Defacta is an independent verification layer that sits between AI-generated or external text and its use in decisions, publications, filings, or reports. It replicates the human fact-checking process as a structured, multi-stage pipeline, using LLMs only to accelerate specific steps while keeping all analysis grounded in fresh data retrieval and credible sources.
The platform checks claims against trusted databases, fact-checking registries, and independent sources to identify hallucinations, fabricated citations, unsupported assertions, and manipulative framing. Three independent LLMs run in parallel, with disagreement between models treated as a verification signal. Reports cover factual accuracy, source credibility, bias detection, linguistic manipulation patterns, internal contradictions, and selective omission.
Every finding is traceable and source-backed, producing timestamped, SHA-256 hashed reports that serve as durable audit trails. Users can store reports privately, share them publicly, or export to PDF, DOCX, Markdown, JSON, XLSX, and TXT. Revision workflows allow editing text, generating repair prompts, re-checking changes, and comparing versions side by side.
Defacta serves analysts, compliance teams, journalists, researchers, and enterprise AI teams working in legal, finance, healthcare, media, and consulting environments where accuracy is critical. It supports both personal verification and agent-to-agent workflows inside enterprise pipelines through API, MCP server, and browser extension integrations. The platform does not edit documents or replace legal responsibility — it flags issues, highlights risks, and provides a clear evidence record for human decision-makers.
