We built Fyntune because production LLMs fail silently

How we got here

Before starting Fyntune, Fatima Al-Rashid spent four years building ML infrastructure at healthcare and SaaS companies shipping LLM-powered features to production users. Her last role before founding was as an ML platform engineer at a clinical documentation company — a team shipping LLM summaries to 200+ hospitals. The same failure mode kept surfacing: a prompt change would pass manual QA on 60–80 test cases, get a green light, and ship. Three to seven days later, support tickets would start appearing. Wrong diagnoses cited in summaries. Hallucinated drug names. Guardrail bypasses on specific phrasing patterns that nobody had thought to test.

The root cause was always the same: the test set wasn't representative. Manual QA at 60 samples can't catch failures that only surface at 0.3–1% of production traffic. But at real deployment scale, that 0.5% is hundreds of bad responses per day before the first support ticket lands.

Fyntune was built to fix exactly this: automated eval suites against a statistically representative sample of production inputs, on every prompt version and model swap, before anything reaches users. We are not a general MLOps platform. We do one thing — catch quality regressions before they ship — and the entire product is designed around that single constraint. We don't do model hosting, prompt management, A/B testing infrastructure, or general observability. Other tools cover those. We cover the gap between "the change looked fine in staging" and "users are reporting wrong answers."

We founded the company in Nashville in 2024, closed an angel round in July 2025, and are a team of four building the tool we needed and couldn't find.