Teams catching regressions before they ship
ML platform leads and senior AI engineers who needed automated regression testing that samples from their actual production input distribution — not from the 50–100 test cases they wrote during development.
How teams are using Fyntune
Catching a 6% factuality regression before a production model swap
Kartova's ML team was swapping from GPT-4 to GPT-4.1 across their summarization pipeline. Manual QA on 50 test cases gave a green light. Fyntune ran against their full production input distribution and flagged a 6% factuality regression on long-form inputs their test set didn't cover.
Three silent guardrail failures found in week one
Meridian's AI research assistant had been in production for four months with no reported guardrail issues. Their manual QA protocol used 80 test cases. In the first week of Fyntune running against production input samples, three separate guardrail compliance failures surfaced under edge-case input patterns that were never in their test set.
Regulatory compliance eval criteria for a medical documentation LLM
Crestline operates in healthcare, where LLM output accuracy has direct documentation compliance implications. Their compliance team needed eval criteria tied to internal clinical documentation standards — cosine similarity scores weren't enough. Fyntune's custom LLM-as-judge criteria let them write requirements in plain language ("Does this response avoid diagnostic language outside the documented scope?") and run those checks automatically on every release.
ML teams across industries
Healthcare AI, legal document analysis, enterprise SaaS, and financial services — teams where a production LLM failure has consequences beyond a negative review.
Run your first eval suite before your next deploy
Free tier — 10,000 eval runs/month. Connect to your existing LLM stack in under 15 minutes. No credit card required.