Defacta

0 reviews

Defacta is an independent verification layer that sits between AI-generated or external text and its use in decisions, publications, filings, or other high-stakes contexts. It replicates the human verification process as a structured, multi-stage pipeline where LLMs accelerate specific steps but remain constrained by fresh data retrieval and source-grounded analysis. The platform checks text against credible sources, databases, and fact-checking registries to identify hallucinations, fabricated citations, unsupported claims, misinformation, and manipulative framing. It detects bias, deception markers, and internal contradictions using three independent LLMs running in parallel, where disagreement between models is itself treated as a signal. Users can paste text, articles, URLs, or AI outputs and receive structured verification reports with findings, sources, and recommended actions. Reports include source metadata, SHA-256 hashes, integrity checks, and a durable audit trail. Revision workflows allow editing, repair prompt generation, re-checking, and version comparison. Defacta serves analysts, compliance teams, journalists, researchers, and enterprise AI teams working in environments where accuracy is critical. It supports both personal verification and agent-to-agent workflows inside enterprise pipelines. Integration options include API, MCP server, and browser extension, with export to PDF, DOCX, Markdown, JSON, XLSX, and TXT.

Defacta

0 reviews

Defacta sits between AI-generated or external text and the moment that text gets used in a decision, filing, article, or publication. It runs each piece of content through a structured, multi-stage verification pipeline that checks claims against independent sources, fact-checking registries, and credibility signals. The system flags hallucinations, fabricated citations, unsupported assertions, bias, manipulation patterns, and misleading framing, then produces a full-spectrum report combining findings, source metadata, risks, and recommended actions. Three independent LLMs run in parallel during analysis, and disagreement between models is treated as its own signal. Every report is timestamped and SHA-256 hashed, creating a durable audit trail with job history, integrity checks, and evidence records. Users can edit flagged text, generate repair prompts, re-check changes, and compare versions within revision workflows, then export results to PDF, DOCX, Markdown, JSON, XLSX, or TXT. Defacta connects through an API, an MCP server, and a browser extension, fitting into existing workflows for both individual verification and agent-to-agent pipelines inside enterprise systems. It serves analysts, compliance teams, journalists, researchers, and enterprise AI teams across legal, finance, media, consulting, and GRC functions.