Turn financial filings into a research product people can trust.

DataStandard brings Indian listed company filings into Claude and ChatGPT. It makes financial data easier to work with while keeping each figure connected to the evidence behind it.

Less copying. More time working with the evidence.

Ask a general AI assistant for a company's revenue and you may get a confident figure with no way of telling which year it covers, whether it is consolidated or standalone, or where it came from. For research that is close to useless, because someone still has to open the filing and check.

DataStandard is designed around that check. Its figures come from company filings, and each one comes back with the filing it was taken from, the period it covers and the basis it was reported on. The analyst can verify a number instead of redoing the work.

Source, period and reporting basis travel with every figure.

The users, and our part

For research teams

Filings inside the assistant they already use.

Analysts ask their questions in Claude or ChatGPT and work with figures from Indian listed companies' filings without copying tables out of documents by hand. The source details come along with each number, so notes and models stay checkable.

  • Indian listed companies
  • Inside Claude and ChatGPT
  • Source on every figure
OLN Labs' role

Designed, built and run by our team.

We designed and built DataStandard for our client. We manage the technology behind it and keep developing the product with them, as part of our platform management work.

  • Product design and build
  • Ongoing technical management
  • Continued development

The engineering behind a dependable AI data product.

01

Provenance has to survive every step.

The link between a number and its filing can easily get lost somewhere between reading a document and answering a question. The platform is built so that link is kept the whole way through.

02

Periods and bases are easy to mix up.

Indian companies report both standalone and consolidated results, and their financial year rarely matches the calendar year. Each figure has to say exactly which it is.

03

Two assistants, one standard.

The data has to behave the same way whether someone reaches it from Claude or from ChatGPT, and that requirement shapes much of how the platform is put together.

04

Research doesn't pause for releases.

Analysts depend on it during working hours, so hosting, updates and fixes are planned around them. That is the part of the work we carry on after launch.

If you're planning something similar

Is DataStandard an OLN Labs product?

No. It belongs to our client. We designed and built it, and we manage the technology and continue developing it for them.

Could you build a source-traceable data product for our firm?

Possibly. Tell us what data you hold, who relies on it and where they need to see it. We begin with a discovery conversation led by our founder, then agree scope and milestones before any building starts.

Who owns the code and data on a project like this?

Our clients do. They own their code, data and accounts, and we provide documentation and a planned handover whenever they ask. More on ownership and handover.

Building on data people must trust?

Tell us about your data and who relies on it, and we'll reply within one to two business days.

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