Financial crime, where the pattern is buried in paper
Financial crime files are rarely short of evidence. They are short of a way to see the shape of it — the relationship between an account, a company, and a person that only appears once the documents are read against each other.
The material is dense and highly structured: bank statements, registry records, transfer histories, invoices, corporate filings. Structure is the point — a table read as loose prose loses the meaning that made it evidence.
The connections that matter are rarely stated anywhere. They exist between documents: the same phone number on two unrelated files, a company name appearing in a statement and again in a registry search, a deposit dated the day before a transfer.
Finding those by hand is a matter of patience and memory across hundreds of pages, and the cost of missing one is that a file closes narrower than the conduct actually was.
What the platform does here
Fraud & Financial Crime
Entity and relationship mapping
Identifies people, companies, locations, and phone numbers across the file and shows how they connect, with an interactive network view over the relationships between them.
Structure-preserving parsing
PDFs, Word documents, and text files are parsed with headings, tables, and layout preserved, so a statement or ledger keeps the structure that gives it meaning.
Cited natural-language query
Ask questions across the whole file and receive answers grounded only in the evidence, each citing document name, page number, and relevance score.
Inconsistency detection
Factual discrepancies across documents are surfaced for investigator review, classified by type and ranked by severity, with the conflicting passages quoted.
Every operation is logged for defensibility, and no evidence is ever used to train a model.
Other use cases
See it on a case file like yours.
A demonstration runs on a synthetic case in under an hour — ingest, ask hard questions, and verify every citation yourself.