Nirav Prajapati, co-founder and CEO of Ignosis, on building intelligence infrastructure for India's financial institutions, why reliability trumps novelty, and the discipline of saying no to growth for growth's sake.
India's financial data ecosystem is evolving at a breakneck pace. Account Aggregators, open banking frameworks, and a flood of alternative data sources promise to reshape how credit decisions are made. Yet for all the data flowing through the system, lenders and fintechs continue to wrestle with a stubborn reality: raw data, no matter how abundant, does not automatically translate into reliable decisions.
Ignosis, a financial data intelligence platform co-founded by Nirav Prajapati and Chintan Sheth, was built to close that gap. Since its founding in 2022, the Ahmedabad-based company has earned the trust of over 125 financial institutions, raised $5.49 million in funding backed by Peak XV's Surge, Razorpay Ventures, and Cred founder Kunal Shah, and turned profitable in FY25. Prajapati, who also founded Pirimid Fintech and serves as Joint-Convenor of Sahamati's Associate Member TSP Steering Committee, has been at the centre of India's Account Aggregator movement from its earliest days.
In this conversation with Prajapati, understand how Ignosis is turning messy financial data into outcomes that matter.
What was the specific problem that led to Ignosis's founding, and how has your understanding of that problem evolved?
Nirav Prajapati: Ignosis was founded to solve a problem we experienced firsthand: financial data existed, but it was unreliable, fragmented, and unusable at decision time. In India, massive volumes of data flow through Account Aggregators, bank systems, bureaus, and alternate sources. Yet lenders and fintechs struggled to convert this into consistent, trustworthy intelligence. The impact was visible. Low approval rates, high drop-offs, mispriced credit risk, rising collection costs, and poor customer experience.
Initially, we believed the core problem was data access. Over time, we realised the real challenge was data reliability and interpretation at scale, especially in live, regulated environments.
Today, our understanding is clear: access to data is becoming commoditized. Intelligence is not. The real gap was never about APIs. It was the absence of orchestration, context, and intelligence that could power real decisions across underwriting, risk, and collections.
AI and data analytics have become crowded buzzwords. How do you differentiate Ignosis? What is genuinely different about your approach?
Prajapati: Most companies talk about AI as a feature. We built Ignosis as intelligence infrastructure for financial institutions. Three things genuinely differentiate us.
First, orchestration before intelligence. We do not assume data is clean or reliable. Our platform actively manages multiple Account Aggregators, fallback routing, health scoring, reconciliation, and monitoring to ensure insights are built on dependable inputs.
Second, BFSI-native intelligence, not generic AI. Our models are designed specifically for financial decision-making in India. They understand cashflows, income volatility, repayment behaviour, life events, regulatory constraints, and risk policies. This is where our Credit Risk Automation, what we call AI CAM, comes in. It helps risk teams automate assessments, reduce manual reviews, and improve decision consistency without compromising control.
Third, execution-linked intelligence. Insights only matter if they drive outcomes. Our intelligence directly powers underwriting, credit risk automation, and intelligent collections, optimising contact strategies, prioritisation, and cost efficiency, all embedded into live journeys, not static dashboards.
In short, we do not sell AI. We sell better financial outcomes.
Walk us through your client acquisition journey. Who was your first paying customer, and what did that early validation teach you about product-market fit?
Prajapati: Our first paying customers were mid-sized lenders and fintechs struggling with Account Aggregator reliability and income assessment for digital unsecured personal loans.
What early validation taught us was critical. Clients did not want more data. They wanted fewer failures. They cared less about models and more about conversion, approval rate, and stability. And trust mattered more than novelty.
Those early conversations forced us to shift from a "data provider" mindset to a mission-critical infrastructure partner mindset. Once clients trusted us with live credit journeys, product-market fit became very clear.
What does your revenue model look like today, and how has it shifted from your initial assumptions?
Prajapati: We started thinking revenue would be driven primarily by data pulls. Today, our revenue is increasingly outcome-linked and intelligence-led. We have usage-based pricing on orchestration and data access, premium pricing for intelligence modules covering income, risk, personas, and collections signals, and enterprise contracts tied to scale, reliability, and business impact.
The shift has been from "pay for access" to "pay for outcomes." As clients see measurable improvements, the value conversation naturally changes.
For readers who are not deeply technical, what does Ignosis actually do when a client engages you, and where does value creation happen?
Prajapati: In simple terms, when a financial institution works with Ignosis, we help them understand their customer better, using consented data, so they can make smarter decisions in real time.
We do three things. We collect consented data reliably, even when systems fail or change. We convert raw data into understanding: income, spending, risk, intent. And we activate that understanding across lending, collections, engagement, or advice.
The value is created where decisions are made faster, more accurately, and more responsibly.
What is the hardest technical or operational problem you have had to solve that your competitors have not cracked yet?
Prajapati: Reliability at scale. Most players can run models in controlled environments. Very few can maintain 85 to 90 per cent or higher success rates across millions of live, regulated financial journeys, across changing bank systems, aggregators, and compliance constraints.
Building systems that do not just work but keep working when the ecosystem changes has been the hardest problem. That is where most competitors struggle.
What is the one metric you obsess over internally that tells you whether you are winning or losing?
Prajapati: Decision reliability. We obsess over whether Ignosis consistently delivers accurate insights, reliable data, and successful outcomes, together, not in isolation.
It is not enough for models to be accurate or systems to be live. What matters is whether a real customer journey completes successfully, whether that is an approval, engagement, or collection, with trust, consistency, and zero surprises. If decision reliability drops, everything else becomes noise.
Where do you see the biggest risk to your business over the next 18 months, and what are you doing about it now?
Prajapati: The biggest risk is mistaking scale for maturity. As the ecosystem grows, complexity increases. Regulatory expectations, client demands, and reliability thresholds all rise.
We are addressing this by investing heavily in platform resilience and governance, building deeper intelligence rather than broader features, and staying disciplined about what we say "no" to.
Our focus is simple: earn trust before chasing growth.
