Ship Prompt and Model Changes Without Shipping Regressions

Govern access before spend happens

Recommendations like 'downgrade this model' or 'shorten this prompt' save money, but engineering teams reasonably ask how quality is protected. Without a registry and eval loop, that question gets answered by anecdote after the fact, or the recommendation gets ignored entirely because nobody wants to risk quality for cost.

• Cost-saving changes stall because nobody can prove quality holds up
• No versioned history of prompt changes tied to the eval results that justified them
• Model or prompt changes ship straight to all traffic with no canary
• Borderline eval results get silently approved or silently ignored

One policy layer across model usage

Every prompt is versioned in a registry backed by evaluation datasets you manage. Before a cost- or quality-affecting change — model downgrade, prompt rewrite, semantic-cache-enforce, route change — is applied, Cloptima runs an eval: deterministic checks or an LLM-as-judge comparison against your dataset. A run that clears the threshold can proceed to canary; a borderline run lands in a needs-review queue for a human decision instead of an automatic pass or fail. Every optimization recommendation carries this evidence directly: a six-dimension delta card — cost, latency, cache, guardrail, business ROI, and eval — with the eval and approval state attached, so quality-affecting recommendations are never marked safe without it.

• Prompt versioning with dataset-backed evaluation history
• Deterministic and LLM-as-judge eval runs, plus a needs-review queue for borderline results
• Release gates: model downgrade, prompt deployment, semantic-cache-enforce, and route changes all require a passing eval before applying
• Canary rollout percent and rollback wired into the same release-approval flow as policy changes
• Recommendations carry their eval and approval state directly — never a bare cost number with no quality evidence

Start with one app, then expand

Pick one recommendation class you already trust the cost math on — model downgrade is a common first choice — build a small eval dataset from real production traffic, and require a passing eval run before that class of change can apply.

Built for private production AI

Eval runs and release gates reuse the same governed approval queue as policy and budget changes, so a quality-affecting change and a budget-affecting change go through one auditable review path, not two parallel systems. Large payloads — dataset records, per-case eval detail — are stored separately from the metadata that drives access control, so dataset growth doesn't bloat the operational database.