Great addition to our workflow! The custom metrics feature is a standout—makes error detection much more aligned with our specific needs. It’s definitely improved how we handle AI model development.
After trying to duct-tape together our own eval stack, we finally gave this a shot. It does what you’d expect: flags model issues, tracks performance, and keeps your iterations grounded in reality. Long overdue in this space.
One of the rare platforms that feels built by people who’ve actually shipped AI in production. The eval tooling is tight, feedback loops are well-designed, and there’s no fluff.
Not everything needs to be end-to-end, but if you care about the full lifecycle — data, testing, evaluation this is worth exploring. Especially useful for teams building LLM products under real-world constraints.
We’re finally able to quantify issues like hallucination, bias, and drift instead of just reacting to them after launch. This solves a real pain point for AI engineers.
The data side of AI often gets ignored until things break. Future AGI is one of the few tools that actually prioritizes evaluation and data feedback as core components. It’s not flashy — but it works, and it’s needed.
