We connect financial data, automate preparation, and keep sources traceable—so your team can research and report with less manual checking.
We own the data connections, extraction checks, source links, and operating workflow. We monitor missing records and corrections, maintain integrations, and improve the system with your reviewers. Your team retains financial judgments and sign-off.
Operations deliveryStart with a baseline for time to a verified answer, source coverage, and correction rates. Agree the definitions and review period, then check quality and effort alongside the result. These are candidate measures for your engagement, not claimed results.
Language models write confident sentences about numbers, and that is the risk in financial work. A reply that reads well but mixes a quarterly figure with an annual one, or a standalone result with a consolidated one, can travel a long way through decks and emails before anyone catches it.
So the job for AI here is narrow and practical: find the right record faster, put it in front of the person who needs it, and keep the source attached so they can check it themselves.
Every number should be able to say where it came from.
Whether the work is equity research or a monthly management report, these five details decide whether a figure can be reused safely.
We built DataStandard end to end: 1.86 million filings, about 3.8 TB of data, market screening and source-linked financial research inside Claude and ChatGPT. [Read the case study](/work/datastandard).
Plenty of finance teams spend month end pulling exports from accounting, billing and banking tools into one spreadsheet. We can connect those sources, prepare the report in the format your reviewers already use, and keep a link from each line back to the records behind it.
Where the inputs are PDFs, AI can help read tables and pick out figures. Those extractions need a check step, and the original page should sit one click away from every value taken from it.
Most of the real decisions in a financial data project concern records and responsibility rather than models.
Usually, yes. We start by reviewing how the data can be accessed, how clean it is and who checks the outputs today. From there we agree a first scope, often one recurring report or one research question, and build out from that once it holds up.
Tell us about your goals and what is holding your business back.