Models drift. Providers update versions, prompts get edited, retrieval indexes change. AI output monitoring gives you a graded sample of what your system actually produced this week, so you catch quality drops before your users do.
We grade outputs. We watch your live outputs against your rubric, week after week. We do not train models, run fine-tuning, or sell experts we cannot verify.
Continuous monitoring is the repeated grading of samples from your live AI system against a fixed rubric, on a schedule. A one-time LLM evaluation tells you how good your model was on launch day. Monitoring tells you whether it is still that good after the third prompt edit, the retrieval refresh, and the provider's quiet version update. Good looks like the same task mix graded the same way every round, with changes flagged by severity instead of buried in averages.
Everything passes in staging. Then real traffic, real edits, and real provider changes do their work.
Providers update models and retire versions without asking. Your prompts were tuned for the old one. A fresh graded round shows exactly which tasks got worse and by how much, before the complaints arrive.
Someone tweaks a system prompt. The knowledge base gets re-indexed. Each change looks harmless alone. Grading a steady sample against a fixed rubric is the only way to see what the edits actually did to output quality.
Aggregate scores smooth over the failures that matter: the wrong refund, the fabricated citation, the confidently wrong medical note. We grade per sample with severities, so a rising P0 count is visible instead of averaged away. Our severity scale explains how that works.
Six months after launch, nobody on the team remembers the original quality bar. Every monitoring round is archived against the same rubric, so you always have a baseline to compare against, with receipts.
Each round, you send 20 to 50 recent outputs from production, or we agree on a sampling method for your logs. Your rubric stays fixed so rounds stay comparable.
Human reviewers grade every sample against the same rubric, backed by automated checks. New failures get flagged by severity, and we compare against your baseline round.
Sample-level grades, what changed since last round, new issue patterns, and recommended fixes. Plus a walkthrough call whenever you need one.
Every round is graded against the same rubric as your baseline, so the report can say plainly: the prompt edit on Tuesday fixed the tone problem and introduced three new P1 failures in the refund flow. That sentence is the whole point of monitoring. Without a fixed rubric and fresh samples, teams argue about vibes while quality quietly moves.
The report separates repeat failures from new ones. A new P0 that did not exist last round goes to the top with the full sample and a suggested fix. Old, known issues stay in the pattern section so they do not drown out the signal. Over time, the archive becomes your quality history: what broke, when it broke, and what fixed it.
Monitoring also settles arguments that otherwise eat meetings. When one person says the new prompt is better and another says the old one was safer, the round report ends the debate with graded samples instead of opinions. Teams that monitor stop relitigating quality in the abstract and start fixing the specific failures the report names. Over a few rounds, something else happens: the team learns which kinds of changes are risky for their system. Prompt edits that touch tone stay safe. Edits that touch scope do not. That institutional memory is worth as much as any single report.
A one-time evaluation is a snapshot: how good is the model right now. Monitoring is the film: the same grading, repeated on fresh production samples, so you can see quality move over time. Most teams start with a snapshot, then keep the camera rolling.
That depends on your release pace and risk. Weekly rounds suit fast-moving products; monthly rounds suit stable ones. We agree on the cadence with you at the start, and you can add an extra round any time you ship a big change.
No. We grade a sampled set of 20 to 50 outputs per round, chosen to cover your riskiest task types. Grading everything would be slow and expensive; grading the right samples catches the patterns that matter. Our sampling guide covers how to pick them.
It goes to the top of the report with the full sample, the written reason, and a suggested fix. If you want faster alerts between rounds, we can agree on an escalation path during setup. The report never hides a critical failure behind an average.
Yes. Human grading carries the judgment each round, and automated checks back it up on things like format compliance, citation presence, and consistency. The report shows both, separately.
Dashboards measure what your system did: latency, volume, click-through. They do not tell you whether the answers were right. Monitoring adds the missing half: human-graded quality on real outputs, round after round.
Send a batch of live outputs. We will grade them and set your baseline.
Not sure where to start? Talk to us and we will point you at the right evaluation.