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LLM Testing Success

A global financial services firm deployed an LLM for compliance and regulatory support. We addressed three critical challenges: hallucination reduction to ensure factual correctness, contextual integrity across multi-turn conversations, and multilingual handling of regulatory terminology — delivering measurable improvements within 90 days.

Results

Key Outcomes

40%Hallucination Reduction
25%Context Coherence
+35%Multilingual Accuracy
+25%Compliance Query Accuracy

The Challenge

The firm's compliance team relied on manual review of regulatory queries across multiple jurisdictions and languages. Responses needed to be factually precise — hallucinated or contextually incoherent answers posed serious regulatory risk. Existing keyword-based systems couldn't handle multi-turn conversations or nuanced legal terminology, creating bottlenecks and exposing the organisation to compliance failures.

Our Solution

We deployed a fine-tuned LLM with retrieval-augmented generation (RAG) anchored to the firm's regulatory corpus. Custom evaluation pipelines — built on OpenAI Evals, LangChain, and WhyLabs — continuously monitored hallucination rates, context coherence, and multilingual accuracy. Post-pilot results: 40% hallucination reduction, 25% improvement in context coherence, 35% uplift in multilingual accuracy, and 25% gain in compliance query precision.

OpenAI EvalTensorFlowLangChainHugging FaceWhyLabs

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