AI-Native Market Analytics Platform for Indian Equities
Built a multi-billion-row verified market data warehouse and the MCP layer that exposes it to AI agents, so every answer traces back to verified data rather than model memory.
Client Indian fintech startup (anonymised, client preference)·Service lines Data platform engineering · quantitative analytics · MCP server engineering·Stack Google Cloud · BigQuery · Cloud Run · Cloud Scheduler · MCP · Python
The Challenge
An Indian financial technology startup was building an AI-native market analytics platform for the Indian equity market: a system where a user asks a plain-language question about a stock or the market and gets an answer grounded in verified data, not an AI model’s memory.
Fragmented sources, bespoke integrations
Every new AI application that wanted to work with Indian market data needed its own integration against fragmented, inconsistent sources.
The chat interface was becoming a commodity
Anyone can bolt a chatbot onto a data feed. What was missing, and what actually mattered, was a verified and trustworthy data foundation underneath it.
Confidently wrong is worse than nothing
Without that foundation, an AI system risks giving a confidently wrong answer on a decision with real financial consequences.
Our Approach
Build the platform data first.
TechTrapture partnered with the client to establish a verified, decades-deep market data foundation before building the AI-facing layer on top of it, so every answer the platform returns is grounded in verified data rather than assumption.
This is a direct application of TechTrapture’s specialisation in agentic AI and MCP server engineering for financial technology: exposing verified, domain-grade data to AI agents through production-grade MCP tooling, rather than treating the AI layer as a thin wrapper over an unverified feed.
What We Built
A medallion architecture on BigQuery
Raw market data lands exactly as received and untouched, providing an immutable audit trail. A staging layer cleans, types and deduplicates. A transform layer produces the corporate-action-adjusted price panel and the analytics computed from it. A reporting layer serves consumption.
Each stage is separated deliberately, because in market data the question is never only what is the number, but what was true on that date and how do we prove where it came from. That is only answerable when every transformation is a distinct, auditable step rather than a single opaque pipeline.
Scale as the engineering constraint
The warehouse holds billions of rows spanning more than two decades of Indian equity history, and grows by millions of rows every trading day.
That scale shapes every design decision. The full universe has to be ingested, corporate-action-adjusted, recomputed and quality-checked inside a fixed nightly window. And because the indicators involved are recursive, each day’s value derived from the day before, an error anywhere propagates forward indefinitely.
Precision as an architectural requirement
Automated quality gates run before anything is published, and the pipeline aborts rather than serve a figure that has not passed validation. A wrong number is worse than no number, and the architecture is built to enforce that rather than hope for it.
Recoverability by design
This is critical financial data. Any part of its history can be reconstructed from source: raw data is retained immutably, every derived value records how and when it was computed, and pipelines are re-runnable and idempotent so a failed or missed run repairs itself rather than leaving a silent gap. Recovery is a designed property of the architecture, not a procedure written down after the fact.
A deliberately thin MCP layer
The MCP server holds no analytics logic of its own. Capabilities are defined against the serving layer, so improvements to data and analytics reach every connected tool without redeploying the service, and one layer serves a chat assistant today and a screener, memo generator or alerting agent tomorrow.
The Outcome
| Before | After |
|---|---|
| Every AI application needed its own bespoke integration | One verified data layer serving every MCP-compatible tool |
| Answers grounded in model memory, unverifiable | Every figure traceable to verified source data |
| Analysts switching to a separate terminal or dashboard | Market data pulled into the AI tools they already work in |
| Data trustworthiness unprovable | Auditability as a designed property |
Before
Every AI application needed its own bespoke integration
After
One verified data layer serving every MCP-compatible tool
Before
Answers grounded in model memory, unverifiable
After
Every figure traceable to verified source data
Before
Analysts switching to a separate terminal or dashboard
After
Market data pulled into the AI tools they already work in
Before
Data trustworthiness unprovable
After
Auditability as a designed property
Financial analysts can now pull real, verified market data directly into the tools they already use, whether Claude, Claude Code, or any other MCP-compatible agent, and get an answer traceable to verified data no matter which agent they asked from. That data foundation is the platform’s real competitive advantage, and the product is positioned safely within financial-services regulatory boundaries by design.
- Billion-row warehouse engineering on BigQuery·
- Real medallion architecture·
- Quantitative and financial modelling depth·
- MCP server engineering for a regulated sector·
- Precision and recoverability treated as architecture, not process
What It Runs On
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