The challenge
Underwriting teams at a Fortune 500 commercial real estate firm were manually processing 10+ large complex documents per deal — a 2 to 3 week effort per engagement involving multiple reviewers, legal cross-referencing, and compliance auditing. Volume was growing. Headcount couldn't scale with it. And the manual process created audit exposure every cycle.
The solution
QuantaumAI designed and delivered a 5-agent orchestration platform built on GPT-4, LangChain, and LangGraph. RAG pipelines handled document ingestion and retrieval. Business logic, legal review, and compliance audit rules were embedded directly into agent workflows. PII redaction and full audit trails were built into every deployment pipeline. A rapid prototyping layer using Lovable enabled business users to validate interaction patterns before engineering resources were committed — shortening decision cycles by 30%. An LLM evaluation framework using Ragas and DeepEval provided continuous accuracy measurement across every release.
The outcome
Processing time reduced from 2-3 weeks to 4-6 hours per deal. LLM response accuracy improved from 68% to 94%. Hallucination rate held below 4%. Platform reached 45% adoption within two quarters. Zero major audit findings across all releases. Secured unanimous C-suite approval. Adopted as the enterprise standard across 4,000+ users.
Tech stack
- GPT-4
- LangChain
- LangGraph
- RAG
- Azure OpenAI
- Ragas
- DeepEval
- Python
- Lovable