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 — relationships that run across thousands of transactions, documents, organizations and individuals, and only emerge when everything is viewed together rather than in isolation.
The material is dense and highly structured: bank statements, registry records, transfer histories, invoices and corporate filings. The challenge is not simply reading each record — it is following the transactions, names, accounts, dates and relationships across all of them without losing the context that connects one record to another.
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. No single record shows the pattern; it only exists across all of them at once.
Finding those by hand is a matter of patience and memory across hundreds of pages, and the cost of missing one is that the investigation may close without the full scope of the conduct ever becoming visible.
Case Intelligence Assistant analyzes the material in the file as a connected body of evidence. It maps the people, organizations, and identifying numbers across every document and shows how they connect — so a pattern that exists only between documents can be seen, reviewed, and verified against its sources.
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.