Solution
For research, credit, and market-data teams: disclosures from 10-Ks, 10-Qs, and annual reports extracted, validated, and normalized — cited and model-ready in minutes.
The problem
A filing drops and its metrics sit buried in dense text and broken tables until someone rekeys them. Analysis waits on transcription.
Per-issuer extraction templates fail the moment a company reformats its report — and someone has to maintain every one of them.
Manual spreading caps how many companies a team can realistically cover. Expanding the universe has meant expanding headcount.
The product, not a promise
How it works
Filings arrive as PDF, XBRL, or scanned reports, across jurisdictions and languages.
Statements, footnotes, and management commentary are identified and separated automatically.
Financials are pulled with context-aware table reading — no template per issuer.
Sub-totals are reconciled and accounting logic checked; low-confidence values route to human review.
Normalized, cited data feeds models, risk systems, and terminals the moment the filing drops.
Who it's for
Research analyst
Head of data operations
Internal audit & IT
The gap between a filing’s release and usable data is where analysis teams lose. Key metrics sit buried in dense text and inconsistently formatted tables across 10-Ks, 10-Qs, and annual reports, and manual spreading caps how many companies a team can realistically cover. This solution closes the gap: filings in, validated structured data out, every number cited.
Real filings are messy — layouts vary wildly between issuers, tables break across pages, footnotes qualify the numbers above them. The platform ingests PDF, XBRL, and scanned documents across jurisdictions and languages, identifies financial statements, footnotes, and management commentary, and extracts figures with context-aware table reading. There is no template to build per issuer and none to maintain when a company redesigns its report. Line items then map to a standard taxonomy, so periods and peers land in one comparable schema instead of a per-company spreadsheet dialect.
Extraction without validation just moves the error downstream. The platform reconciles sub-totals, checks accounting logic, and scores its own confidence — anything ambiguous is flagged for a human before it enters your data set, while clean values flow straight through. That is what lets spreading run five times faster than manual entry without trading away the accuracy that high-stakes decisions require.
Every extracted metric links to its exact location in the original filing — click the number, see the source cell. Every extraction and correction is logged, which satisfies internal audit and keeps model inputs defensible long after the analyst has moved on. Built on a platform that has understood more than 1M+ documents, the practical outcome is coverage: teams spread more companies, faster, and analysts spend their time interpreting the numbers instead of retyping them.
Objections, answered
The platform reconciles sub-totals, checks accounting logic, and scores its own confidence. Anything ambiguous is flagged to a human before it enters your data set; clean values flow through. Every number stays cited to its source cell.
Yes. Line items map to your taxonomy so periods and peers land in one comparable structure, while as-reported figures are preserved alongside the normalized view.
Every extraction, validation result, and human correction is logged, and each figure links to its exact location in the filing. An auditor can trace any model input back to the issuer's own document.
There are no templates to build, so onboarding is connecting sources and confirming the taxonomy mapping. PDF, XBRL, and scanned reports work from day one, across jurisdictions and languages.
Watch a scanned annual report become validated, cited, model-ready data in minutes, live in the demo.
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