An autonomous agent that monitors your LLM outputs around the clock, flags quality drift, and tells you before your users notice.
Tell EvalFlow what good looks like. Use our built-in criteria templates — accuracy, relevance, toxicity, coherence — or write custom checks in plain language.
Drop in a client library or point to your API endpoint. EvalFlow ingests inputs and outputs continuously, running evaluation in parallel — no sampling, no manual runs.
When quality drifts, EvalFlow fires a real-time alert — Slack, email, or webhook. With context: what triggered it, which outputs are affected, and what changed.
Every LLM output is scored against your criteria. No sampling, no manual runs. The agent never sleeps.
Statistical process control on your evaluation scores. Know when quality changes before it becomes a problem.
Plain-language evaluation criteria. No prompt engineering required. Define what matters to your product in minutes.
Slack, email, webhook — route quality alerts wherever your team lives. With full context, not just a number.
Time-series dashboards showing score distributions, failure patterns, and quality trajectories across every model version.
Everything available via REST API. Integrate evaluation into your CI/CD pipeline, model deployment workflow, or existing infra.
LLM-as-judge evaluation running on every output. Configurable judges — use any model you trust.
Statistical process control on score distributions. Detects gradual degradation and sudden drops.
Smart routing — suppress noise, escalate real incidents. Configurable thresholds and auto-escalation.
Connect any LLM provider via SDK or API proxy. Streaming support for real-time scoring.
"Most AI teams find out their model quality degraded the same way their users do — when someone files a bug report. EvalFlow is the agent that finds out first."
— evalflow philosophy