# Tijori’s AI stack for company research
**URL:** https://tradingqna.com/t/tijori-s-ai-stack-for-company-research/194604
**Category:** General
**Created:** [June 1, 2026, 11:00am UTC](https://tradingqna.com/t/tijori-s-ai-stack-for-company-research/194604 "2026-06-01T11:00:16Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@nithin\_kumrr](https://tradingqna.com/u/nithin_kumrr)
#### Post date: [June 1, 2026, 11:00am UTC](https://tradingqna.com/t/tijori-s-ai-stack-for-company-research/194604/1 "2026-06-01T11:00:16Z")
@nithin shared the below on [X today](https://x.com/Nithin0dha/status/2061396773261005197?s=20):
> While everybody is busy stuffing an AI chatbot into every product, the folks at Tijori are using AI to build high-quality market research tools. They’re calling it Tijori Stack, and what they are doing is taking the messy and scattered universe of company disclosures, financials, filings, concalls, and regulatory documents, and extracting useful insights.
**They have several products:**
**1. Concall Monitor**
Tracking concalls is annoying because the transcript comes after a few days. Then there’s the fact that you have to sit through one hour of management-speak just to find the three useful things they said. Tijori is trying to fix that.
With Concall Monitor, a few minutes after a concall ends, you get the full transcript and an AI-generated summary that extracts the key insights like pricing, margins, growth, demand, guidance, capex, risks, etc. But the interesting thing is not just summarization but the management consistency check. Tijori tracks what management has said historically vs. what they are saying now. If a company promised something four quarters ago, the system can help track whether they followed through, changed the story, postponed the target, or pretended the earlier comment never happened.
**2. Report on Demand**
Tijori ingests pretty much all the filings and disclosures that a company makes, from financials, concalls, exchange filings, and regulatory disclosures to other company-specific documents. Using this, they generate different kinds of company reports:
Risk Probe Report gives you all the red flags and key risks.
Management Credibility Report looks at what management said versus what they actually did.
Five-year revenue and EBITDA estimate is an AI-assisted financial forecast grounded in company data.
All these reports are useful when you are doing first-pass research on a company. If you are tracking five companies and want to quickly figure out which are bad and which are worth researching further, these reports help. Instead of reading annual reports, concalls, exchange filings, presentations, and financials from scratch, you can use these reports to build a baseline view in minutes.
**3. Radar**
Radar is probably my favourite idea in the stack.
It allows you to define a metric or risk you care about for a particular company, and Tijori keeps scanning company disclosures and financials for relevant mentions. When the metric you define shows up, you get an alert.
And the metric does not have to be a typical financial metric.
You can track things like client concentration, employee growth, dollar revenue exposure, margin pressure, working capital stress, promoter pledging, regulatory risk, capex delays, inventory build-up, receivables, raw material pressure, or anything else that matters to your thesis. This is powerful because every investor has a different question.
Radar turns those questions into live monitors. I don’t think there are many products in India, or even in the world, that do this well.
**4. Atlas**
When you use ChatGPT or Claude to ask questions about a listed company, the answer is based on info that scraped from the public web. This is problematic because public web data about companies is messy.
There is news, commentary, rumours, stale articles, bad research takes, and gossip. Sometimes it is useful, but often it is noise. What makes Atlas different is that it gives you answers that are grounded in company-specific material like filings, disclosures, financials, concalls, and other company data.
Instead of asking a generic chatbot, “Tell me about this company,” and getting an unreliable answer scraped from the internet, you are asking a system that is constrained by the company’s own disclosures and financial history. This ensures there are no hallucinations.
You can check out these features here: [https://tijoristack.ai](https://tijoristack.ai)
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### Author:  [@RupeeRiser](https://tradingqna.com/u/RupeeRiser)
#### Post date: [June 1, 2026, 1:03pm UTC](https://tradingqna.com/t/tijori-s-ai-stack-for-company-research/194604/2 "2026-06-01T13:03:49Z")
> [@nithin\_kumrr](#):
>
> While everybody is busy stuffing an AI chatbot into every product, the folks at Tijori are using AI to build high-quality market research tools
I appreciate the additional effort the Tijori team has invested in building an AI engine that identifies, validates, and analyzes fundamental data based on factual information.
That said, the final investment decision still rests with the investor.
For AI to move closer to its full potential in investment decision-making, it should be capable of addressing prompts such as:
> “I want to invest ₹1 lakh in a stock. I am targeting an upside potential of 30% to 40% from the current price over the next four months, while limiting the maximum drawdown after investment to 10% to 15%.”
TijoriStack appears to have made significant progress in forecasting future valuation gaps by estimating the difference between current market prices and intrinsic values over a defined investment horizon using factual, publicly available fundamental data. This enables a data-driven assessment of potential return opportunities. The next major challenge is estimating downside risk with the same level of rigor, transparency, and confidence.
If the platform can effectively model potential drawdowns, its AI capabilities would move much closer to delivering comprehensive investment decision support. Downside risk could be triangulated using factors such as mutual fund activity, foreign institutional investor (FII) participation, market absorption patterns, stock-specific liquidity dynamics, indicators of liquidity stress, and management buyback valuations for individual stocks.
The analysis should be grounded in the interaction between price, demand, supply, and the market’s perception of institutional fair value.
A more robust downside model would seek to answer questions such as: Who is accumulating or distributing the stock? At what valuation levels are institutional investors willing to deploy capital? At what prices has management itself demonstrated conviction through buybacks? How much liquidity exists to absorb selling pressure? Where are the likely demand zones based on actual capital flows rather than historical price patterns?
By incorporating these factors, AI could estimate downside risk based on the behavior of informed market participants and capital allocators. This would enable a more realistic assessment of drawdown probabilities and a more actionable framework for investment decision-making.
If Zerodha succeeds in integrating these dimensions into the TijoriStack’s AI-driven investment framework, it could establish a new industry benchmark and set a standard that others in the financial services sector may eventually follow. Such a system would represent a meaningful step toward AI becoming a true investment decision-support engine rather than simply a sophisticated research and screening tool.
You have already invested significant intellectual effort into solving one of the most challenging aspects of investment research. That is precisely why I hope the team does not view the current achievement as the finish line. The opportunity ahead may be even more impactful.
The ideas outlined above could represent the next major leap in AI-driven investment decision support. The foundation is already in place, and the momentum is clearly visible. This is the ideal time to build on that progress and continue pushing the boundaries of what is possible.
While the problem is still fresh and the vision is taking shape, I would encourage the team to keep the flame burning. Zerodha has a unique opportunity to define the next standard for AI-assisted investing and create a benchmark that the rest of the industry will seek to follow.