AI Stock Analysis in India: A Step-by-Step Research Workflow

Good AI stock analysis in India should leave you with more evidence and clearer assumptions—not just a longer summary.

A useful analysis answers four questions:

1. How does the business make money?
2. What is changing in its financial and competitive position?
3. What expectations are already embedded in the price?
4. What future evidence would prove the thesis wrong?

AI can speed up the work behind each question. The workflow below keeps it close to primary sources and keeps the investment judgement visible.

Step 1: Start with the business, not the ratios

Ask the system to map the company in plain language:

• products and services
• operating segments
• customer types
• revenue model
• important volume and pricing drivers
• major costs
• working-capital needs
• capex and capacity

Then verify the map against the annual report and investor presentation. Ratios only become meaningful after you understand the economics underneath them. High margins mean one thing in software and another in commodity manufacturing.

Useful prompt: “Using only the attached annual report, explain how each segment earns revenue, what drives volume and price, and which costs or assets constrain growth. Cite the page for every claim.”

Step 2: Build a clean financial history

The next task is not to ask AI for five years of numbers from memory. It is to assemble a consistent statement history from filings or a source-linked database.

Check:

• consolidated versus standalone basis
• quarter, half-year and full-year periods
• restatements and reclassifications
• exceptional items
• segment changes
• share-count and corporate-action adjustments
• missing values

Missing information should remain unavailable. Turning absence into zero can manufacture growth, margins and ratios that never existed.

For each series, keep the source period and document. A table becomes research-grade when you can trace every important cell.

Step 3: Find the driver tree

Revenue growth is an output. A driver tree explains it.

Examples:

• lender: loan book × yield, funding mix and credit cost
• retailer: stores × sales per store × gross margin
• manufacturer: capacity × utilisation × realisation
• IT services: billable headcount × utilisation × billing rate
• marketplace: users × orders × value per order × take rate

AI can find candidate drivers in management discussion, segment notes and concalls. The analyst decides which ones belong in the model and which are merely management language.

The question to ask is not “Will revenue grow?” It is “Which observable driver must move for revenue to grow, and where will that movement appear first?”

Step 4: Read management across time

One concall provides commentary. Several calls provide a track record.

Use AI to compare:

• guidance issued versus later outcomes
• confident statements versus subsequent hedges
• metrics repeatedly discussed versus metrics dropped
• capex promised versus capex delivered
• temporary explanations that persist for several quarters

This is a language task where AI genuinely helps, but the comparison must use the exact calls and dates. A paraphrase without a timestamp is difficult to audit.

Our article on tracking historical guidance accuracy explains how to turn management promises into a testable record.

Step 5: Check earnings quality

Profit is an accounting result. Cash and balance-sheet movement help test its quality.

Look for:

• profit growth without operating cash-flow growth
• receivables or inventory rising faster than sales
• recurring “exceptional” income
• high other-income dependence
• capitalised costs
• debt increasing while reported profit rises
• dilution or share-count changes

None of these is automatically wrongdoing. They are questions that deserve explanation. A good AI system flags the relationship and shows the underlying figures; it should not jump from anomaly to accusation.

See stock forensics: finding problems before the market for a fuller framework.

Step 6: Separate facts, estimates and judgement

Keep three columns in the research note:

Layer	Example	Owner
Reported fact	FY26 revenue from the annual filing	Source document
Derived metric	Operating margin calculated from consistent inputs	Deterministic code
Forecast assumption	Volume growth of 8% in the base case	Analyst

This prevents generated prose from quietly turning an assumption into a fact.

AI can propose a first-pass forecast structure. It should not hide who chose the assumption.

Step 7: Value scenarios, not one future

Use a small number of coherent scenarios rather than a single precise target.

Each scenario should connect operating assumptions to financial outcomes:

• revenue drivers
• margins
• working capital
• capex
• financing
• cash flow
• valuation multiple or discounted cash flow

Then identify sensitivity: which assumption moves value most? That is often more useful than the base-case output.

An AI assistant can build formulas and tables. The spreadsheet or calculation engine should produce the numbers. Our scenario-analysis guide shows how to keep the futures internally consistent.

Step 8: Compare valuation with expectations

A low P/E is not an argument by itself. A high multiple is not a disqualification by itself. Valuation needs to be read beside expected growth, return on capital, balance-sheet risk, cyclicality and earnings quality.

Ask:

• What must be true for the current valuation to make sense?
• Is the company near peak or trough earnings?
• Are one-time gains distorting the denominator?
• Which peer group is economically comparable?
• How much of the forecast is already consensus?

The AI can organise the questions. It cannot eliminate uncertainty from the answer.

Step 9: Write the thesis and its failure conditions

A usable thesis is short enough to monitor. It should state:

• why the market may be misreading the business
• which two or three drivers determine the outcome
• what evidence would support the view
• what would falsify it
• which valuation range or scenario matters

If a research system produces twenty pages but cannot state what would change the conclusion, it has generated information rather than analysis.

Step 10: Keep the analysis alive

After purchase, monitor the thesis rather than only the share price. Link each key assumption to a disclosure or metric:

• demand to volume or order data
• pricing power to realisation and gross margin
• execution to capacity and utilisation
• management credibility to guidance outcomes
• cash quality to working capital and operating cash flow

This is where AI adds lasting value: it can compare every new filing with the original decision and bring the relevant change forward.

The quality test

Before accepting an AI stock analysis, ask:

1. Can I open the source behind every important number?
2. Is the reporting basis consistent?
3. Are facts separated from forecasts?
4. Are calculations reproducible?
5. Is historical analysis point-in-time?
6. Are missing values shown honestly?
7. Does the thesis state what would prove it wrong?

If the answer to several is no, the analysis is not ready—regardless of how polished it looks.

How Altys implements the workflow

Altys combines the evidence layers behind this process: filings, financials, concalls, guidance, shareholding, macro context, factors, modelling, forecasting, screening and portfolio monitoring. The company board connects the current numbers with the documents and events behind them.

AI is used to read and organise evidence. Calculations remain explicit, point-in-time and source-linked. The goal is not an instant verdict. It is a faster route to work an analyst can defend.

For the broader category, see finance AI in India and AI for the Indian stock market.