New Delhi, Sept. 19 -- Digital technology is expanding in rural India, changing access to financial services and government systems. Senrysa Technologies, which integrates AI with public infrastructure, has enabled Aadhaar payments, micro-financing, and branchless banking for millions of unbanked citizens.
In a conversation with TechCircle, Kumar P Saha, Founder and MD, explained how the company applies AI across rural and urban India, handles sparse and unstructured rural data, develops enterprise solutions, and explores generative AI in healthcare, education, and governance, focusing on practical, scalable systems for India's diverse digital needs.
Edited Excerpts:
How is your work in AI and machine learning being applied, and what areas are you focusing on?
Our expertise covers computer vision, natural language processing, and generative AI. One of our products is Visai, a real-time video analytics solution.
We have also developed an AI-based intranet platform designed for organizations with large volumes of historical data. Many institutions have decades of records stored in various formats, often reaching massive scales. Retrieving specific information from this data is difficult. Our platform uses AI models to search and provide precise answers within the intranet, without relying on external tools like ChatGPT. It also references the exact documents and policies where the information is found.
In addition, we are creating a range of AI-driven tools. One example is Uniswap, a retail technology product under our high-tech domain. It uses AI to generate catalogues from images, automatically building entries with all relevant attributes.
How do you address the different challenges of working in urban versus rural areas?
We have developed a product portfolio that addresses the very different needs of rural and urban India, with technology at the core of both. The priorities in rural areas are centered on basic financial access and digital inclusion, while urban markets demand advanced enterprise solutions.
When financial inclusion began in rural India, people often spent an entire day just to complete one banking transaction or withdraw subsidies. This was a major barrier. Today, with digital banking infrastructure in place, every village has access to outlets that enable real-time transactions. The shift has been driven by technology, and it continues with new use cases being built for rural economies. For example, AI-based applications are being designed that can analyze soil composition from a photograph to guide farmers on what crops will grow best. Other tools allow farmers to take a picture of a fruit to instantly assess its quality and determine a fair market price. These kinds of solutions directly address rural needs and are not relevant in metro cities, where the requirements are different.
In urban India, technology adoption has taken another path. Enterprises and institutions require advanced platforms. Here, the focus is on real-time video analytics through solutions like Visa-based products, retail technology platforms for customer engagement and operations, and AI/ML-based bots for analytics and process automation. These products serve the needs of large organizations and institutions that are managing scale, security, and efficiency.
The overall approach is to design and deploy technology in ways that are suited to the environment where it will be used. For rural India, the emphasis is on accessibility, ease of use, and solving everyday challenges through digital tools. For urban India, the emphasis is on high-performance enterprise systems, automation, and data-driven decision making. This distinction shapes our entire product strategy across the country.
How should AI tools be designed differently for non-metro users compared to urban areas or Western markets?
When we look at rural India, the aim is to solve real problems. Right now, we are developing tools that are in the prototype stage and will soon be launched.
In rural areas, agriculture is central, but farmers often do not receive fair prices for their produce because they cannot properly grade its quality. To address this, we are enabling farmers to use their smartphones to take a picture of their crops-whether vegetables, fruits, grains, or jute-and instantly receive the correct grade and an estimate of the price they can set based on that grade.
How do you train AI models when data from rural and semi-urban India is often sparse, unstructured, or noisy?
Our AI team consists of specialists with doctoral degrees and research experience who focus on model development and training. The models are trained on both structured and unstructured data, which allows us to handle a wide range of use cases. A significant part of our work is in document processing. We have developed in-house OCR tools that can extract information from old and complex documents. In ongoing projects, we are scanning archives that go back more than seventy years and converting them into usable data. This capability is not limited to scanned records; we also integrate unstructured data from various external sources, including publicly available information online. These efforts allow us to create systems that learn from diverse types of inputs and make use of information that would otherwise remain locked in outdated or unorganized formats.
Considering India's bandwidth, hardware, and cost factors, what are the realistic opportunities for generative AI in this industry over the next three to five years?
AI is advancing at a pace that is far faster than most of us assume. Predicting its level of maturity even three years from now is extremely difficult. In this field, the only forecasts that hold some certainty are short-term, perhaps within a year, because research and investment are happening simultaneously across the world on a massive scale.
The capability of AI systems today illustrates this speed. From just a few terabytes of data, it is already possible to retrieve accurate information and references within seconds. To put this in perspective, imagine having access to the entire repository of India's policies. By entering a query about a new policy under consideration, the system could immediately identify whether it conflicts with any existing ones and point to the exact documents. At present, this task is slow and complex. Government departments often rely on teams of consultants to ensure a new policy does not overlap or contradict earlier ones. This is a real example of how generative and descriptive AI can streamline processes that currently demand significant time and effort.
Beyond such applications, AI has also reached a stage where it can generate content that closely resembles human work. The difference between AI-generated and human-produced material is increasingly difficult to detect. This development has wide implications for content creation, decision-making, and knowledge management.
At the same time, the growth of AI requires caution. Every organization needs to take responsibility for how its AI use cases are trained and deployed. Without safeguards, the same capabilities that make AI powerful can also be misused. Responsible development and oversight are essential if these systems are to provide value without creating new risks.
With India's digital public infrastructure like Aadhaar, UPI, and ONDC in place, which sector do you think will see the next major development-health, education, or another area?
Health is one of the areas where research and development are becoming increasingly important. In a country like India, with 1.4 billion people, there are not enough doctors or medical facilities to meet the needs of the population. Building infrastructure in every village and every settlement is not practical, which creates a gap in access to healthcare. Technologies such as AI and IoT can help address this gap. By connecting existing resources, analyzing large amounts of health data, and supporting decision-making, these tools can contribute to solutions that are cost-effective and scalable. Cost is a crucial factor in India, where the size of the population makes expensive systems unsustainable.
Work in this space is not limited to one organization. Many groups are experimenting with different models in health technology, and progress is happening across the sector. Alongside healthcare, education is another field that is beginning to change. AI has the potential to reshape the way students are taught, how learning materials are delivered, and how learners interact with content. This could alter both classroom teaching and distance education models.
Security is also an area where AI applications are expanding. In physical security, systems like CCTV can be paired with AI for monitoring and alerts. In digital security, AI can help detect threats, analyze risks, and respond to incidents more quickly. Similar opportunities exist in defence, agriculture, and other sectors. In each of these cases, the technology is less about replacing what already exists and more about filling gaps and making systems more efficient at scale.
The wider question is how quickly AI is advancing. Because the pace is so fast, there is a growing concern about misuse. This is why governments and regulators in India and elsewhere are working to design policies that set clear boundaries for how AI should be developed and applied. The aim is to ensure that while AI brings change across healthcare, education, security, and other fields, it does so in a way that is safe and responsible.
Published by HT Digital Content Services with permission from TechCircle.