We are building an in-house research platform that automates equity analysis, generates structured insights, and helps investors make informed decisions — without the conflicts of interest that plague most advisory.
Most investment research in India is either unaffordable (institutional reports behind paywalls) or unreliable (social media tips, Telegram channels, commission-driven advice). We wanted to build something in between — a tool that does the heavy lifting of research automatically, but stays transparent enough that you can see exactly how it reached its conclusions.
The Veridhan AI Research Engine is our answer to that. It is a proprietary platform that ingests financial data, runs it through a structured analysis pipeline, and produces actionable insights — complete with confidence scores, risk indicators, and clear explanations. It is not a black box. Every output can be traced back to the data that informed it.
There is no shortage of AI tools in finance right now. Most of them are thin wrappers around large language models — you type a question, you get a paragraph of text. That is useful for general knowledge, but it is not research. LLMs generate plausible-sounding text, but they do not actually run financial models. They do not compute ratios. They do not track earnings revisions over time.
Our engine is fundamentally different. It is not a chatbot and it is not a text generator. It is a structured analysis pipeline — think of it as a research analyst's workflow, automated and scaled. The engine reads real financial data, applies quantitative models, and produces outputs that are grounded in numbers, not generated from training patterns.
We deliberately avoid deep neural networks for the core analysis. Instead, we use interpretable models — the kind where you can point to exactly which variable influenced the score and by how much. This is not a philosophical choice; it is a regulatory one. SEBI requires advisors to explain their rationale, and you cannot explain a rationale if your model is a 175-billion parameter black box.
The engine is built as a three-layer system. Each layer has a distinct job, and the layers feed into each other sequentially.
The first layer collects and normalizes financial data from multiple sources: exchange feeds, fundamental databases, corporate filings, and news. It cleans, deduplicates, and structures everything into a consistent format that the analysis layer can work with.
Think of this as the engine's intake system. It handles the messy reality of financial data — different formats, missing fields, delayed updates — and turns it into something usable.
The second layer is where the actual research happens. It runs quantitative models on the structured data: fundamental analysis (ratios, valuation, earnings trends), technical screening (momentum, volume, patterns), and sentiment analysis (news tone, filing language).
Each security gets scored across multiple factors. The models are interpretable — you can see which factors contributed to the score and by how much.
The third layer takes the analysis outputs and translates them into human-readable reports, alerts, and dashboards. It generates compliance-ready documents, risk assessments, and watchlist updates. Everything is structured so a human analyst can review and validate before it reaches the end user.
The human review step is not optional — it is built into the pipeline. We do not send AI outputs directly to investors without oversight.
The interactive prototype of the Veridhan AI Research Engine is available for preview. It showcases the full user interface, the analysis workflow, and the reporting output.