Catch LLM regressions before your users do
Fyntune runs automated eval suites on every prompt version, model swap, and guardrail change — so your team knows within 90 seconds whether the new build is better or worse than the one before it.
Production LLMs fail silently
Your hallucination rate just jumped 3x after prompt v47. Nobody noticed for six days.
Quality regressions ship silently on every deploy cycle
Every deploy is tested before it reaches users
Prompt versioning. Regression detection. Guardrail tracing.
Three capabilities that close the eval feedback loop between every prompt change, model swap, and guardrail update — before any of it reaches users.
Every prompt change is a testable diff
Tag prompt versions like code commits. Fyntune runs your full eval suite on each version and surfaces the delta — semantic similarity score, factuality score, and guardrail compliance rate — side by side. You see exactly which change moved which criterion.
Automated quality gate on every deploy
Fyntune integrates with your CI/CD pipeline and runs your eval suite automatically on each release. If quality drops below threshold on any criterion — factuality, guardrail compliance, semantic similarity — it blocks the deploy and sends a Slack or email alert with per-criterion delta scores before a single user sees the change.
Know exactly when and why guardrails fail
Guardrail failures are the hardest regressions to catch manually — they pass unit tests but surface under specific edge-case inputs that your 50-case QA set never covers. Fyntune evaluates guardrail compliance across a statistically representative sample of your production input distribution on every release. Not for teams still running manual QA as their primary quality gate.
What a Fyntune eval run looks like
Real eval results with per-criterion delta scores. Prompt diff view showing which line change triggered the regression. Quality score timeline across your last 30 versions.
| Criterion | v47 Score | v48 Score | Delta | Status |
|---|---|---|---|---|
| Semantic Similarity | 0.847 | 0.831 | ▼ -1.9% | Warn |
| Factuality | 0.912 | 0.874 | ▼ -4.2% | Fail |
| Guardrail Compliance | 0.991 | 0.958 | ▼ -3.3% | Fail |
| Response Coherence | 0.884 | 0.891 | ▲ +0.8% | Pass |
| Tone Consistency | 0.923 | 0.918 | → -0.5% | Pass |
Plugs into your existing LLM stack
SDK for Python and TypeScript. REST API for any stack. No infrastructure changes required — eval runs asynchronously in a separate worker pool, never in your user-facing request path. Token latency on your production endpoint is zero.
SDK support for Python and TypeScript. REST API for any language. See all integrations →
From the teams running Fyntune in production
We switched from GPT-4 to GPT-4.1 in March. Fyntune flagged a 6% factuality regression on our summarization prompts within 90 seconds of the staging deploy — on inputs that weren't in our golden dataset. We fixed the system prompt, reran the eval suite, confirmed the regression was gone, and shipped the same day. Zero user impact. That used to take us two days of manual review to catch, if we caught it at all.
Our guardrail compliance score looked fine in CI because we were testing against 80 hand-picked cases. Fyntune sampled our actual production input distribution — 600 inputs — and found three separate guardrail failure patterns in the first week. All three were failing at the 0.3-0.8% rate on real traffic. At our volume that was hundreds of bad responses per day nobody knew about. Fyntune is the only tool we found that actually tests on the inputs your users send, not the inputs you remembered to write test cases for.
Start catching regressions before your users do
Free tier — 10,000 eval runs/month, no credit card. Connect to your existing LLM stack in under 15 minutes using the Python or TypeScript SDK.