A number used in research without its filing is hearsay.
DataStandard brings source-traceable financial data for India’s largest listed companies into Claude and ChatGPT. Every figure carries the filing, period, and basis it came from.
AI is good at thinking through a question. It should not be trusted to invent the facts.
Analysts are already using Claude, ChatGPT, and Claude Code to work faster. Those tools can help reason through a company, but they can also make up a number, mix two reporting periods, or forget the work after the conversation ends.
For an investment firm, that is not a small inconvenience. A figure needs to stand up when a colleague, client, or regulator asks where it came from.
A source is not enough. The receipt needs to travel with the number.
A financial figure can look correct while still being the wrong figure: standalone instead of consolidated, a forecast shown as an actual, or a restated result without the original context.
DataStandard attaches the useful detail to every fact — the filing, page, exact period, reporting basis, units, and restatement history. The analyst does not have to reconstruct the trail later.
We guarantee the inputs and the paper trail. We do not guarantee the analysis. Grounded means traceable and checked — it does not mean infallible.
The receipt should expose the details that commonly create mistakes.
A figure can look correct while still using the wrong period, basis, unit, or version. Those mistakes are difficult to spot once the number has already been copied into a memo.
DataStandard makes the source details travel with the number and flags restatements, so the evidence is visible before the figure becomes part of the analysis.
- Do not mix consolidated and standalone accounts in one trend.
- Do not present management guidance as a reported result.
- Flag a figure with the wrong unit scale before it is used.
- Handle a company changing its financial year without hiding the gap.
Good research should not disappear when an analyst changes tools, teams, or jobs.
Important thinking often lives in personal AI conversations, private files, and individual models. The firm pays for the work but has no reliable way to find the assumptions behind a past conclusion.
DataStandard works with the firm’s own Claude or ChatGPT environment, so the research stays alongside the files, chat history, and context the team already uses.
For Indian listed equities, accuracy comes from going deep on one market.
Indian filings have their own reporting patterns, fiscal years, restatements, and source documents. A broad data layer can cover many markets superficially. It cannot always carry the detail an investment team needs to rely on the answer.
DataStandard is built around India’s largest listed companies so the data, checks, and research workflow fit the market the team actually covers. Official annual and quarterly filings are brought into one repository, with data available after filing.
The research stays in your own AI, your own way. The numbers stay provable, and the firm keeps what it learns.