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    Your eval gave you a number
    Plumloom tells you if it holds

    Plumloom scores your scenarios, conversations, and agent traces with calibrated judges, repeats the run until the answer settles, and tells you plainly whether the improvement is real.

    Get started freeSee what it evaluates
    Start Free · No credit card
    Step one

    Bring what you ship

    Test the thing you are actually shipping, judged against the standards you actually care about.

    Scenarios

    See how your model handles the situations your users actually bring you.

    Written in-app · supports repeated runs

    Conversations

    Find out whether your assistant holds up across a whole exchange, beyond one good reply.

    .json transcript · expected outcome

    Agent traces

    Find out whether your agent reached the right answer for the right reasons.

    OpenTelemetry · OpenInference preferred
    All three are on the free plan. No credit card. Attach reference docs and the judge grades against your own policies and style guide.
    Step two

    Know exactly what went wrong

    Every evaluation ends in the same place: can I ship this? What you bring decides what you get to tell apart.

    Scenarios

    Which model to ship, and by how much

    Every model scored on the same scenarios by the same judge, with the margin between them made visible.

    Conversations

    Whether the assistant reached the outcome, and which turn lost it

    The whole exchange graded against the outcome you expected, with per metric scores and the transcript beside them.

    Agent traces

    Whether the run reached the outcome, and whether the path was sound

    Outcome and reasoning path scored separately, so a tidy sequence of steps cannot hide a wrong answer.

    One number hides too much. An agent run here scored 4.9 out of 5 on the path it took and still failed the outcome: a clean, sensible sequence of steps that confirmed a refund the tool never authorized. Scored once, it looks like a pass.

    Catch it on a real trace. Prove the fix on a scenario.Uploaded conversations and traces show you whether a failure is systematic or a one-off. Scenarios then repeat the test until the fix holds up.
    Behind the answer

    Other tools hand you five results. Plumloom hands you the answer.

    Three things stand behind every call it makes.

    Graded the way your experts would

    Show Plumloom a few responses your best people have already rated, and every evaluation grades to that same bar.

    More than one opinion

    Several judges score the same answer independently. You see how much they agreed, so you know how much weight the number carries.

    An answer, not homework

    Plumloom keeps testing until the score stops moving, then says whether it is safe to act on. When it never settles, it tells you that too.

    Instability is a finding. It earns a place on the results page.
    Ganesan Anand

    Plumloom exists because AI teams need more than a score.

    The same model can produce different outputs. Different judges can reach different conclusions. Yet important decisions are often made from a single evaluation run.

    After leading AI and product initiatives at Adobe, eBay, Paylocity, and Wells Fargo, I saw the same pattern repeatedly: teams wanted confidence, and their evaluation process could not provide it.

    Ganesan Anand, Founder & CEOPreviously led AI and product at Adobe, eBay, Paylocity, and Wells Fargo

    FAQ

    How is Plumloom different from observability tools?
    Observability tools show what happened after a response was generated, and they need your app instrumented first. Plumloom helps you decide whether an evaluation result is trustworthy before you ship, with nothing to install. The same split applies to eval frameworks: most can run your evaluation repeatedly, then return the runs and leave the judgment to you. Plumloom returns the judgment. Many teams use both: observability in production and Plumloom for evaluation.
    What does the free plan include?
    25 evaluations a month, all three context types, up to 3 test cases per evaluation, multi-judge scoring on your primary model, and 7 days of history. No credit card. Repeated runs with confidence intervals, model comparison, the metric catalog, and JSON export arrive on Professional. See plans.
    What do I have to set up before I see a result?
    A provider key and a prompt. There is no SDK to install, no code to write, and no observability pipeline to wire up first. Everything happens in the interface, and most first evaluations finish in under a minute. Engineers can export any run as JSON.
    How do you handle score stability?
    A single run is a sample. Plumloom repeats runs until the result holds up, so you can tell signal from noise before you make a decision. Uploaded conversations and agent traces run once, since their text is fixed, and reliability there comes from multiple independent judges scoring the same artifact.
    Which models can I evaluate?
    Connect your own OpenAI, Anthropic, and Together.ai keys, on every plan including free, then choose which models appear in your workspace.

    Find out whether your improvements are real

    Run your first calibrated evaluation in minutes and find out if the result holds up.

    Get started freeNo credit card
    Get started free · no credit card