India’s digital broking industry is entering a new phase where the focus is no longer limited to faster execution, sleek interfaces, or low-cost transactions. As millions of first-time retail investors enter the markets, the conversation is steadily shifting towards intelligence, contextual decision-making, and personalised financial guidance.
For Rohit Chatter, Chief Data Officer (CDO), Angel One Ltd., this transition represents one of the biggest structural shifts the broking ecosystem has seen in years. According to him, the next generation of investment platforms will not simply execute trades, they will actively help investors understand markets, interpret risks, and make more informed decisions.
In an interaction with Express Computer, he speaks about how Angel One is building an AI-native investment ecosystem through “Ask Angel”, the company’s generative AI-powered financial co-pilot, while balancing governance, privacy, compliance, and hyper-personalisation at scale.
Moving beyond transaction-led broking
According to Chatter, India’s digital broking journey initially evolved around execution efficiency, faster trade placement, better interfaces, and platform reliability. However, the industry is now entering a far more intelligence-driven phase. “If you look at how digital broking evolved in India, it was really an execution story. Speed of trade placement, interface design, uptime reliability. All of that mattered, and we got good at it. But the piece that was missing was intelligence.”
He points out that the post-pandemic surge in retail investing fundamentally changed investor expectations. A large section of new investors entering the market today are not simply looking for another trading interface, they want contextual guidance and simplified decision support.
This is where generative AI, according to him, becomes transformational. For Chatter, GenAI’s biggest impact will come from democratising access to high-quality market intelligence. “The big one is access: making high-quality market context and research easier to discover and understand for everyday investors, right when they need it.”
At the same time, AI also creates opportunities for deep personalisation and stronger long-term engagement.
“You can actually tailor insights to someone’s specific portfolio and risk appetite, not just show them generic research,” he says, adding, “Honestly, I think the less obvious one is trust. When someone feels like the platform genuinely understands their situation rather than just pushing products, they stick around.”
Building ‘grounded intelligence’ with AI
Internally, Angel One refers to the philosophy behind Ask Angel as “grounded intelligence”, an approach designed to ensure that AI-generated responses remain contextual, verifiable, and trustworthy.
Given the risks associated with hallucinations and misinformation in financial services, the company built multiple governance layers before launching the platform. “We put guardrails in place to minimise hallucinations and to avoid unsupported claims,” he points out.
The architecture itself combines real-time market data, technical indicators, portfolio-level information, and research datasets that are organised into a financial knowledge graph.
“At the base level, we’re pulling in market data, fundamental research, technical indicators, and portfolio-level information, all in near real-time. That raw data then gets cleaned up and organised into what we call our knowledge graph. Think of it as a structured map of financial entities, companies, sectors, events, and how they relate to each other,” he explains.
On top of this, Angel One uses a retrieval-augmented generation (RAG) framework to ensure that the AI responds using verified internal intelligence rather than generating unsupported outputs. “When a user asks a question, we don’t just hand it off to an LLM and cross our fingers. The system first pulls the most relevant, verified data from our knowledge base, and then the language model’s job is to stitch that together into a clear answer. It synthesises; it doesn’t invent. That’s what separates a co-pilot from a chatbot, in my view.”
The intelligence layer is also deeply integrated into the user journey itself. “We didn’t want users to have to go looking for it. So if you’re already on a stock page, Ask Angel will surface relevant signals on its own. You don’t have to type a question first. That’s what makes it feel like a co-pilot and not just a fancy search bar.”
Governance becomes central to AI deployment
For Chatter, governance was one of the most critical aspects of the Ask Angel rollout. “This is probably the question I care about most, and I will be upfront. We genuinely lost sleep over this before launch. In financial services, getting an answer wrong has its own implications. Once you internalise that, it changes how you build everything.”
The company therefore built governance mechanisms at both the data layer and the model layer. “Every data source that feeds into Ask Angel has to go through a proper onboarding. We check where it comes from, how fresh it is, how accurate it’s been historically. We don’t let random internet data flow into our responses unchecked. That’s a standard we are strict about.”
At the AI model level, Angel One implemented controls to prevent hallucinations and ensure the system remains within regulatory boundaries. “We have intent detection so the system can distinguish education and information from requests for recommendations — and it’s designed to avoid crossing into personalised advice. That’s a regulatory line you absolutely cannot blur, and the system is designed to stay on the right side of it.”
Before launch, the platform also underwent compliance reviews and independent security assessments.
Importantly, Chatter believes compliance and innovation are not inherently contradictory. “I think people assume speed and compliance are always in tension, but they don’t have to be, not if you build the governance infrastructure from day one.”
Hyper-personalisation without compromising privacy
Chatter avers that personalisation in financial services only works if users trust how their data is being used.
The platform uses portfolio-level context and user preferences to make interactions more relevant and educational. “If you already have significant exposure to a sector, the response can highlight concentration and risk considerations in an educational way.”
At the same time, Angel One has established clear boundaries around data usage. “Your portfolio data is used to serve your experience—used to provide and improve the service, consistent with your consent, applicable law, and our privacy policy,” he adds.
The company is also actively monitoring algorithmic bias within AI models. According to him, bias governance cannot be treated as a one-time exercise. “We’ve built bias detection into our model governance process as an ongoing practice, not something we checked once and moved on from.”
Conversational AI lowers the entry barrier for new investors
For Chatter, conversational AI is not simply a UX enhancement, it is becoming fundamental to financial inclusion and retail investor participation.
“It’s everything, honestly. You can build the smartest AI in the world, but if people can’t use it, it doesn’t matter.”
He believes the next wave of investors will come from Tier 2 and Tier 3 markets, where traditional financial interfaces and research-heavy investing experiences often create friction. “People coming in from Tier 2 and Tier 3 cities, often more comfortable in their local language than in English. The interface has been just as big a barrier as the knowledge gap.” Conversational AI removes much of this complexity.
The company spent significant effort refining tone, response length, language complexity, and interface behaviour. “The goal was never to build an AI tool for power traders. It was to build something a first-generation investor could open on their phone and feel confident using immediately.”
The evolving role of the Chief Data Officer
Chatter believes the role of the Chief Data Officer has fundamentally evolved beyond infrastructure management. “When CDO functions first started appearing, it was mostly about infrastructure: data pipelines, warehouses, quality frameworks. In an AI-first organisation like Angel One, that’s not the case anymore. The CDO today is as much a product and business role as it is a technology one.”
His role now spans product strategy, governance, AI enablement, and business transformation. “On any given week, I’m spending as much time with product and business teams, figuring out where AI can actually create value for users, as I am with the engineering and data science teams who are building it.”
At the same time, governance frameworks are becoming more operationally embedded into product decisions. “Our governance framework at Angel One isn’t a PDF that sits on someone’s drive. It’s an operating system that feeds into product decisions every week,” he says.
Towards a full-stack AI-native investment platform
Looking ahead, Chatter believes India is uniquely positioned to lead AI-driven financial innovation due to its rapidly growing retail investor base, digital adoption, and evolving regulatory ecosystem. “You’ve got the world’s largest and fastest-growing retail investor base, a young population that’s grown up digital, and a clear, evolving regulatory framework.”
Over the next few years, he expects AI within fintech to evolve from reactive systems towards predictive and educational experiences. He also expects voice-led and vernacular investing experiences to become mainstream. “I think voice and vernacular interfaces are going to become the primary way the next hundred million investors interact with financial platforms.”
For Angel One, Ask Angel is only the starting point of a much broader transformation journey. “What we’re working towards is a platform where AI is part of every touchpoint. From the moment someone signs up, through goal-setting, research, execution, all the way to portfolio review.”
He highlights that the point is not to bolt AI features onto an existing app. “It’s to rethink the investor experience from scratch with intelligence at the centre.”
And according to Chatter, the companies that successfully execute this shift could fundamentally redefine investing for the next generation of Indian users.
“I believe the organisations that get this right can meaningfully shape how the next generation engages with investing.”