Approach & guarantees
Built for the team that won't trade quality for a cheaper invoice.
Cirra assumes you are right to distrust optimization that cannot show its work. So every finding is measured on your traffic, and every migration earns the cutover.
Quality is a hard constraint
Savings never win by silently degrading answers. Every candidate is scored against an eval set built from your production traffic, with an agreed quality bar before work begins.
Meaningful savings — or you owe nothing
The optimization sprint is the wedge for a reason. If we cannot identify meaningful inference cost reduction on your workload, the engagement costs you nothing.
Shadow before cutover
Migrations prove the alternative on live traffic in parallel. Traffic shifts gradually, with automatic fallback to the prior path if anything drifts outside bounds.
Your infrastructure, your bill
Client-specific validation runs in your cloud environment. Only reusable benchmarking touches Cirra-rented capacity — folded into the engagement, never a surprise line item you operate.
Generous handoff
Implementation ends with runbooks, monitoring hooks, and training. We are not optimizing for lock-in — the retainer exists because the ecosystem moves, not because the stack is opaque.
Fail closed on quality
If a candidate misses the quality gate, it is out — regardless of how attractive the cost curve looks. Missing eval coverage makes us more conservative, never less.
Data handling
Your traffic stays purposeful.
- ✓Traffic samples used only to build evals and measure candidates
- ✓No model weights or proprietary prompts leave your control without agreement
- ✓Validation stays in your cloud; reusable benchmarks are anonymized patterns
- ✓Access is scoped to the engagement and revoked at handoff
What we report
Numbers you can defend.
Quality delta
Measured gap versus baseline on your production-derived eval set — reported for every recommendation.
Cost impact
Ranked cost effect of each opportunity, grounded in your traffic mix and serving economics.
Shadow fidelity
How the candidate stack behaved against live traffic before any cutover decision.
Drift readiness
On retainer: whether the current stack is still optimal after new model and hardware releases.
Skeptical? Good.
Ask for a sample sprint outline, eval protocol, and how shadow cutover works on a stack like yours.