Days 1–2: telemetry and architecture review
Map current providers, RAG paths, retries, cache patterns and GPU/self-hosted usage.
Days 1–2: Connect telemetry
Collect request traces, token usage, latency, retry count, provider/model, RAG metadata and GPU utilization when available.
Days 3–5: Identify waste
Map unnecessary context, noisy retrieval, repeated requests, retry loops, overpowered model usage and idle serving capacity.
Days 6–9: Simulate controls
Estimate safe savings under token reduction, semantic cache, model routing, fallback and GPU right-sizing policies.
Days 10–14: Deliver decision report
Provide an executive report, technical recommendation, deployment level and first production control candidate.
Designed for low-friction proof
Enterprises often hesitate to place a new control layer in the inference path. The ML Mind pilot solves this by starting with evidence: where waste exists, how much can be safely removed, and which integration level is justified.
- No production routing change required for the first read-only phase.
- Clear executive summary for finance and technical teams.
- Policy recommendations grounded in real workload patterns.
Days 3–6: waste classification
Separate token, RAG, retry, routing, cache and GPU opportunities.
Days 7–10: safe savings plan
Estimate savings only where answer integrity can be preserved.
Days 11–14: executive readout
Deliver a board-ready savings brief, deployment recommendation and next-step plan.