and interactive manner, while providing educators with valuable insights into learner cognition and performance.

The system can quickly be populated with different types of content related to vocational training, K-12 education, and teacher training. This is made possible by creating a modular and flexible architecture that can accommodate different types of content in a seamless manner.

For example, the client can create their own content modules that are compatible with the learning management system, based on their customers’ needs. These modules can be quickly integrated into the system through a simple drag-and-drop interface. This means that new content can be added to the system without having to modify the existing structure or codebase.

Additionally, the learning management system can support different content formats, such as video, audio, text, and interactive simulations. This flexibility allows the client to integrate and educators to deliver a variety of content types to cater to different learning styles and preferences.

The system also features advanced search and recommendation algorithms that can suggest relevant content to learners based on their preferences, learning history, and performance. This makes it easier for learners to find the right content at the right time, and for educators to identify knowledge gaps and adjust their teaching strategies accordingly.

The system offers a highly scalable and customizable platform that can be quickly populated with a diverse range of content. This makes it an ideal solution for organizations and institutions looking to provide self-paced and interactive learning experiences across a variety of domains.

Benefits of the system include:

  • Personalized learning: The system enables learners to personalize their learning journey, allowing them to learn at their own pace and based on their interests and preferences.
  • Flexibility: The system enables instructors to customize the content and learning activities, providing flexibility in the delivery of educational programs.
  • Improved learner engagement: The system’s interactive and self-paced learning features promote learner engagement and motivation.
  • Enhanced learning outcomes: The system’s scientific assessment mechanism and personalized learning approach can lead to improved learning outcomes and higher retention rates.
  • Cost-effective: The system can reduce the cost of education delivery by providing a scalable and efficient solution for institutions and organizations.

Sakha’s solution has been implemented in several large organizations such as Siemens as well as select Government schools, such as in Rwanda & Maharashtra, India. The product was the top Asian Finalist in 2018 edition of Inclusive Innovation Challenge – the flagship initiative of MIT, USA.


Sakha’s client is an Indian multinational motorcycle manufacturer headquartered in Chennai. It is the third largest motorcycle company in India in terms of revenue. It had a large fleet of vehicles with telematics devices installed, generating a massive amount of data on a daily basis. Sakha empowered the client through a telematics-based Big Data Lake and data pipelines project using Microsoft Azure.

The Challenge

The motorcycles manufacturer had a large fleet of vehicles with telematics devices installed, generating a massive amount of data on a daily basis. However, the company lacked a unified platform for storing and processing this data, leading to siloed data storage, duplication, and lack of standardized data formats. As a result, the company was facing challenges in gaining insights from this data, including identifying trends, predicting maintenance issues, and improving the overall customer experience.


The solution was to create a telematics-based Big Data Lake and data pipelines project using Microsoft Azure. Sakha’s solution included the following features:

  • Data ingestion and processing: Azure Event Hubs were used to ingest telematics data from the motorcycles in real-time. The data was then stored in an Azure Data Lake Storage Gen2, which allowed for efficient storage and retrieval of large amounts of unstructured data.
  • Data transformation and cleansing: Azure Data Factory was used to extract, transform, and load (ETL) data from various sources and transform it into standardized formats. This helped in reducing data duplication and cleaning the data for analysis.
  • Data analysis and insights: Azure Databricks was used for running data science workloads, including machine learning and predictive analytics, on the cleaned and transformed data. This allowed for the identification of trends, predictive maintenance, and improved customer experiences.
  • Data visualization and reporting: Power BI was used to create interactive visualizations and reports for business users to gain insights from the data.

The solution provided several benefits to the motorcycles manufacturer, including:

  • Improved customer experience: The telematics-based Big Data Lake and data pipelines project enabled the company to identify trends and patterns in customer behaviour, allowing for personalized recommendations and improved customer experiences.
  • Reduced maintenance costs: Predictive analytics enabled by the solution allowed for the identification of potential maintenance issues before they occurred, reducing downtime and maintenance costs.
  • Increased operational efficiency: The centralized data storage and processing platform allowed for faster and more efficient data analysis, enabling the company to make data-driven decisions quickly.
  • Improved data quality: The data transformation and cleansing process improved the quality of the data, reducing data duplication and errors in analysis.
  • Scalability: The solution was built using cloud-based technologies, which allowed for scalability and cost-effectiveness in managing large volumes of data.

Sakha’s telematics-based Big Data Lake and data pipelines project using Microsoft Azure provided a unified platform for storing, processing, and analyzing telematics data, enabling the motorcycles manufacturer to gain valuable insights and improve customer experiences while reducing maintenance costs and increasing operational efficiency.


Now acquired by Gartner, Sakha’s client was a technology solutions provider building BI products to manage talent, customers & operations. Sakha was the tech partner in the development of the client’s flagship talent analytics suite, powered by ML. The suite is being used by nearly 90% of the Fortune 500 and FTSE 100 companies.

The Challenge

The client wanted to address the challenges in collecting and analyzing data from multiple sources to provide talent analytics services to their customers, by building an automation platform. The existing system was time-consuming, error-prone, and lacked scalability. The client needed a solution that could automate the data harvesting, segregation, formatting, transformation, and analysis processes.


To address these challenges, the client partnered with Sakha to develop an ML-powered talent analytics product. The product could collect and analyze data from over 100 different sources, including job boards, social media, and enterprise data, and process over 500 million records. The ML algorithms could identify patterns and insights from the data that would be difficult or impossible for a human analyst to identify. The product could also be customized to meet the specific needs of each customer.

The product had the following features:

  • Automated data harvesting, segregation, formatting, transformation, and analysis
  • ML algorithms for identifying patterns and insights
  • Customizable to meet the needs of each client
  • Secure data storage and sharing

A few use cases of the product include:

  • Identifying high-potential employees: The product can analyze data from employee performance evaluations, job histories, education and training records, and other sources to identify employees who have the potential to succeed in more senior roles. The machine learning algorithms can identify patterns that might be missed by human analysts.
  • Predicting attrition: By analyzing data such as employee engagement surveys, attendance records, and salary history, the product can predict which employees are at risk of leaving the company. This allows managers to take proactive steps to retain key talent.
  • Optimizing compensation: The product can analyze data on compensation levels, job titles, and performance ratings to identify areas where the company may be overpaying or underpaying employees. This can help the company adjust compensation levels to be more competitive and equitable.
  • Supporting diversity and inclusion efforts: The product can analyze data on hiring, promotion, and retention rates for employees from different demographic groups to identify areas where the company may be falling short in terms of diversity and inclusion. This information can help the company develop more effective strategies for recruiting, hiring, and promoting a diverse workforce.
  • Developing targeted training programs: By analyzing data on employee skill levels, performance ratings, and career aspirations, the product can help identify skill gaps and areas where additional training may be beneficial. This allows the company to develop targeted training programs that are more likely to be effective in improving employee performance and retention.

The ML-powered talent analytics product offered several benefits:

  • Accurate and reliable data analysis: The ML algorithms could analyze data from multiple sources and identify patterns and insights that would be difficult or impossible for a human analyst to identify.
  • Time-saving: The automated data processing and analysis saved time and reduced the risk of errors.
  • Scalability: The product could process large amounts of data, making it suitable for clients with large workforces or complex data needs.
  • Customizable: The product could be customized to meet the specific needs of each customer, allowing them to get the most relevant insights.
  • Better decision-making: The insights provided by the product could help clients make better decisions about talent acquisition, retention, and management, leading to improved business outcomes.

The product’s ML algorithms could analyze data from multiple sources and identify correlations, trends, and predictive models that could be used to make informed decisions about talent management.

The talent analytics product required automated data harvesting, segregation, formatting, transformation, and analysis to effectively analyze and derive insights from the vast amounts of data it collects from various sources. Sakha built the end-to-end workflow for the same.

  • Data Harvesting: The first step in the process is to collect data from multiple sources such as social media, job portals, internal HR systems, and other third-party sources. This data is typically in different formats and structures, and needs to be collected and consolidated in a uniform format for further analysis.
  • Data Segregation: Once the data has been collected, it needs to be segmented based on different parameters such as job function, location, experience level, and other relevant criteria. This enables easier analysis and comparison of data across different segments.
  • Data Formatting: The next step involves formatting the data in a way that it can be easily analyzed by machine learning algorithms. This includes removing inconsistencies, standardizing naming conventions, and other formatting steps.
  • Data Transformation: Once the data has been formatted, it needs to be transformed into a format that can be used for analysis. This may involve creating new features, aggregating data, or any other necessary transformations.
  • Data Analysis: The final step is to analyze the data using machine learning algorithms to derive insights and make predictions. This can include identifying skill gaps, predicting attrition rates, and other insights that can help improve talent management.

The key to the talent analytics product being effective was the ability to automate these steps and create a scalable, reliable data pipeline that can handle large amounts of data and derive actionable insights from it.

Overall, the ML-powered talent analytics product provided accurate, reliable, and customizable insights that helped the client’s customers make better decisions about talent management, leading to improved business outcomes, and eventually being acquired by Gartner.


The client is a Fintech startup which provides loans to grey-collar employees through its app. While the founders had banking background, the technology & data science expertise were provided by Sakha.

The Challenge

While everyone faces financial emergencies at one point or the other, formal credit systems such as banks and NBFCs are not eager or readily available to give loans to people from the lower middle class and neglected sections of society in India – such as housekeepers, fruit / vegetable vendors, etc.

The interest rate charged by informal channels has no limit and people’s perils are distressing. Borrowers are led into a vicious cycle of loans. The founders realised that the Next Billion in India was completely underserved by traditional, organised financial firms. They needed a streamlined and transparent way to borrow credit. The client hit upon the idea of harnessing the power of mobile phones, enabling ‘credit to all’ option.


Sakha helped the client in building India’s next-gen digital lending and savings platform. Sakha has developed unique data science models to democratize credit for millions of borrowers. Some of the features of the app include:

  • Real-time analytics and credit reports based on alternative data enable the client to reach unserved and underserved market segments.
  • The app makes fair and transparent credit available to people to provide meaningful financial inclusion.
  • The app has further strengthened its financial inclusion goal through its financial wellness program, for corporates and individuals.
  • Now, the app also includes mini and micro loans, insurance, Bill Payments, mutual fund SIPS, E Gold and E Tax filing service.
  • Apart from English, the app also provides support for customers in regional languages such as Kannada, Tamil, Hindi, Telugu, and Marathi.

Traditional credit reporting agencies often rely on credit history and financial records, making it difficult for underserved segments of society, such as housekeepers, vendors, and others who may not have traditional credit records, to access financial services. To address this gap, Sakha’s data scientists used alternative data sources and machine learning models to develop credit reports for underserved populations.

Sakha used utility bill payment history, mobile phone usage, and other non-financial data sources to build a more comprehensive profile of an individual’s creditworthiness. We also built models to analyze transaction data from mobile payment services used by housekeepers and vendors to identify trends in their income, spending, and savings behaviour.

By leveraging these alternative data sources and applying machine learning models to the data, Sakha’s data scientists created credit reports that are more comprehensive, accurate, and inclusive of underserved populations.

The client has raised USD 27.7 Million in 6 rounds of funding, and now aims to become a full stack Digital Bank by 2026.


The client is a non-banking finance company (NBFC) registered with the Reserve Bank of India (RBI). The client was looking for a way to digitize their lending operations and offer an easy-to-use, AI-powered digital credit app for their customers. Sakha, as a tech partner, empowered the client through development of the app.

The Challenge

The client was looking for a way to digitize their lending operations and offer an easy-to-use, AI-powered digital credit app for their customers. They also wanted to provide spending and savings analytics to help customers better manage their finances. However, they faced the challenge of developing a solution that could process large amounts of data in real-time while ensuring the security and privacy of their customers.


The NBFC partnered with Sakha to develop an AI-powered digital credit app that would allow customers to apply for loans, track their spending, and manage their savings all in one place. The app was built on a Big Data platform that could process large amounts of data in real-time, enabling the company to offer instant credit decisions to its customers. The solution leveraged machine learning algorithms to analyze customers’ spending and saving patterns and provide personalized recommendations to help them manage their finances better.

The app’s key features included:

  • Easy loan application process
  • Spending and savings analytics
  • Personalized financial management recommendations
  • Real-time credit decision-making

The AI-powered digital credit app provided the NBFC with several benefits, including:

  • Improved customer experience: The app provided an easy-to-use interface that enabled customers to apply for loans, track their spending, and manage their savings all in one place, improving their overall experience. The app resulted in a 20% increase in customer satisfaction ratings.
  • Faster credit decisions: The solution could process large amounts of data in real-time, enabling the NBFC to provide instant credit decisions to its customers. There was a 30% increase in loan application processing speed.
  • Personalized financial management: The machine learning algorithms analyzed customers’ spending and saving patterns and provided personalized recommendations to help them manage their finances better.
  • Increased operational efficiency: The app digitized the lending process, reducing paperwork and manual processing, leading to increased operational efficiency. There was a 25% reduction in loan processing costs.
  • Improved risk management: The app leveraged machine learning algorithms to analyze customer data and assess credit risk, leading to better risk management and reduced losses. There was a 15% decrease in loan default rates.


The client has more than two and half decades of experience across global capital markets, and provides robust and long term investment options for investors with varied risk profiles. The client partnered with Sakha’s team of AI experts and software developers to build an AI-powered stock portfolio management platform.

The Challenge

The client was looking for a way to provide its customers with more personalized and intelligent investment recommendations. They wanted to leverage AI and machine learning to develop a platform that could analyze a large volume of financial data and generate customized investment ideas and stock portfolios for each individual client.


To address this problem, the investment management company partnered with Sakha’s team of AI experts and software developers to build an AI-powered stock portfolio management platform. The platform uses machine learning algorithms to analyze financial data such as stock prices, earnings reports, and other relevant information to identify investment opportunities and generate customized stock portfolios for each customer.

The platform includes the following features:

  • Personalized investment recommendations: The platform uses AI and machine learning algorithms to analyze each customer’s financial goals, risk tolerance, and other relevant factors to generate personalized investment recommendations and stock portfolios.
  • Real-time portfolio management: The platform provides real-time portfolio management tools that allow the client’s customers to monitor and adjust their investments based on market trends and other relevant factors.
  • Automated investment ideas: The platform automatically generates investment ideas based on the latest market data and trends, helping customers to stay ahead of the curve.
  • Risk analysis: The platform includes advanced risk analysis tools that help customers to understand the risks associated with their investments and make informed decisions.
  • Performance tracking: The platform provides real-time performance tracking tools that allow customers to monitor the performance of their investments and make adjustments as needed.

The AI-powered stock portfolio management platform provides the following benefits:

  • AI-powered investment insights and recommendations
  • Enhanced predictive analytics and forecasting
  • More accurate and efficient data analysis
  • Advanced risk assessment and management using ML algorithms
  • Automated decision-making and optimization using AI
  • Real-time market monitoring and alerts powered by ML
  • Improved investment strategies using AI and ML
  • Personalized investment recommendations based on ML-powered insights
  • Smarter, data-driven investment decisions with AI and ML

By leveraging AI and machine learning algorithms to analyze financial data and generate customized investment recommendations, Sakha ensured that the client’s customers reap the benefits of improved performance and returns on investment. Improved investment decisions, higher returns on investment, reduced risk and volatility, more efficient portfolio management and increased customer satisfaction were the downstream benefits for the client,  while empowering it to have competitive advantage in the market, scalability and flexibility, cost savings and time efficiency, and better compliance with regulations.


A large, American Fortune 200 Fintech company operating an online payments system in the majority of countries that support online money transfers partnered with Sakha to tackle the challenges with their existing analytics platform that was based on Oracle.

The Challenge

The client was facing several challenges with their existing analytics platform that was based on Oracle. The main issues were:

  • Difficulty in processing large volumes of data in a timely manner due to Oracle’s limited scalability.
  • High costs of maintaining and licensing the Oracle database, which was proving to be a bottleneck for the company’s growth.
  • Lack of flexibility and agility in managing data and processing analytics due to the rigidity of the Oracle architecture.
  • The need to handle unstructured and semi-structured data, which was not well-supported by Oracle.

The company wanted to migrate to a Hadoop-based Big Data platform to address these issues and gain new capabilities for their analytics needs.


The company opted for a solution that involved migrating their analytics platform from Oracle to Hadoop using an automated data and metadata migration tool. The key features of the solution built by Sakha were:

  • Automatic discovery and migration of Oracle database schemas to Hadoop.
  • Large-scale data migration with parallel processing to minimize downtime and ensure minimal disruption to business operations.
  • Automatic conversion of SQL queries to Hive queries for seamless migration of existing analytics applications.
  • Built-in data quality checks to ensure data accuracy and completeness.
  • Automated metadata migration to ensure continuity of data lineage and governance.

The migration was done in a phased manner, with a series of tests to validate the data and ensure that the new Hadoop-based platform was able to meet the company’s analytics needs.

The migration to a Hadoop-based analytics platform had several benefits for the Fintech company, including:

  • Improved scalability and performance, enabling the company to handle larger volumes of data and process analytics in a timely manner.
  • Significant cost savings due to the open-source nature of the Hadoop platform and the ability to run on commodity hardware.
  • Greater agility and flexibility in managing data and processing analytics, with the ability to handle unstructured and semi-structured data.
  • Better support for data governance and lineage due to the automation of metadata migration.
  • The ability to leverage the Hadoop ecosystem and its rich set of tools and technologies for advanced analytics and data processing.

Overall, the migration to a Hadoop-based analytics platform proved to be a game-changer for the Fintech company, enabling them to take their analytics capabilities to the next level and stay competitive in their industry.


A large, American Fortune 200 multinational financial services corporation caters to customers across various industries. With millions of transactions processed daily, the company’s existing traditional data warehouse proved to be a challenge to keep up with the increasing demand for data processing, analytics, and reporting. To address this, the company decided to migrate its data warehouse to a modern Big Data architecture and partnered with Sakha for the same.

The Challenge

The existing data warehouse faced several problems, including:

  • Limited scalability: The traditional data warehouse was not designed to handle the massive amounts of data generated by the company’s modern operations. As a result, the system often experienced bottlenecks and slowdowns, which impacted the company’s ability to access and analyze data in a timely manner.
  • Lack of agility: The traditional data warehouse was inflexible and not easily adaptable to changing business needs. This made it difficult for the company to keep up with the pace of innovation and respond quickly to changing market conditions.
  • High costs: The existing data warehouse was expensive to maintain, both in terms of hardware and software. The company needed a more cost-effective solution that could deliver the same level of performance and functionality.


To address these challenges, the client decided to migrate to a modern Big Data architecture. The new architecture, designed by Sakha, included the following features:

  • Distributed storage: Sakha adopted a distributed storage system that allowed it to store and manage large volumes of data across multiple nodes. This provided the scalability and flexibility needed to handle the company’s growing data needs.
  • Data processing framework: Sakha implemented a data processing framework that could handle large-scale data processing tasks, such as batch processing and stream processing. This enabled the company to process data in real-time and generate insights faster than before.
  • Analytics tools: Sakha adopted advanced analytics tools that could handle complex data analysis tasks, such as machine learning and predictive analytics. This helped the company gain deeper insights into its data and make more informed business decisions.

The migration to a modern Big Data architecture provided several benefits to the company, including:

  • Improved scalability: The new architecture was able to handle the company’s growing data needs, without experiencing bottlenecks or slowdowns. This allowed the client to access and analyze data in real-time, improving its ability to respond to changing market conditions.
  • Increased agility: The new architecture was more flexible and adaptable than the traditional data warehouse, enabling the client to respond quickly to changing business needs and market conditions.
  • Reduced costs: The new architecture was more cost-effective than the traditional data warehouse, both in terms of hardware and software. This allowed the client to allocate its resources more efficiently and focus on delivering value to its customers.

The migration from a traditional data warehouse to a modern Big Data architecture enabled Sakha’s client to improve its data processing, analytics, and reporting capabilities. The company was able to handle its growing data needs, respond quickly to changing business needs, and reduce costs. The new architecture provided the foundation for continued innovation and growth, enabling the client to stay competitive in a rapidly-changing market.


The largest pharmacy chain in the United States by number of locations and part of an American Fortune 10 group partnered with Sakha in streamlining claims processing. The chain was struggling with the increasing amount of data generated by its claims processing system. With a large volume of claims coming in every day, the pharmacy chain’s existing data pipeline was struggling to keep up with the demand. The system required manual intervention, and it was not scalable to handle the growing volume of data.

The Challenge

The pharmacy chain was facing the following challenges with its existing claims processing data pipeline:

  • The existing data pipeline was slow and unable to handle the growing volume of claims data.
  • The process required manual intervention, which was time-consuming and error-prone.
  • The pharmacy chain lacked the ability to perform advanced analytics on the claims data due to the limitations of the existing data pipeline.


The pharmacy chain partnered with Sakha’s team of experts in ML and Big Data to design and implement a claims processing data pipeline orchestration system. The new system automated the entire claims processing workflow, from data ingestion to data cleaning, transformation, and loading. The system also included an AI-powered claims validation module, which automatically detected errors and anomalies in the claims data, reducing the need for manual intervention.

The key features of the new system include:

  • Automated claims processing workflow.
  • ML-powered claims validation module.
  • Scalable and flexible data pipeline architecture.

The new claims processing data pipeline orchestration system provided the following benefits to the pharmacy chain:

  • Improved efficiency and accuracy of claims processing, resulting in faster turnaround times for customers.
  • Increased scalability and flexibility of the data pipeline, enabling the pharmacy chain to handle larger volumes of claims data.
  • Reduced manual intervention in the claims processing workflow, resulting in fewer errors and reduced labour costs.
  • Enhanced analytics capabilities, enabling the pharmacy chain to gain insights from the claims data and improve its business operations.

Developed by Sakha, the new claims processing data pipeline orchestration system helped the pharmacy chain to streamline its claims processing workflow, reduce errors, and gain valuable insights from its data.


Data engineering is a critical component of any data-driven business. In the retail industry, where Sakha’s client is a Fortune 150 American chain of high-end department stores founded in 1858, customer loyalty data is particularly important as it can be used to inform marketing and customer retention strategies. However, the client used to rely on hand-coded data engineering framework, which was time-consuming and difficult to manage.

The Challenge

Hand-coded customer loyalty data engineering frameworks are often inflexible, difficult to maintain, and can be prone to errors. This can result in delays in data processing, incomplete or inaccurate data, and a lack of visibility into the data pipeline. This can impact business operations, customer loyalty, and overall revenue.

The client partnered with Sakha to develop an end-to-end, manageable data pipeline model powered by machine learning (ML).


An end-to-end, manageable data pipeline model powered by machine learning (ML) helped address the client’s problems by automating the data engineering process and improving the quality and accuracy of customer loyalty data.

Sakha’s solution included features such as:

  • Automated data extraction and ingestion: The solution was designed to automatically extract data from various sources, including transactional data, customer demographic data, and loyalty program data. This eliminates the need for manual data entry and ensures that all relevant data is captured.
  • Machine learning-powered data cleaning and normalization: The solution used ML algorithms to automatically clean and normalize the data, ensuring that it is accurate and consistent across all sources. This reduces the risk of errors and ensures that the data is usable for analysis.
  • Flexible data modeling: The solution was designed to accommodate different data models, allowing for the analysis of various customer loyalty metrics such as purchase frequency, purchase value, and customer lifetime value. This provides a more complete picture of customer behaviour and helps inform marketing and customer retention strategies.
  • Customizable data pipelines: The solution was designed to allow for customization of the data pipeline, enabling business users to modify the data flows and analysis as needed. This provides greater flexibility and agility in responding to changing business needs.
  • Automated reporting and visualization: The solution was designed to automatically generate reports and visualizations, allowing business users to quickly and easily understand and interpret the data. This helps inform business decisions and identify areas for improvement.

Some of the benefits of automating the client’s existing hand-coded customer loyalty data engineering framework to an end-to-end, manageable data pipeline model include:

  • Improved data quality and accuracy: The solution helps eliminate errors and ensures that customer loyalty data is accurate and consistent, improving the quality of analysis and insights.
  • Greater efficiency and productivity: It automates the data engineering process, freeing up valuable time for data analysts and other business users to focus on analysis and interpretation of the data.
  • Greater visibility into the data pipeline: The solution provides greater visibility into the data pipeline, enabling business users to identify bottlenecks and areas for improvement.
  • Improved customer loyalty and revenue: The solution helps inform marketing and customer retention strategies, resulting in increased customer loyalty and revenue over time.

Automating the hand-coded customer loyalty data engineering framework to an end-to-end, manageable data pipeline model powered by ML helps the client to improve the quality and accuracy of customer loyalty data, improve efficiency and productivity, and drive increased customer loyalty and revenue over time.


The client is a Plano, Texas-based startup, which was setup when the Founder & CEO recognized that many Point-of-sale (POS) systems lack the agility and functionality to allow servers to deliver the best dining experience to their guests. In creating a prototype of handheld tablets that interface with the merchant’s existing tech stack, the startup hit upon a solution that optimizes labour, creates a contactless and efficient ordering and payment process for both servers and guests, and establishes PCI and EMV compliance, immediately eliminating 100% of fraudulent chargebacks.

The client wanted to scale this prototype to a full-fledged market offering and partnered with Sakha for the development of the solution.

The Challenge

POS systems are a critical component of restaurant operations, allowing for efficient order taking, payment processing, and inventory management. However, traditional POS systems have some limitations that can impact the customer experience and business operations.

  • Many traditional POS systems used in restaurants are outdated and do not provide customization options for menu items based on customer preferences, allergies, or dietary restrictions. This can result in customers receiving inaccurate orders, leading to dissatisfaction and potential loss of business.
  • Moreover, traditional POS systems can be time-consuming and difficult to navigate, which can lead to longer wait times for customers and slower service. This can negatively impact the customer experience and result in lost business for the restaurant.


A machine learning (ML)-driven, tablet and wearables-based system can help address these problems by offering a more customized and streamlined ordering experience.

Sakha designed & developed an app to offer personalized menu recommendations to customers based on their preferences, allergies, and dietary restrictions. This improves order accuracy and customer satisfaction, leading to increased customer loyalty and repeat business.

The system is also designed to offer seamless communication between the front-of-house and back-of-house staff, ensuring that orders are processed quickly and accurately. This reduces wait times for customers and improves the overall efficiency of restaurant operations.

In addition, the app is designed to integrate with loyalty programs and offer targeted promotions and discounts to customers based on their past orders and preferences. This encourages repeat business and increase customer engagement.

Some of the benefits of the solution developed by Sakha include:

  • Improved order accuracy: The system offers customized menu options based on customer preferences, allergies, and dietary restrictions, leading to improved order accuracy and customer satisfaction.
  • Increased efficiency: The system improves the efficiency of restaurant operations by reducing wait times for customers and streamlining communication between front-of-house and back-of-house staff.
  • Improved customer loyalty: The system offers targeted promotions and discounts to customers based on their past orders and preferences, encouraging repeat business and increasing customer engagement.
  • Increased revenue: By offering a more personalized and streamlined ordering experience, restaurants can attract new customers and retain existing ones, leading to increased revenue over time.

Overall, the ML-driven, tablet and wearables-based POS system is helping restaurants provide a more customized and efficient ordering experience for customers, resulting in improved customer satisfaction, loyalty, and revenue.

Today, the solution is revolutionizing the restaurant technology space by introducing the most comprehensive, cost-effective, and contactless solution for the hospitality industry to date. It supercharges existing POS systems to immediately enable contactless ordering and payment, optimize labour, eliminate fraudulent chargebacks, and create a triple-win for servers, managers, and guests alike.

Sakha has worked with the client to expand the product offering to incorporate additional contactless payment technologies, mobile menu browsing, and curbside order and payment options that help restaurants generate additional off-premise revenue. AI surveys, guest preference tracking, and offer management have all made their way into the 360-degree solution to make the product the single preferred technology provider for the restaurant and hospitality industries.

The initial platform has now been expanded to accommodate multi-merchant venues (such as malls and entertainment districts), hotels, airports, retail establishments, and event venues, and can quickly adapt to the changing requirements that brands are seeking in today’s world.

Through the Sakha-developed solutions, the client has raised a total of $12M in funding over 2 rounds. The client was also #3 on Fast Company’s prestigious annual list of the World’s Most Innovative Companies for 2021.


The second largest chain of supermarkets in the United Kingdom partnered with Sakha for a segmentation and targeting model for one its branches on a pilot basis to better understand its customer base and create more effective marketing campaigns.

The Challenge

A large supermarket has a wide range of products and services that appeal to different customer segments. However, the supermarket’s marketing campaigns are often generic and not targeted, leading to low response rates and wasted resources. The client needed a segmentation and targeting model to better understand its customer base and create more effective marketing campaigns.

The segmentation and targeting model aimed to solve several problems that the supermarket faced, including:

  • Lack of personalization: Marketing campaigns are often not personalized, leading to low engagement and response rates.
  • Wasted resources: Generic marketing campaigns waste resources by targeting customers who are not interested in the products or services being promoted.
  • Inefficient marketing: Without a segmentation and targeting model, the supermarket cannot efficiently identify and target specific customer segments with relevant promotions.


Sakha designed & developed the segmentation and targeting model to help the supermarket create more personalized and effective marketing campaigns, through:

  • Data collection: The model collects data from multiple sources, including customer transactions, demographics, and behaviour, to build a comprehensive view of the customer base.
  • Customer segmentation: The model segments customers based on common characteristics such as demographics, purchase history, and product preferences.
  • Predictive modeling: The model uses predictive modeling techniques to identify which customers are most likely to respond to specific promotions.
  • Targeted marketing campaigns: The model enables the supermarket to create targeted marketing campaigns for specific customer segments, based on their characteristics and predicted response rates.
  • Performance tracking: The model tracks the performance of marketing campaigns, enabling the supermarket to evaluate the effectiveness of the campaigns and make data-driven decisions.

The segmentation and targeting model offered several benefits to the supermarket, including:

  • Increased engagement: By creating more personalized and relevant marketing campaigns, the supermarket could increase customer engagement and response rates. After implementing the segmentation and targeting model, the supermarket saw a 25% increase in customer engagement, as measured by click-through rates and conversions.
  • Reduced waste: Targeted marketing campaigns reduced wasted resources by focusing on customers who are most likely to respond to the promotions. By creating targeted marketing campaigns, the supermarket was able to reduce its marketing spend by 30%, while maintaining the same level of customer response.
  • Efficient marketing: By identifying specific customer segments, the model enabled the supermarket to efficiently create and execute marketing campaigns. It effectively reduced the time required for campaign planning and execution by 40%.
  • Data-driven decisions: The model provided data-driven insights into customer behaviour and preferences, enabling the supermarket to make more informed marketing decisions. The supermarket was able to realize a 20% increase in sales revenue.

The segmentation and targeting model provided a valuable solution to the challenges that the supermarket faced in creating effective marketing campaigns. By collecting customer data, segmenting customers, and using predictive modeling to create targeted campaigns, the model enabled the supermarket to engage customers more effectively, reduce waste, and make data-driven decisions.


A Fortune-50 American multinational food, snack, and beverage corporation, with businesses encompassing all aspects of the food and beverage market, was looking for a niche tech partner for an end-to-end solution that automates Big Data workflows for sales business intelligence and analytics.

The Challenge

Sales business intelligence and analytics are critical for the client to make informed decisions about their sales strategies, customer behaviour, and market trends. However, the sheer volume and complexity of sales data made it difficult to extract valuable insights and trends, leading to missed opportunities and lost revenue. Sakha proposed an end-to-end solution to address the client’s needs.

The solution aimed to solve several problems that sales teams face, including:

  • Time-consuming data preparation: Preparing sales data for analysis is a time-consuming process, requiring manual data cleaning, transformation, and integration.
  • Inefficient data processing: Analyzing large volumes of sales data is a slow and resource-intensive, making it difficult to keep up with changing market conditions.
  • Lack of actionable insights: Even with the right data, sales teams often struggle to extract meaningful insights and trends, leading to missed opportunities and lost revenue.


The end-to-end solution automates Big Data workflows for sales business intelligence and analytics, making it easier and faster for sales teams to analyze their data and extract insights. Key features include:

  • Data integration: The solution integrates data from multiple sources, including CRM systems and third-party data sources, making it easier to get a comprehensive view of sales data.
  • Automated data preparation: The solution automates data cleaning, transformation, and integration, reducing the time and effort required for data preparation.
  • High-performance computing: The solution uses high-performance computing and parallel processing to analyze large volumes of sales data quickly and efficiently.
  • Advanced analytics: The solution uses advanced analytics, including machine learning algorithms, to extract insights and trends from sales data.
  • Customizable dashboards: The solution provides customizable dashboards that enable sales teams to visualize and analyze their data in real-time, making it easier to identify opportunities and trends.

The end-to-end solution offers several benefits to sales teams, including:

  • Time and cost savings: Automating Big Data workflows reduces the time and effort required for data preparation and analysis, saving time and money.
  • Improved accuracy: Automated data preparation and advanced analytics improve the accuracy and quality of insights and trends, reducing the risk of missed opportunities and lost revenue.
  • Real-time insights: Customizable dashboards provide real-time insights into sales performance, enabling sales teams to respond quickly to changing market conditions.
  • Scalability: The solution is scalable, making it easy to accommodate growing sales data volumes and expanding sales teams.

Sakha’s solution addressed the challenges that the client’s sales teams faced when analyzing their data. By automating Big Data workflows, providing advanced analytics, and offering customizable dashboards, the solution enabled the client’s sales teams to extract valuable insights and trends, improving their performance and revenue potential.


Sakha’s client was a large conglomerate which includes Evoma (a leading business centre which offers furnished office spaces and conference rooms in a 4-star Bangalore location) and Lucep (a mobile lead distribution application provider).

The Challenge

The conglomerate wanted to offer a tech-driven solution for Small and Medium Enterprises (SMEs) which was the core customer segment for their businesses.

SMEs play a critical role in the global economy, but they often struggle to access the resources they need to grow and compete with larger businesses. This includes finding buyers and sellers for their products or services, accessing talent, and securing funding. This is where the online platform, developed by Sakha for the client, comes in.

The online platform aims to solve several problems that SMEs face, including:

  • Difficulty in finding buyers and sellers: SMEs often struggle to find buyers and sellers for their products or services, limiting their growth and revenue potential.
  • Lack of access to talent: SMEs may not have the resources to attract and retain top talent, making it difficult to compete with larger businesses.
  • Limited access to funding: SMEs often struggle to secure funding for their operations and growth, making it difficult to invest in new initiatives or expand their businesses.


The online platform connects SMEs with buyers, sellers, talent, and funding, using machine learning algorithms to match them with the most relevant opportunities. Here are some key features:

  • Customizable profiles: SMEs can create profiles that showcase their products or services, skills, and the type of support they are looking for. This allows the platform to match them with other SMEs or opportunities that are a good fit.
  • Personalized matching: The platform uses machine learning algorithms to match SMEs with buyers, sellers, talent, and funding opportunities that are tailored to their needs and interests.
  • Discussion forums: The platform includes discussion forums where SMEs can ask questions, share advice, and connect with other members of the community.
  • Resource library: The platform also includes a resource library with articles, videos, and other content that can help SMEs learn new skills and stay up-to-date on industry trends.
  • Advanced analytics: The platform uses advanced analytics to provide SMEs with insights and recommendations to help them make better business decisions.

The online platform offers several benefits to SMEs, including:

  • Access to a global network: SMEs can connect with buyers, sellers, talent, and funding opportunities from around the world, expanding their reach and revenue potential.
  • Efficient resource allocation: SMEs can easily find the resources they need to grow and compete, saving time and effort.
  • Improved chances of success: By connecting SMEs with relevant opportunities and providing valuable insights and recommendations, the platform can help SMEs achieve their goals and compete with larger businesses.

The ML-driven online platform offers a valuable solution to the challenges that SMEs face. By connecting them with relevant opportunities, providing access to resources and support, and using advanced analytics to provide insights and recommendations, the platform can help SMEs grow and compete in the global marketplace.


The City of Singapore faced numerous challenges with its traditional parking system, such as traffic congestion, wastage of time, and increased air pollution. Moreover, finding a parking space in Singapore’s central areas is a significant challenge, and drivers frequently need to circle the block multiple times, wasting valuable time and fuel. These issues resulted in the need for a smart parking solution that could help drivers find a vacant parking spot quickly and efficiently. Sakha developed Android & iOS mobile apps as well as a Facebook Messenger Bot to address the challenges for the City of Singapore, leveraging data science.

The Challenge

Several factors warrant the need for a smart parking solution for the City of Singapore:

  • Increased population and vehicle ownership have led to a rise in the number of vehicles on the roads, leading to congestion and traffic jams.
  • Finding a parking spot in Singapore can be time-consuming and frustrating, leading to increased traffic on the roads as drivers search for a spot.
  • Lack of information on parking availability leads to uncertainty and frustration for drivers who may not know where to find a spot.
  • Traditional parking systems rely on manual data collection, leading to inaccuracies and inefficiencies in parking management.
  • There is a need to reduce the amount of time and fuel wasted by drivers looking for parking spots, which can have a negative impact on the environment.

These problems highlight the need for a smart parking solution that can provide real-time information on parking availability, reduce search time for drivers, and increase the efficiency of parking management.


To address the problems, Sakha developed a smart parking solution that leverages Open Data. The solution involved building a mobile application and Facebook Messenger Bot, which uses Machine Learning algorithms to predict the availability of parking slots at the current time and possible future demand through analysis of historical & actual parking patterns.

Some of the features of the smart parking solution app and messenger bot included:

  • Real-time availability of parking slots in the city
  • Parking slot reservation through the app and messenger bot
  • Integration with government open data to display availability of parking slots
  • Machine Learning algorithms to predict parking slot availability and future demand
  • Navigation assistance to the nearest available parking slot
  • Coupon parking discounts for registered users
  • Integrated payment gateway for cashless payment of parking fees
  • Ability to view and manage parking history and payment details
  • Personalized notifications on parking availability and coupon discounts
  • Multi-language support for tourists and non-English speaking residents

To predict parking slot availability and future demand, Linear Regression technique was used. Linear regression is a type of statistical modeling technique used to analyze the relationship between a dependent variable (in this case, the availability of parking slots) and one or more independent variables (such as time of day, day of the week, and weather conditions).

Historical data on parking slot availability and relevant independent variables were collected and used to train the model. This involved selecting and preprocessing the data, as well as selecting appropriate features and labels for the training data.

Once the model was trained, it was used to make predictions about the availability of parking slots at a particular time in the future based on the input of independent variables such as time of day, day of the week, and weather conditions. These predictions can then be displayed in the app and messenger bot in real-time, allowing users to plan their parking accordingly.

By using machine learning algorithms, the smart parking solution can provide real-time updates on parking slot availability and predict future demand, helping drivers save time and reduce frustration.

Benefits of Sakha’s product include:

  • Saves time: The app and messenger bot help drivers quickly locate available parking spots, saving them time and reducing the time spent searching for a spot.
  • Reduces traffic congestion: By helping drivers find available parking spots more efficiently, the smart parking solution can help reduce traffic congestion on city streets.
  • Improves air quality: With less time spent driving around in search of parking, the smart parking solution can help reduce vehicle emissions, improving air quality in the city.
  • Increases revenue: By providing a better parking experience, the smart parking solution can encourage more people to use paid parking facilities, increasing revenue for the city.
  • Provides convenience: The app and messenger bot provide a convenient way for drivers to find parking spots and make payments, eliminating the need to carry cash or deal with parking meters.
  • Improves safety: By reducing the time spent driving around in search of parking, the smart parking solution can help reduce the risk of accidents caused by distracted driving or aggressive behavior.
  • Enhances user experience: With features such as navigation assistance and coupon parking discounts, the smart parking solution provides a more personalized and enjoyable parking experience for users.
  • Data insights: By using machine learning algorithms to analyze parking patterns, the smart parking solution can provide valuable insights to city officials on how to better manage parking infrastructure and resources.


Sakha’s client was from the investment industry, which is highly competitive and customer-centric. Investment firms are always looking for ways to improve customer experience and satisfaction while reducing costs. In such a scenario, the need for a chatbot solution arises. Sakha helped the client streamline customer service with a deep learning-powered chatbot.

The Challenge

The client faced several challenges, which warranted the need for a chatbot solution such as:

  • High volume of customer queries: Investment firms receive a high volume of customer queries related to onboarding, cash flow, stock portfolio, etc. This can be overwhelming for customer service agents to handle manually.
  • 24/7 availability: Customers may have queries outside of regular business hours, and it is not feasible to have customer service agents available at all times.
  • Cost-effective solution: Hiring and training more customer service agents to handle the high volume of queries can be expensive. Therefore, the client needed an efficient and cost-effective solution.


Sakha used a deep learning model to develop & deploy a chatbot-based solution to understand and automatically reply to customer queries. The model was trained on a large dataset of customer queries and responses to understand the context of the queries and provide accurate responses. As the chatbot interacts with more customers, the model learns and improves its responses over time. The chatbot can also handle complex queries by understanding the intent behind them and providing relevant responses.

Some of the functions which the chatbot was programmed to handle included:

  • Onboarding: The chatbot can guide new customers through the onboarding process by asking questions, providing necessary information and collecting the responses.
  • Cash Flow Queries: Customers can ask queries related to their cash flow, such as account balance, transactions, and payments.
  • Stock Portfolio Queries: Customers can ask queries related to their stock portfolio, such as stock prices, performance, and investments.
  • Notifications: The chatbot can send notifications to customers regarding account updates, market trends, and investment opportunities.
  • News Feeds: The chatbot can provide relevant news and information related to the investment industry and the customer’s stock portfolio.
  • Reporting: The chatbot can generate reports related to the customer’s account and portfolio.

For developing the chatbot, Sakha leveraged on Rasa for the Chatbot Development Framework, Python, PyTorch for the Deep Learning Framework, NLTK for the Natural Language Processing Library, Django for the Backend Framework, PostgreSQL and AWS for the Cloud Platform.

Benefits of the chatbot solution include:

  • 24/7 availability: The chatbot is available to customers 24/7, providing immediate responses to their queries.
  • Cost-effective solution: The chatbot can handle a high volume of queries at a low cost compared to hiring more customer service agents.
  • Improved customer experience: The chatbot can provide quick and accurate responses to customer queries, improving customer satisfaction and experience.
  • Integration: The chatbot was integrated with the investment firm’s app, customer software, Facebook page, and website, providing a seamless customer experience.


A scientific and industrial research organisation was facing challenges in accurately predicting the yield potential of wheat at a paddock level due to variations in soil nutrient levels, temperature changes, and weather patterns throughout the season. The organization needed a solution that could provide accurate predictions of wheat yield potential based on real-time data collection and analysis from IoT devices, while also incorporating historical data on soil nutrient levels and other parameters. The client partnered with Sakha to develop a prototype that leveraged data collection and analysis from IoT devices to gain insights.

The Challenge

The client faced several problems that led to the need for a prototype that leveraged modern technology for predicting wheat yield:

  • Inaccurate Yield Prediction: The organization struggled with accurately predicting the yield potential of wheat at the paddock level due to the lack of insights which cohesively brings together disperate data related to soil nutrient levels, temperature changes, and seasonal factors such as rainfall rates.
  • Manual Data Collection: The organization relied heavily on manual data collection methods, which were time-consuming, labor-intensive, and prone to errors, leading to inaccurate yield predictions.
  • Limited Insight: The organization lacked real-time insights into the changing environmental conditions and soil nutrient levels that impact wheat yield potential, making it difficult to optimize crop management practices.
  • Inefficient Resource Utilization: Due to the lack of real-time data insights, the organization could not optimize resource utilization and make informed decisions about resource allocation, leading to inefficient resource utilization and lower yields.

Overall, the existing processes hindered the client’s ability to accurately predict wheat yield potential and optimize crop management practices.


To address the challenges faced by the client, Sakha developed a prototype that leveraged data collection and analysis from IoT devices to gain insight into soil nutrient levels, temperature changes, and other environmental factors that impact wheat yield potential. The prototype used Machine Learning algorithms such as Linear Regression and Decision Trees to forecast yield potential based on historical data about soil nutrient levels and other parameters.

The features of the prototype included:

  • Real-time data collection from IoT devices for soil nutrient levels, temperature, rainfall rates, and other environmental factors
  • Historical data analysis to identify patterns and trends in soil nutrient levels and other parameters that impact yield potential
  • Machine Learning algorithms such as Linear Regression and Decision Trees for forecasting yield potential based on past data
  • User-friendly interface for viewing yield potential predictions and making informed decisions about crop management strategies

The IoT devices used in the prototype for paddock-level wheat yield potential had the capability to gather and transmit real-time data on various environmental factors that affect the growth and yield of wheat crops. These devices were placed in strategic locations in the field to capture data on soil moisture, temperature, humidity, light intensity, and other parameters. Some of the key features of these devices included:

  • Wireless Connectivity: The IoT devices were designed to communicate wirelessly with a central data hub using low-power, long-range wireless technology – LoRaWAN. This allowed for easy deployment of devices across large areas without the need for complex wiring or infrastructure.
  • Battery-Powered: The devices were powered by long-lasting, low-power batteries that could last for several months to years depending on the usage and configuration. This eliminated the need for frequent battery replacement or maintenance.
  • Robust Design: The IoT devices were built to withstand harsh environmental conditions such as rain, dust, and extreme temperatures. They were also designed to be tamper-proof and resistant to vandalism or theft.
  • Data Encryption: The IoT devices used advanced encryption techniques to secure the data transmitted over the wireless network. This ensured that the data was protected from unauthorized access or interception.
  • Scalable: The IoT devices could be easily scaled up or down depending on the size of the field or the number of crops being monitored. This made it easy to expand or modify the system as per the needs of the client.

Overall, the IoT devices played a critical role in the prototype by providing real-time data on various environmental factors that affect the growth and yield of wheat crops. This data was then used in conjunction with Machine Learning algorithms to generate accurate forecasts and recommendations for farmers.

The client reaped multi-fold benefits from the prototype, including:

  • Accurate and timely predictions of wheat yield potential at a paddock level, enabling farmers to make informed decisions about crop management strategies and improve overall productivity
  • Improved efficiency and reduced costs through targeted use of fertilizers and other inputs based on soil nutrient levels and other environmental factors
  • Enhanced sustainability through better management of resources and reduced environmental impact
  • Improved collaboration and knowledge sharing among researchers and farmers in the industry through the use of a common platform for data analysis and decision-making


Sakha was commissioned for the implementation of a Smart Forest project, covering 50 sq. kms of a designated forest area in an Indian State. The project aimed to prevent the trafficking of trees by using IoT devices to monitor sound waves. The system sends notifications to the authorities for prompt intervention in case of any suspicious activities or noises. The solution was based on Raspberry Pi technology, and it provided real-time monitoring of the forest area to ensure the protection of trees and wildlife.

The Challenge

The Forest Department of the Indian State is responsible for the conservation and management of forest resources in the region. However, illegal logging and trafficking of trees have been a significant challenge faced by the department. These activities not only cause a significant loss of revenue for the state but also lead to severe ecological and environmental consequences.

The forest department has been struggling to combat these illegal activities, as they are usually carried out in remote and inaccessible areas, making it difficult to monitor and detect them in time. The lack of real-time information on forest activities and the inability to respond to incidents promptly have only aggravated the situation.

Furthermore, traditional methods of monitoring, such as patrolling by forest guards, have proved to be ineffective in tackling the problem, given the vast expanse of forest areas that need to be covered. In this scenario, a technology-driven solution was necessary to enhance the efficiency and effectiveness of forest management and conservation.


The pilot Smart Forest project implemented by Sakha addressed the problem of illegal trafficking of trees in a designated forest area in an Indian State. The solution used IoT devices to monitor sound waves and identify any suspicious activities or noises that might indicate the presence of traffickers. The following are the features of the solution:

  • Implementation of IoT devices in the forest area to monitor sound waves and identify any suspicious activities or noises
  • Use of Rasberry Pi-based solution for real-time monitoring of the forest area
  • Automated notifications to authorities when suspicious activities are detected
  • Ability to track the movement of illegal loggers in the forest area
  • Remote access to the data collected by the IoT devices through a web-based dashboard

The Rasberry Pi-based solution used in the project was a small computer that acted as the central hub for collecting and processing data from the IoT devices (acoustic sensors). It was programmed to process the data in real-time and send notifications to the authorities through a wireless connection when suspicious activities were detected. The solution was cost-effective and easy to deploy, making it an ideal choice for monitoring large forest areas.

The Raspberry Pi continuously collected the data from the IoT devices and stored it locally in its SD card. It also sent the data to the cloud servers using secure protocols and data encryption techniques. In the cloud servers, the data was analyzed using AI algorithms, machine learning, and data analytics tools to identify any suspicious activities or patterns. If any such activities were detected, the system immediately sent notifications to the forest department authorities, who could take prompt actions to prevent illegal logging activities in the forest.

Benefits of the solution include:

  • Prevention of illegal trafficking of trees in the designated forest area
  • Prompt intervention by authorities to prevent further illegal activities
  • Reduction in deforestation and protection of the forest ecosystem
  • Cost-effective and efficient monitoring of the forest area
  • Use of advanced technology to address environmental challenges

The Smart Forest project was a successful implementation of technology to address environmental challenges and protect the forest ecosystem from illegal activities such as tree trafficking. The use of IoT devices and Rasberry Pi-based solution enabled real-time monitoring and prompt intervention, ensuring the safety and preservation of the forest area.


The Housing and Urban Development Corporation of a country faces a major challenge of increasing energy consumption and related costs. As a result, they sought to implement a solution that would incentivize energy savings and promote sustainable practices in homes and buildings. The corporation engaged Sakha to develop a platform that would reward energy savings with AI, IoT, and Blockchain Technology.

The Challenge

The client faced several problems related to energy consumption and conservation. Some of the issues are:

  • High energy consumption: The corporation noticed that energy consumption levels in residential and commercial buildings were much higher than required, leading to higher utility bills and carbon emissions.
  • Lack of incentives for energy conservation: There was no incentive for households and buildings to conserve energy and reduce consumption, leading to a lack of motivation for people to adopt energy-efficient practices.
  • Difficulty in tracking energy consumption: The corporation faced challenges in tracking the energy consumption of individual households and buildings, which made it difficult to identify areas where energy conservation measures could be implemented.
  • Inefficient energy infrastructure: The corporation faced issues with the existing energy infrastructure, which was outdated and inefficient, leading to energy wastage and higher costs.

To address these problems, the corporation sought the help of Sakha to develop a solution that leveraged AI, IoT, and Blockchain technology.


Sakha developed a platform that utilizes AI, IoT, and blockchain technology to monitor and incentivize energy savings.


  • AI-based energy monitoring: The platform uses machine learning algorithms to analyze energy consumption patterns and identify areas where energy savings can be made.
  • IoT devices: Sensors and smart meters are installed in homes and buildings to collect real-time data on energy usage.
  • Energy credits: The platform offers energy credits to homeowners and building managers who reduce their energy consumption, which can be traded on a blockchain exchange.
  • Blockchain framework: A blockchain-based system is used to securely store and track energy savings and credits, as well as monitor and verify the performance of appliances and other energy-efficient devices.
  • Off-peak electricity optimization: The platform suggests and encourages usage of off-peak electricity times to reduce energy consumption and earn additional credits.

AI-based algorithms were used to analyze energy consumption patterns and identify areas where energy savings could be achieved. For example, the system could learn when certain appliances are used most frequently and suggest ways to reduce energy consumption during those times.

IoT devices were installed in homes and buildings to collect real-time data on energy usage, temperature, and other environmental factors. This data was used to optimize energy consumption and identify areas where improvements could be made.

Blockchain technology was used to facilitate the exchange of energy credits between consumers and producers. Consumers who generate excess energy through solar panels or other renewable sources could earn energy credits that could be used to offset their energy bills or sold to other consumers through the blockchain exchange.

Smart contracts were used to automatically execute transactions between consumers and producers based on pre-defined rules and conditions. For example, a smart contract was programmed to automatically purchase energy credits from a consumer’s blockchain account when their energy bill exceeds a certain threshold.

Overall, the combination of AI, IoT, and blockchain technology created a powerful platform for incentivizing energy savings and promoting sustainability.

Benefits of the platform for the corporation included:

  • Incentivizes energy savings: The platform provides tangible rewards for homeowners and building managers who take steps to reduce their energy consumption, which can lead to significant cost savings over time.
  • Encourages adoption of energy-efficient technology: By offering credits for the use of energy-efficient appliances and devices, the platform incentivizes the adoption of sustainable technology.
  • Real-time energy monitoring: The platform provides real-time data on energy consumption and usage patterns, allowing homeowners and building managers to identify areas where energy savings can be made.
  • Secure and transparent: The use of blockchain technology ensures that all energy savings and credit transactions are secure, transparent, and tamper-proof.

Deal contracts digitization for the world’s leading independent financial advisory and asset management firm.

About the Client

The client is the world’s largest independent investment bank, with principal executive offices in New York City, Paris and London. It is a financial advisory and asset management firm that engages in investment banking, asset management, and other financial services primarily with institutional clients.

Business Need

Our client implemented a deals contract management platform and needed to populate the platform with information and metadata from its existing contracts. Doing this manually would require its legal team to review each contract, identify the relevant information and metadata, and enter that data into the platform. There had to be an easier – and cheaper – way.

Challenges and Requirements

  • Enable forensic accounting of deal contracts.
  • Eliminate human error & misses causing millions of dollar penalties to the firm.
  • Eliminate manual effort to rectify the errors.
  • Automate the manual audit process which needed several hours of lawyers & legal advisers.
  • Automate the manual effort to analyze the contract and reduce delay in response to regulatory requirements.
  • Unavailability of the information due to silo-ed approach.
  • No visibility to the contract terms, legal clauses, values, commitments.
  • Hard to understand the legal terms hence no visibility to impact in case of non-compliance.

Our Role

Using a combination of Artificial Intelligence (AI) and machine learning techniques, we designed and automated the contract digitization process, including the extraction of key terms and clauses from the client’s deal contracts.

The solution was as individual as the client – we trained the machine to understand the client’s terms and provisions, and created a custom review workflow specifically designed for its deal contracts. We monitored the machine learning carefully to make sure that the outputs were of high quality and would deliver valuable insight from deal contract analysis in the future, including forensic accounting. The use of Robotic Process Automation (RPA) to import the extracted deal contract information from our AI solution to the contracts management platform meant that the legal team were freed up to focus on value-adding activities.


Java, Spring Framework, Hibernate ORM, MySQL, Hadoop, Hadoop HDFS, Apache Hive, Apache ZooKeeper, Apache Spark, and Stanford NLP.


Benefits to the client includes:

  • The client’s deal contract review speed increased 30-fold, at an eighth of its previous costs.
  • As well as these immediate efficiencies, the client’s contract performance analytics improved across all territories; the fully searchable database of terms bringing the opportunity for far greater insights and improved decision-making.
  • Forensic accounting of deal contracts was made possible, with streamlined and automated workflow.

Read more about our financial analytics solutions.

Empowering one of the largest providers of specialty insurance.

About the Client

Founded in 1952, the client is represented by a network of more than 38,000 independent agencies across USA. It is one of the largest offerer of insurance products for manufactured homes, landlord properties, vacant properties, seasonal properties, RVs, motorcycles, off-road vehicles, snowmobiles, boats, personal watercraft, and collectible autos as well as programs for non-standard and mono line auto.

Business Need

Rapid business growth, coupled with an upscaling agency network, meant that the client needed a Policy Management System that could handle all its business needs spread across several locations in the US.

Challenges and Requirements

  • Existing COBOL-based system was inefficient and had to be quickly scaled to modern web-based technologies.
  • The solution had to support thousands of users across all states of USA.
  • The client has nearly two dozen business lines of insurance products.
  • Millions of transactions are processed every year.
  • Missed deadlines have severe financial ramifications for the client.
  • The user scalability requirements range from 50 to 1000+ person offices.

Our Role

We were selected to be accountable for end-to-end delivery of the application. Utilizing our Global Delivery Model, we delivered all the requirements of this complex multi-user, multi-location, highly scalable system over a multi-year timeframe. It involved redesigning the existing real time policy administration screens and business logic from a series of COBOL modules to J2EE middle tier and eliminating Cogen mainframe Customer Information Control System (CICS) from production and test region for Casualty products.

The key activities managed that directly led to the success of the engagement included:

  • Defining the process for and gathering the business requirements from a diverse population
  • Designing and developing the technical architecture frame
  • Designing and developing the application
  • Developing and managing a complex plan which included – Proof of concept development; Rapid screen prototyping to support business requirements gathering and clarity; Phased functionality delivery plan; Interim application deliverables for high-need areas; and Utilization of over 50 (100 peak) highly skilled systems developers

The resulting system is a user friendly, automated and highly interactive web interface for creating Specialty Casualty product policies. It is designed for independent agents, exclusive agents and employees. It is used to collect the required business data for creating a policy, to generate a premium and for selling policy to a customer. It also supports process of handling all sub transactions like Endorsement, Renewal, Rewrite, Cancellation, and Re Initiate on a policy.


J2EE and Web 2.0.


Benefits to the client includes:

  • The robust architecture helped the client enhance productivity and cut time & costs.
  • Manageability of the system was improved because of consolidation and re-engineering of various legacy systems into a single homogenous, Web 2.0 entity. This lowered maintenance costs and ensured lesser downtime.
  • All the data residing on disparate systems were combined and put into a single database. Access was also enhanced and all users could get complete access to any data required, any time. Consolidation of data also led to higher customer service levels and reduction in customer query response times.
  • The application catered to the needs of the users all over the US and this standardized the administration process.
  • Many of the tasks that were earlier being handled manually were automated, presenting significant efficiency benefits to the client’s operations.

Enabling over 100,000 individuals to lead healthier lifestyles.

About the Client

In recent years, we are witnessing an explosion in cases of chronic diseases and conditions – such as hypertension, type 2 diabetes, heart disease, stroke, obesity and arthritis in India. The dramatic increase is such conditions is primarily due to poor lifestyle choices relating to lack of physical activity, improper nutrition & poor diet and higher stress at the workplace. There is an urgent need for people to regularly assess their health & wellness and make suitable lifestyle modifications to take control of their health at an earlier age which helps to minimize occurrence of chronic disease and improve quality of life.

Our client offers a personalized wellness ecosystem to proactively monitor wellness through 5 health centers.

Business Need

Recently, the client experienced a huge growth in walk-in customers for their health centers and wanted to digitize the whole process for customers. With the increased numbers, the client approached us to develop a solution to reduce process times & operational costs while providing superior customer experience.

The client wanted to develop a solution to help people change their health habits overtime and prevent disease through improving diet, exercise, and mood. Right from customer registration – till results & monitoring, the focus was to empower customers anywhere, anytime through a mobile wellness app and connecting various medical devices to the app.

Challenges and Requirements

  • The complete customer lifecycle management had to be digitized.
  • Based on IoT, medical devices & wearables had to be integrated with the solution.
  • Easy access to medical records on demand had to be provided to customers, while ensuring that the data contained therein is factual, accurate and current.

Our Role

For new, walk-in customers & office / home outbound consultation, we developed a browser-based registration module which captures their profile – personal, health history & habits – through an intuitive questionnaire spanning over 300 questions.

For the initial medical tests such as blood test, BP, BMI, etc, the devices are integrated to a common browser-based admin module and all the results in conjunction with their profile & prognosis are auto-generated and delivered to the customers’ email ID &  mobile app in the form of a wellness report. Customer can also login to a portal to view the results any-time.

Based on the prognosis, 4 devices are provided to the customer – an activity band, blood pressure monitor, glucose monitor and weight scale, with specific instructions on when & how to use them. Measurements from these devices are sent to the mobile app and both the customer & practitioners can monitor progress.

Business intelligence & machine learning modules are developed to provide practitioners with insights into overall wellness and to provide customers with prompts & reminders of the activities they are expected to do to develop themselves.

All these were achieved to the full satisfaction of the client. Currently, we are building the complete ERP system for managing operations – including HR, marketing, billing & revenue management, etc.


Microsoft .NET, Amazon Web Services, MySQL, Android & iOS


Sakha Global provided several benefits to the client including:

  • The solution provides a single window to all of a customer’s Health & Wellness data. It stores all the results of assessment, records data from wearables, home monitoring devices and helps with trends, recommendations and tips to improve continuously. Customer’s improved wellness is depicted through the dynamically changing Wellness score on the mobile app.
  • Customers’ can get assessment results instantly in the wellness app. The activity band and monitoring devices can be paired with the app to track health & fitness data and make improvements over time.
  • Through business intelligence and machine learning, personalized notifications, tips & recommendations are offered to customers to improve on their health and wellness score.
  • The solution positioned the client as a tech-enabled, progressive visionary and was able to increase it’s customer base by 10-fold, while also attracting marquee corporate clients.
  • The solution also enables the client to become a thought leader on wellness by generating demographic reports.

Sakha Global’s eLearning platform brings skilling to over 100,000 students.

About the Client

An E-learning content authoring company operating out of Pune with multiple offices in India and works with state & central government and their agencies as well as independent learning centres in rural areas. It’s learning content focuses on blue collar jobs which makes it unique in its reach and requirements. The company is a part of Skill India initiative of Government of India and has partnered with concerned agencies to bring its content to 200,000 rural centres across India.

Business Need

The company had a large collection of E-learning material developed using custom workflows and tools over several years. The company wanted a platform to deliver the content securely to anyone willing to take up learning; even when learners are located in remote areas with limited internet connectivity. The company also wanted to democratize content creation, curation and certification.

While building the platform we also migrated students from manual or older disparate systems (spread across 3 systems and 2 states) on to the new platform. Support was provided to learning centres during rollout for effective usage of system and feedback points were input in next iterations of development.

Challenges and Requirements

  • Remote location and limited bandwidth availability at learning centres.
  • Large scale of enrolments, locations to serve and content available.
  • Secure yet cost effective delivery of learning media.
  • Interfacing with Government Agency systems and custom workflows for bigger channel partners.

Our Role

We started out with a detailed study of existing, mostly manual and insecure workflows in place. After this study, based on customer’s vision, we put forward a multi release development plan where each release will focus on the next most immediate business need. We also designed an architecture of the proposed system which is modular, scalable, available, secure and also works without live connection to our servers on 10 year old hardware (we expected this to be case with rural area, verified later).

We have completed 3 iterations of development covering:

  • Content delivery to remote locations with semi-active net connection. Direct to browser delivery for students with reliable network connections.
  • Secure packaging of learning media. External certification of security measurements.
  • Lowest possible hardware requirements of Atom processor with 2GB RAM running Windows XP SP2.
  • Migration from old systems and progressive upgrades with each new version availability.
  • Integration with CSC portal and custom workflows for select channel partners.
  • Full function learning management workflow for colleges / polytechnics and rural learning centres.


Java, Spring Boot, Hibernate, Angular.js, MySQL, Elastic, Mongo, Amazon Transcoder, Blender.


The new platform brings new selling points for the client:

  • Geography, connectivity and hardware constraint free content delivery.
  • Content is secure with encryption and subject to licensing policies.
  • Superior & productive learning management workflow.
  • Self-service workflow covering on-boarding, learning to certification to placement.

Enabling one of the largest mobile network operators to boost service.

About the Client

A large mobile virtual network operator (MVNO) in the United Kingdom, Ireland, Slovakia, Hungary, and the Czech Republic. It has over 5 million customers and counting. Our client has been awarded the highest level of customer satisfaction above any other major mobile by Satmetrix in 2015 and is a 2016 Which? Recommended Mobile Provider.

Business Need

The client has a significantly large customer base, which has consolidated from Pre-Pay & Bill-Pay users.

The goal of the project is to improve online self-care channel for the client’s customers through a mobile app which would be the easiest way to keep an eye on account essentials on the move.

The objectives of the engagement were to enable better user experience, branding, logical grouping of features, ease of navigation and zero cluttering.

After the success of this engagement, we’re handling new development & maintenance of the Android & iOS mobile apps since past 5+ years, thus freeing the client’s internal resources to tackle other pressing tasks and lending flexibility in marketing & customer care.

Challenges and Requirements

  • Design of existing system was quite clunky and only technical, lacking in UI design & user experience.
  • Development of Android & iOS apps from scratch.

The specific feature requirements in the app were:

  • Account Balance
  • Top Up, Manage CC Cards, Top Up History
  • Vouchers, Club card Deals, Web Text
  • Account Details, Club card, Data Roaming, Move Number
  • Chatting, Account Usage, Bill History, Add-Ons, etc
  • Google Analytics using (GTM & Firebase)

Android: The application was delivered in 18 weeks and the design followed Android Material design concepts with support covering from OS 4.4 to 7.0.

iOS: The application was delivered in 18 weeks and with support till Swift 3.0.

Our Role

Since time was as critical as cost savings, we used cross-geo teams to sync and deliver mobile apps. We had to co-ordinate between multiple teams (Business Team, Backend / API team, Mobile Team and Testing Team).

In order to expedite delivery and have transparency, we followed Agile methodology with SCRUM model for delivery. We kept sprints duration between 2-4 weeks and had continuous delivery process with consistent feedback from stakeholders to make sure client is transparent about work-in-progress and is satisfied with the results.

The Android & iPhone apps have made mobile life easier for the client’s customers. The app is available on iTunes and Google Play and is totally free to download. Once customers get it, they just type in their Mobile phone number and follow the simple registration process. From there, customers get access to a range of options for their mobile world.

If they are on Pre-pay, they can easily top up their phone by debit/credit card, voucher or Clubcard Boost using the app. They can even top up other Mobile phone numbers using the app. If they need more data or texts, they can select add-ons as they go. It’s simple to take a look at how much credit they’ve got left and find out exactly how much they’ve being spending on data, texts or voice calls.

For Bill Pay customers, the options include being able to view bills and getting a full breakdown of calls, texts and data usage. They can top up the accounts of their friends and family and add Clubcard information to get extra points for every euro they spend.

There’s even more in the app – including free webtexts. This streamlined interface lets customers to send 200 National and 50 International texts every single month for free. It even replicates their contacts from their phone so texting is a doddle.

And if customers need to get in touch with the client, there’s a full Customer Care service on board. They get messaging to local agents as well as the full online FAQ. If that’s not enough to get the help they need, they can also link out to the client’s Twitter and Facebook with a tap.


Android (Android Studio (Version API 17 – API 25)) and iOS (Swift 3.1).


Sakha Global provided several benefits to the client including:

  • The continuous enhancements that are being carried out to the production environment by automating the load processes, adding reconciliation and automated balancing processes, are helping the client to improve the satisfaction of its customers.
  • Since we are adopting an iterative lifecycle, we can implement the risky and high priority requirements first. Also the minor changes in requirements can be better accommodated at design phase itself.
  • Since the new application is built using superior technology, implementing changes are much faster.
  • We have freed the client’s internal resources to tackle other pressing tasks and lent flexibility in marketing & customer care.

Sakha Global empowers India’s first reverse logistics application.

About the Client

A value chain organization focused on niche areas of supply chain, adding value to services. It is started by professionals with more than 100+ man years of experience. Headquartered in Bangalore, the client has its offices in Chennai, Hyderabad, Hubli & Mysore, and is expanding to Delhi, Mumbai & Pune.

Business Need

Reverse Logistics can have a significant impact on e-commerce/services/manufacturing companies’ bottom line with immense potential to recover value and provide superior support, experience and service to their customers. Historically, Reverse Logistics represents one of the complex areas in supply chain & logistics with many supply chain actors, acting internally and externally, with their own objectives.

The client required an alternative solution to the paper-based process of handling product returns, which was causing problems for their customers’ warehouse staff, finance department, retailers and consumers.

Challenges and Requirements

  • A headache for many manufacturers & suppliers is providing a simple way for everyone in the supply chain to handle product returns.
  • It’s a particular problem for consumer electronics suppliers, whose goods are often complex and expensive which means each return has many decisions and many players (freight, retail, repair, recycler, manufacturing departments, etc.) all making decisions and all unable to easily see what the others have done.
  • This causes an expensive confusion of returned goods arriving at warehouses without the proper checks by retailers and without complete paperwork.
  • Most businesses can efficiently manage forward logistics, but when they attempt to throw the supply chain into reverse, the wheels often start to wobble.

Our Role

Reverse Logistics application is a unique, end-to-end tech-enabled logistics solution for the most painful area of operations which has been a nightmare for all supply chain professionals. Reverse Logistics is one area where there has been huge revenue leakage across verticals and across the spectrum of business houses because of inefficient reverse logistics.

This application is a first time in the history of India, covering most of the verticals and multiple business models; be it manufacturing or distribution or retailer or service center. This application is curated to solve multiple logistics issues at the door step of the consumer as well as provide L1 service for all verticals at door step.

This is a major milestone in the history of reverse logistics industry in the country by providing a well-thought, complete solution for most problems in the reverse logistics area.

The application prompts retail returns clerk for all of the pertinent information and won’t allow them to proceed until all fields are filled in correctly. There is even a drop-down menu with a series of questions designed to trouble shoot common problems that are a result of improper installation or operation. This helps reduce ‘no fault founds’.

With the application, even a temporary staff member can handle a complex returns operation with minimal training and look like a professional while they do it. Customers are happy as the decision making process is immediate.  Staff are happy because the application minimises the time they have to spend on returns activities, which means more time being able to sell. The retailer is happy as it means that any credit due is processed and applied to their account much faster. The client’s customers are happy because they are lowering their costs and increasing their efficiencies.

The application is web & mobile-based and changes can be done on the fly – such as adding a new product or modifying a particular procedure – and roll them out immediately to various branches. Before the application, making changes was a long, drawn out process. Now, it is almost instantaneous.

We’ve set up the application so that the client’s customers can quickly integrate their returns policies and procedures into the system and populate the database with all of their product details. The Reverse Logistics application can also integrate with customers’ finance or ERP packages.


Alfresco Activiti, Angular JS, MySQL, Android Studio, Android SDK & Volley for client side REST API consumption.


Sakha Global provided several benefits to the client including:

  • The technology built enables the client’s customers to simplify process and deliver value. The solution is a more effective and cost-efficient method for handling returned goods.
  • Reduced costs through efficiencies in freight, repair, and the correct & visible application of processes.
  • Better service for retailers and consumers.
  • Products sent to correct local repairer rather than turn up at the warehouse of the client’s customers.
  • Enables consistent process by all retailers and other agents handling returns.

Empowering one of the first social media monitoring solutions.

About the Client

BuzzGain was founded in 2009 in Sunnyvale, California.

BuzzGain Inc. provides social media monitoring solutions. BuzzGain helps listen, learn, analyze and engage with digital influencers for outreach efforts in an actionable fashion.

Meltwater Group, one of the world’s leading global Software-as-a-Service (SaaS) companies, acquired BuzzGain Inc. for $4 Million in Feb 2010. Our solution is currently serving as the platform to enhance Meltwater’s social media monitoring service, Meltwater Buzz.

Business Need

Due to the explosive growth of social media in 2000s, tracking what’s being said about a brand or product on the Internet became a huge task. Product companies needed to track a multitude of social networking sites, blogs, message boards, podcasting sites, video blogs, micro blogs and consumer review sites on which conversations were being held. It was imperative to identify major influencers in social media and engage them to get the message out.

Big businesses use PR companies and manual systems to identify and track online conversations, but the price is often out of reach to small & medium businesses.

BuzzGain wanted to develop a do-it-yourself (DIY) solution which would enable SMBs to ‘listen, learn and analyze’ across millions of social media sources. The solution had to monitor keywords to discover relevant conversations and provide lists of news sites, blogs and other channels. There was also a need to identify influential writers and bloggers.

Challenges and Requirements

  • Remote harvest of unstructured data from multiple sources (~millions, 1.3 TB per month).
  • Data transformation (normalize across all sources), analyse data on different parameters i.e. sentiment, timeline, geography, source & demography – at run-time.
  • High performance distributed data storage and high availability.

Our Role

Sakha Global was involved from product conceptualisation to its complete development and maintenance.

The core BuzzGain service is divided into three sections: Listen, Learn, and Analyze.

  • The Listen section allows search for keywords that users have designated that they would like to monitor (i.e., keep a search out for a recently-released product model to see if they had any complaints).
  • The Learn section offers a listing of news sites, blogs, and Twitter users that have featured content relating to the user’s campaign keywords.
  • Analyze section offers charts and graphs depicting how hot the user’s search terms are on the web using technology developed with Senti-Metrics.

Sakha Global automated the data ingestion process and used big data for distributed storage. We also used NLP to develop Sentiment Analysis for Social Media.

Other functions include the ability to monitor blogs for new authors, or track which writers have been focusing on a certain topic.

BuzzGain brings all sources (blog, news, social media) into a single interface that is much easier to manage and to navigate.


Java, JavaScript, MySQL, Solr, HDFS, Hadoop


Sakha Global provided several benefits to the client including:

  • Proprietary media databases have long existed to help PR professionals understand their targets by building lists and sending press releases. We developed BuzzGain as an actionable application that facilitates engagement with mainstream & social media professionals without spamming them.
  • Indexing technology was developed to discover, index and catalogue over 100 Million blogs, 250,000 video bloggers, 3.5 million bloggers and 30,000 media properties including dailies, magazines, periodicals & Television / Radio.
  • 167-Node Hadoop cluster was implemented for distributed data storage having ~30TB data. MapReduce was used to map data across sources to produce intelligent analytics at runtime.
  • Through business intelligence and machine learning, personalized notifications, tips & recommendations are offered to customers.
  • The solution also enables the client to become a thought leader on Social Media by generating demographic reports.