Key Highlights:

• 91%+ latency reduction (120s → <10s average response time) 

• 40-60% cost reduction per query through infrastructure optimization 

• Sub-10 second AI responses enabling real-time decision support 

• Parallel execution architecture replacing sequential tool calls 

• Deterministic workflows eliminating hallucination retries and tool loops 

Challenge Faced:

Performance Crisis: 

The existing system delivered an average response time of 120 seconds per complex query, an unacceptable delay for mortgage advisors working with time-sensitive borrower inquiries. Users frequently abandoned sessions before receiving answers, creating poor engagement metrics and limiting platform adoption.

Technical Root Causes: 

• Sequential execution: Lender searches, rate lookups, and eligibility checks ran one after another instead of in parallel.

• SDK overhead: Extra abstraction layers added latency without real benefit.

• Tool loops: The model often retried tool selection, burning tokens on meta-decisions instead of retrieval.

• Hallucination retries: Bad or incomplete answers triggered repeated tool calls and inferences, further slowing responses.

• API delays: Database access was routed through unnecessary API layers instead of direct queries.

• High costs: Long runtimes, retries, and inefficient tooling drove up per-query costs.

Business Impact: 

120-second delays eroded credibility, left advisors hanging mid-conversation, The high cost per query threatened unit economics, making it difficult to scale the platform sustainably.