AI infrastructure cleanup: cost & reliability overhaul
A production AI system that was slow, over-budget, and failing silently — audited, root-caused, and rebuilt into something dependable.
The Challenge
The client inherited an AI feature shipped by a previous developer. It demoed well, but in production it was burning through the monthly model budget, responding slowly, and failing quietly. When retrieval missed, the system guessed instead of saying it didn't know, and nobody could tell the difference until a customer complained.
- Model costs skyrocketing due to daily embedding of duplicate documents.
- High request latency (over 4 seconds) causing user dropouts.
- Silent errors where LLM hallucinated instead of confessing missing context.
Operations Bottleneck
Redundant embedding calls and invisible API errors.
Infrastructure Solved
Telemetry-controlled indices costing 63% less tokens.
The Solution
We re-architected the retrieval layer around a persisted vector store, added a grounding step that refuses to answer when retrieval confidence is low, and instrumented every stage so cost and failure modes became observable. We delivered the diagnosis as a developer-ready roadmap first, then executed the refactor.
- Persisted index storage reducing duplicate vector embeddings.
- Retrieval score thresholds rejecting answers when search confidence drops.
- Comprehensive telemetry logging all model tokens, latency, and success rates.
Our Approach
Codebase Audit
Investigated legacy code to trace duplicate embedding loops.
Index Persistence
Migrated local in-memory indices to a cloud vector store database.
Grounding Filters
Programmed vector similarity filters to prevent empty retrieval responses.
Telemetry Hooks
Configured step-by-step tracing to track model prompts and costs.
Roadmap Handover
Created a detailed refactor roadmap defining APIs and milestones.
Production Overhaul
Refactored the endpoints and ran load tests under production traffic.
User Journeys
End User
SaaS Operator
Tech Stack
API Layer
- FastAPI (Python)
- Pydantic parsing
- Gunicorn server
Retrieval Database
- Pinecone Store
- Persisted Index updates
Telemetry Traces
- Datadog APM
- OpenTelemetry hooks
Model Guardrails
- Similarity score check
- System context bounds
Development Process
Persisted Indexing
Eliminated corpus re-embeddings by building an index updater triggered on document updates.
Grounding Pipeline
Implemented similarity validation steps before LLM calls to prevent empty queries.
Structured Telemetry
Added metadata tagging to record execution path, token counts, and API response status.
Load Testing
Simulated production load profiles to verify response latency is stable under 1 second.
Results & Impact
Measurable efficiency gains, reduced operations costs, and reliable integrations delivered.
Building something ambitious, or fixing something that's gone sideways?
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