DG Compliance RAG & Verification
AI/ML Consultant — AutomateThis
Automated DG firm's customer response system
- RAG
- LLM-as-judge
- Retrieval
- LLM hallucination
Problem
Companies shipping dangerous goods need fast, accurate answers to regulatory questions spread across dense, jurisdiction-specific policy documents. Manual lookup doesn't scale, and getting an answer wrong isn't a cosmetic mistake — it's a compliance failure.
Approach
We built Nidral a RAG framework that retrieves the specific regulations and policy documents relevant to a compliance question, then generates an answer grounded in that retrieved text. Every generated answer passes through an LLM-as-judge layer that checks it against the source documents before it's returned — the same verifiable-over-flashy approach as the confidence-threshold routing in the document extraction case study, applied to a domain where a confident-sounding wrong answer carries real regulatory risk.
Outcome
Every answer is checked against its source material before a user sees it, rather than trusting the model's word for it — the verification layer that makes the system usable for a compliance decision, not just a plausible-sounding one.