Use case
Cost Reduction
Eval-harness-driven sweeps of models, prompts, retrieval depth, and tool budgets to find the cheapest configuration that still passes quality gates.
Overview
AI cost optimization without an eval gate is racing to the bottom. With a gate, it is finding the cheapest variant that does not regress.
What it solves
Lets the team capture the cost reductions the model landscape keeps offering without shipping quality regressions.
How we build it
The harness runs a guided search across the cost-relevant surfaces: smaller models, shorter prompts, narrower retrieval, tighter tool budgets. Quality, latency, and tool reliability gates filter candidates; the cheapest survivor wins.
- Guided search across cost surfaces
- Quality, latency, reliability gates
- Per-route attribution to verify savings
- Re-run quarterly as the landscape moves
What changes when it is in place
AI cost per workflow becomes a tunable. Quarterly re-runs capture the savings as new providers and models arrive.