How RevRag.AI Is Fixing Fragmented Journeys Of Digital Onboarding In BFSI Sector

Snazzy interfaces, lightning fast transactions, bouquet of services at fingertips – the frontends are flawless for Indian fintech apps – but the backends have barely evolved beyond call centres. Multi-step verifications, repetitive data entry, and cumbersome documentations still make the onboarding process a pain.

No wonder, this striking disconnect between front-end promises and backend realities in India’s banking, financial services, and insurance (BFSI) sector frustrates today’s digitally empowered consumers who expect instant and frictionless services, and eventually lead to massive drop-offs.

RevRag.AI revved up to fill this gap by easing onboarding and sales for banks and fintechs. “Revenue should never be lost to friction,” said Ashutosh Prakash Singh, one of the founders of the startup, explaining how each of the friction points leads to revenue leaks.

Unlike regular chatbots, this Bengaluru startup develops intelligent AI agents that are integrated into apps and websites. These agents can interact in multiple languages, follow rules, and guide users through the full process, including filling up forms, finishing the KYC, making payments and even reconnecting to users, who leave halfway.

RevRag.AI targets mid-to-large banks, insurers, and fintech companies for business. These organisations handle high volumes of digital applications, but struggle with user drop-offs and high manual overheads. RevRag.AI helps them boost conversion rates by up to 25% while reducing operational costs by around 30%.

When Singh, a Galgotias University engineer with experience in revenue tech, converged with Neeraj Gupta, an MTech from IIT Kharagpur with experience in product-led growth, and Pankaj Gupta, an MTech from IIT Kanpur with background in AI Engineering, after a decade of distinct career paths, their middle-class upbringings fostered a shared drive to build meaningful solutions.

The trio rolled out RevRag.AI in 2023. Their combined skillset – Singh’s BFSI leadership experience, Neeraj’s user-centric design approach, and Pankaj’s AI infrastructure knowledge – helped them transform their previous venture, Blance (a goal-centric recurring deposit fintech platform), into RevRag.AI.

The startup operates on a usage-based pricing model, where AI voice usage is charged based on the number of minutes used, while AI chat or text generation is charged based on the number of responses it creates. Most of its revenue comes from enterprise BFSI clients, who are its key targets.

RevRag.AI has managed to do $50K ARR in 3 months (changed their core product focus from February 2025)and is aiming for a $5 Mn topline at the end of this fiscal. With backing from Powerhouse Ventures and strategic angels from 6Sense, CRED and Slintel, the company has raised $600K so far.

Early-Day Roadblocks And Challenges

A fundamental market insight lies at the core of RevRag.AI. Abandonment during a financial process shows a systemic failure, and not a lack of customer interest. The founders believe that when a prospect initiates an application, the intent to purchase is evident. A departure midway stems not from a second thought, but from the friction with complexities in the process.

A principle that no revenue should be sacrificed to procedural friction became the cornerstone of the company’s mission. “Our multilingual, compliance-ready AI agents step in at that exact moment by guiding, nudging, and even completing the KYC, filling up forms, and assisting in payments in real time so that disgruntled deserters turn into delighted customers,” Singh said.

And, how does it pull it off? “There are four guiding rules we have laid out. One, revenue-first AI model should measure conversions, not tickets. Two, understanding user empathy that explains finance in a first-time borrower’s language. Three, creating trust-by-design with RBI data localisation and SOC 2/ISO 27001 baked in. And, four, India-scale engineering that delivers sub-second voice in eight languages for millions of daily interactions.”

RevRag.AI positions itself as a revenue acceleration platform that enhances operational efficiency and delivers measurable revenue outcomes by transforming broken onboarding funnels into real-time, self-served experiences.

The founder claimed that their novel approach helped the startup secure four large enterprise clients within its first year. Early results showed up to 25% improvement in conversions. It wasn’t, however, an easy task.

Winning the trust of enterprises has always been the biggest hurdle in highly regulated industries like banking and insurance, especially for a startup with no affiliation to established tech giants. Financial institutions are cautious, particularly regarding compliance and security, and often require clearances from regulators, such as the RBI, before engaging in such activities.

The next challenge they encountered was building an accurate, responsive voice AI for Indian languages and dialects. It was a deeply technical and resource-intensive task. Off-the-shelf models do not perform well with the Indian regional languages, and GPU costs can be prohibitive for startups like RevRag.AI.

“RevRag began the journey as an AI-SDR tool. When we saw a stronger pull in onboarding, we refunded paid pilots $100K. It was a scary decision for any team,” Singh said.

RevRag started with making AI tools for sales representatives to find customers. The founders soon noticed that the clients were excited about a different part of their product – getting customers set up and ready to use their services.

The scary part was the financial risk they took. “Imagine returning $100K to paying customers who had already committed to your original product. That’s like turning away guaranteed income when you’re a small company trying to survive,” he said.

Changing product direction was another high-risk move. RevRag.AI’s decision to move came at a potential cost of alienating early adopters and sacrificing immediate revenue.

This bold move, however, allowed it to focus on building what customers actually wanted – a system that uses AI to handle the entire customer journey for financial companies, from verification to onboarding.

The RevRag.AI Blueprint To Win Over Challenges

The RevRag.AI thrashed out a three-point strategy to overcome the challenges along the course.

1. Earning credibility without big-tech backing

RevRag.AI put up enterprise-grade SOC 2 and ISO 27001 security frameworks for financial institutions. These table stakes helped it build confidence among the customers. Their strategic offering of on-premises deployment, which is uncommon in the startup sphere, gave security-conscious clients full control over their data. To reinforce the credibility, the startup formed an evangelist board with former bank CXOs, including J Venkatramu, former MD and CEO of India Post Payments Bank. It gave RevRag.AI both industry validation and access to key decision-makers.

2. Building a budget Voice AI platform

The startup refined its speech models using datasets in regional languages to enhance accuracy in names and accents. “We fine-tuned multiple models on regional language datasets, bartered spare GPU hours, and reiterated until we hit sub-second latency with less than 6% error in more than 12 languages. That technical win turned a nice voice bot into a robust AI agent that BFSI teams could trust,” Singh said.

3. Pivoting without losing trust

The startup’s decision to refund $100K in paid pilots earned it respect. It worked with more than 19 design partners to co-create the onboarding product, ensuring that it solved real problems from the ground up. Within two months of launch post pivot in February 2025, they clocked $50K in Annual Recurring Revenue (ARR), validating both their product shift and user-first strategy.

RevRag.AI stands apart by delivering deeply personalised, action-driven AI, rather than generic chatbot experiences. At the heart of its platform is the Octopus Orchestrator, a self-learning policy engine that dynamically selects the best-performing channel, script, and language for each user in real time, boosting conversions by 5–25%.

While most competitors rely on one-size-fits-all chatbots, RevRag.AI offers revenue-optimised nudges tailored to individual behaviour. The system is powered by retrieval-augmented large language models (LLMs) that are directly integrated with core banking systems. This allows the AI agent to push payment links, trigger KYC APIs, and escalate complex queries to human agents, far outgrowing the capabilities of traditional FAQ bots or call-centre voice assistants.

The platform is built with compliance-by-design, offering RBI data localisation, on-premises deployment, dual-layer guardrails for PII and tone, and full SOC 2 and ISO 27001 certifications from day one. It removes major procurement hurdles for security-conscious financial institutions.

How Galgotias University Helped Building RevRag.AI

“Galgotias was my launchpad. I arrived on campus with curiosity but no clear direction. Four years later, I was organising hackathons, front-manning a Hindi rock band, and pitching product ideas to real investors. Galgotias’ Entrepreneurship Cell gave me my first taste of building for users, rather than grades. A 24-hour hackathon in second year, fuelled by samosas and borrowed laptops in the C-Block lab, produced a crude lead-scoring bot. The idea flopped, but the adrenaline of demo day never left me and later shaped RevRag’s revenue-first mindset,” Singh said.

The RevRag Gameplan For Global BFSI

More than 790 funded startups are driving the Indian fintech landscape to throw up a $2.1 Tn market opportunity by 2030, on the back of a sharp rise in digital adoption in recent years. But digital onboarding remained a pain point. Many firms still lose high-intent users to clunky workflows and manual verifications.

In the next 1–2 years, the company aims to launch a no-code flow builder, expand its presence in Indian and American BFSI markets, and hit $1 Mn in ARR.

By 2028, RevRag.AI aspires to be an AI-powered revenue automation layer for global BFSI enterprises with an annual recurring revenue of $50 Mn. Towards this, the startup has lined up innovations like AI-led cross-selling, real-time risk scoring, and automated claims handling.

If RevRag.AI stays focussed on eliminating friction from financial user journeys and continues building for scale, it can grow in lockstep with the digital transformation sweeping across the global BFSI landscape.

With AI at its core and compliance at its foundation, the company is well-positioned to power a future where onboarding, upselling, and claims are not just automated but intelligently optimised. As financial institutions seek smarter ways to drive revenue and reduce drop-offs, RevRag.AI could become a significant player in the next-gen fintech stack.

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