The Challenge & Story
We tested an internal customer service chatbot using a ChatGPT Custom GPT and quickly encountered typical enterprise obstacles: the context window could not hold our full documentation library, manual files frequently desynchronized from living articles, and high-reasoning models increased latency and token spend. We solved this by architecting a production Azure AI Search hybrid knowledge hub with automated accuracy benchmarking and role-based access control.
Measurable Business Outcome
Delivered sub-second retrieval (<850ms), eliminated manual documentation desynchronization, enforced role-based data isolation, and lowered query compute costs by ~65%.
Architectural Stack & Tools
- Azure AI Search
- Vector Embeddings
- FastAPI & Python
- Hybrid BM25 + Vector
- Continuous Evaluation
- Entra ID & RBAC