Document intelligence
AI evidence extraction from documents, with analyst review
NC Data turns documents into candidate facts, events, entities, relationships and risk indicators, then lets the analyst inspect the source passage before anything becomes a finding.
Why source-linked extraction matters
For due diligence, litigation support, fraud investigations and compliance reviews, an AI answer is not enough. The analyst needs to know exactly which document, page and passage supports the statement.
Document-heavy investigations fail when useful facts stay buried in filings, contracts, emails, spreadsheets, scanned exhibits or meeting material. They also fail when extracted statements cannot be traced back to the record. NC Data is built for the middle ground: faster extraction, but with human review and source context before a finding is trusted.
Candidate findings
The system proposes facts, events, entities, relationships and red flags that may be relevant to the case, while leaving the analyst in control of what is accepted.
Source passages
Each candidate finding is connected back to the source material that produced it, helping reviewers check the statement instead of relying on a detached summary.
Human approval
The analyst validates, edits or rejects the finding before it is used in a report, timeline, evidence pack or due diligence note.
Report-ready output
Approved findings can flow into evidence packs, timelines, case notes and due diligence reports with a clearer link between conclusion and source.
Supported material
Different investigations produce different material. NC Data is designed to work with structured files, unstructured documents and source material collected during research, while keeping the important review step visible.
- PDFs with page-level citation context.
- Word, Excel, CSV, Markdown and plain text files.
- Images with OCR where text extraction is required.
- Audio and video files with transcription for supported workflows.
- Web, registry and search results collected during an investigation.
Use cases for evidence extraction
AI-assisted extraction is most useful when the analyst has many documents and a specific question. The goal is not to summarize everything. The goal is to find the passages that matter, turn them into candidate findings and make it easier to decide what belongs in the final work product.
Due diligence files
Extract companies, people, dates, contracts, ownership references and risk signals from material collected during counterparty review.
Investigation timelines
Pull events from documents and notes so analysts can build a sequence of what happened, when and according to which source.
Evidence packs
Collect relevant passages for a finding, preserve the record behind them and prepare a cleaner handoff for review or reporting.
Regulatory review
Identify statements, disclosures and named parties that may need comparison against company records, sanctions checks or public data.
Controlled AI, not unchecked automation
Extraction quality depends on the source material, file format, OCR quality, language and the clarity of the research question. NC Data treats AI output as candidate work. Analysts still need to verify important statements, resolve ambiguity and decide whether a finding is relevant to the case.
- Keep original files and extracted findings in the same review workflow.
- Inspect passages before relying on the extracted fact.
- Separate raw extraction from approved findings.
- Use company research and sanctions screening to check named entities.
- Move approved findings into reports only after review.
Questions to ask of every extracted finding
Evidence extraction is useful only when the reviewer can test the output. NC Data is designed to keep extracted material close to the original source so analysts can decide whether a candidate finding is accurate, relevant and strong enough to appear in a report.
That review step is the difference between document automation and investigation work. A useful extraction workflow should help the analyst find the right passage faster, but it should also make it easy to reject weak or ambiguous output before it reaches a client, partner or internal decision maker.
- Which original document, page or passage supports the extracted statement?
- Is the extracted entity, date, event or relationship complete enough to use?
- Does the finding need to be checked against a company record, screening result or another document?
- Should the finding be accepted, edited, rejected or left unresolved?
- How will the source be cited if the finding appears in a report or evidence pack?
Built for controlled AI use
The workspace is local-first. Your core case material stays in the browser by default, and connected AI services receive only the specific context needed for the feature you choose to use.
For company-specific work, combine document extraction with the corporate intelligence platform. For standalone company research, start with a due diligence report and use document workflows when the case needs deeper evidence review.