Every financial AI product now claims accuracy. Some promise guarantees. We took a different route: build the system so that the dangerous failure — a confident, wrong number — is structurally hard, and so that every claim can be checked by the reader in one click. This post explains the actual machinery, because “trust us” is not a methodology.
Layer 1 — the numbers never come from a model
Everything quantitative in AnalystBook — financial statements, segment tables, forensic scores, guidance changes, pay figures, ownership stakes — is extracted by fixed rules reading official SEC filings: XBRL for the financials, dedicated parsers for proxy pay tables, Exhibit 21 subsidiaries, Form 4s, 13D/G stakes, comment letters. The same filing always yields the same figures. A language model never decides a number, so a language model can never hallucinate one into your financials.
Layer 2 — the AI reasons over that record, not the web
Ana, our AI analyst, answers through read-only tools over the structured record — verbatim filing sections, period-aligned financials, insider activity, events. No web browsing, no answering from the model's training memory. If the record doesn't support an answer, the correct output is a refusal — and she's built to say “I don't have that in the filings yet” rather than improvise.
Layer 3 — gates check the AI before you read it
Grounding isn't a prompt instruction; it's enforced after the model writes. Every statistic in an answer is compared against the record and dropped if it doesn't match. Quotes get the subtler check — management “quotes” that sound verbatim but were never read from a filing in that conversation — and forces a corrective pass: read the document or drop the narrative. Investigations run under hard time and context budgets, so a long-running ask finishes honestly from the evidence gathered instead of grinding into nonsense.
Layer 4 — published analysis is audited against the SEC
Our Intelligence Briefs are written by AI that reads the actual filings under strict rules (never from memory; a figure not in the source is “N/A”, never estimated). Then every figure the brief states is audited against raw SEC data and a computed year-over-year change report. A brief with a mismatched figure is rejected and never published — the rejection is stored for internal audit. Published briefs carry their gate verdict and are monitored for staleness against the filing calendar.
Layer 5 — measurement, every day
Ana's behavior is pinned by a certified question bank across dozens of research desks — questions whose correct answers have been verified — and evaluated daily. Every answer in the product carries a one-click flag; flags land on an internal health dashboard we actually read. When something slips through, it becomes a new gate or a new certified question.
What we don't claim
We don't claim infallibility — no honest AI vendor can. We claim something more useful: verifiability. Every number links to the filing paragraph it came from; every AI claim carries a citation to the source document. You never have to take our word for anything, which is the only trust model that belongs in financial research. For research purposes only; not investment advice.
The full technical story lives on our Technology page.