AI agent pipeline cut query resolution time by 73%
Results
- 73%
- reduction in query resolution time
- < 1.8s
- average AI response latency
- 94%
- confidence-passing responses (no human review needed)
- 0
- policy data transmitted to any external cloud
Situation
A financial services enterprise was handling thousands of internal policy queries per week through email — slow, inconsistent, and consuming analyst hours that should have been spent on higher-value work.
Approach
We built a private multi-agent AI pipeline running on-premise SLMs inside their existing infrastructure. The system classifies intent, retrieves from a policy knowledge base, generates a grounded response, and routes edge cases to a human analyst — with a confidence score on every output.
