- Tool type
- AI document-integrity and fraud analysis
- Made for
- Claims, risk and document-processing teams
- Runs on
- fileAI web platform, API and MCP
What it does
fileForensics analyzes uploaded documents for evidence of manipulation, inconsistent details and other fraud signals. Its AI checks examine file integrity, history and internal logic, producing scores, flags and a report to support review before the document enters an extraction or business process.
Teams can use it through the fileAI platform, an API or MCP access. Developers can add the integrity checks to an existing pipeline without replacing their extraction system. The result is evidence for a human decision, rather than a definitive finding that a document or its sender is fraudulent.
What it can do
- File-integrity checks
- Manipulation signals
- Document scoring
- Flagged-file reports
- API integration
Before you choose
- A risk score does not prove fraud.
- Confirm production usage rates and data-handling terms before submitting sensitive files.
Listed under
Get started
- Create an account or arrange a sample review.
- Submit authorized documents and configure the relevant business checks.
- Inspect flags and supporting evidence before making a decision.
Plans and prices
The provider offers a free sample review and a developer sandbox. Confirm production rates and sandbox limits before deployment.
We couldn’t find published prices for fileForensics. See their pricing page ↗
Sources for this overview
Product pages, documentation, and pricing checked 30 Oct 2026. Each source supports part of the overview; availability and prices can change.
Ratings and reviews
No ratings for fileForensics yet.
What’s changed
Free accountSignals
Free accountData and security
Free accountCompare with similar tools
Free accountWhere this comes from
How we research- Company website
- file.ai: product, pricing and policy pages
- Human review
- Checked by a person
- knowledge_base
- manual_review
Last checked 30 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.