Technical portfolio project
VeriClaim: LLM-Powered Business Claim Verification for Trustworthy Content
Detects fact-checkable claims, retrieves evidence from reliable sources, and returns Supported, Refuted, Misleading, or Unclear verdicts with citations for auditability.
This entry documents MuFaw research and technical work. It is not presented as a paid client engagement unless the project page explicitly states otherwise.
Project details
What we delivered
Overview
Claim verification workflow for editorial and compliance teams.
Delivers verdicts with citations for traceable review.
Why this exists
Fast publishing increases risk of inaccurate claims and compliance exposure.
LLM hallucinations make AI-assisted writing unsafe without grounding.
How it works
Claim detection -> evidence retrieval -> LLM verification -> structured output with citations.
If evidence is weak or conflicting, route to Needs Human Review.
Key engineering decisions
Evidence-first outputs to prevent hallucinations.
Human-in-the-loop review for borderline cases.
Iterative retrieval when evidence is thin.
Use cases
Content marketing and SEO publishing checks.
Sales enablement and proposal validation.
Product and technical documentation verification.
Compliance-sensitive industries.
Tech stack
Web retrieval with source ranking.
RAG-style grounding for verification.
LLM with strict output schema for verdict, confidence, rationale, and citations.
What MuFaw delivered
Claim detection and evidence retrieval pipeline.
Verification workflow with structured outputs and audit logs.
Reviewer queue with human-in-the-loop controls.
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