Whitepaper: How We Built a Zero-Hallucination AI for Customer Support
LLMs hallucinate at 8–15% in customer support contexts. IrisAgent's Hallucination Removal Engine reduces that to near-zero.
This whitepaper covers the multi-layer architecture — multi-model federation, precision RAG with Qdrant, and post-generation validation — along with our evaluation methodology and production results across enterprise deployments including Dropbox and Zuora.
What you'll learn:
- Why generic LLMs hallucinate in support contexts, with real failure mode examples
- The 4-layer architecture that achieves 95%+ resolution accuracy
- Head-to-head benchmarks: IrisAgent vs. GPT-4o and Claude 3.5 Sonnet
- Rigorous evaluation methodology with documented rubrics
- Enterprise case studies with measurable results
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