Graded the way your experts would
Use your rubric and labeled examples so every eval is judged against the same standard, even as models and prompts change.
Evals you can rely on.
The evidence behind your AI release decisions. Hosted app or open-source CLI.
No credit card · bring your own keyMicrosoftVisaIBMSiemens HealthineersAlteryxClarivateAllstateOptumTyler TechnologiesSeismicInfo EdgeMahindra Last Mile MobilityathenahealthAvalaraRealPageKPMG
MicrosoftVisaIBMSiemens HealthineersAlteryxClarivateAllstateOptumTyler TechnologiesSeismicInfo EdgeMahindra Last Mile MobilityathenahealthAvalaraRealPageKPMG
Reliability
Evals are noisy. Plumloom tells you when the score holds.
Run the same check twice and the verdict can move. Plumloom makes the number trustworthy.
More than one opinion
Use multiple judges and see where they agree or disagree, so one model’s opinion does not decide the result.
A score that settles
Run enough evaluations to see whether the result is stable, with confidence intervals that separate real improvement from noise.
What you can evaluate
Agent traces, conversations, and scenarios
Evaluate the full context that matters, from a single response to a multi-turn conversation or complete agent trajectory.
Scenario
Test how a model answers a prompt against your rubric, run enough times to separate a real result from a lucky one.
Conversation
Check whether a multi-turn conversation reached the outcome you expected, graded turn by turn against your standard.
Agent trace
See whether your agent reached the right outcome, and for the right reasons. Outcome and path are scored separately, and you can import a run straight from your harness.
Trueline & Autoeval
One release-readiness goal, two workflows
Trueline is for collaborative review and release decisions. Autoeval brings the same evaluation into the development workflow for fast, automated gating.
Hosted · for your team
Trueline
Review evidence and decide what is ready to ship. Compare models, inspect failures, and make release decisions with your team in a shared workspace.
Explore Trueline
Open source · for developers
Autoeval
Bring release decisions into the development workflow. Run and gate evals from the terminal, CI, or an agentic harness in minutes.
Explore AutoevalBehind the answer
Other tools hand you the runs. Plumloom hands you the answer.
Not an eval framework
Frameworks run your evaluation and give the runs back, leaving the judgment to you. Plumloom returns the judgment: calibrated, scored by several judges, and for scenarios placed with a confidence interval.
Not observability
Observability shows what happened after a response ships, and needs your app instrumented first. Plumloom tells you whether to ship at all, before release, with a provider key and nothing to install.
FAQs
What does “reliable” actually mean here?
It’s the trustworthiness of the evaluation itself, rather than of your AI. Judges are calibrated to a standard your experts set, several judges score each output so you see the agreement behind the number, and for scenarios the evaluation runs multiple trials and stops once the score is stable, then reports a confidence interval. Whether your agent behaves correctly at runtime is a separate job.
What’s the difference between Trueline and AutoEval?
Two ways into the same engine. Trueline is the hosted UI for authoring and reviewing in a shared workspace. AutoEval is the open-source CLI for the terminal, CI, and coding agents. Both run on EvalCore, so the reliability is identical either way.
Do I need to instrument my app or send you my logs?
No. You bring the run: a scenario, a conversation transcript, or an agent trace your harness already produces. There’s no SDK to install and no logging pipeline to wire up first.
Which models can I evaluate with?
Bring your own key: OpenAI, Anthropic, or Together.ai. The models you select run on your key; there’s no provider markup.
How do the three contexts differ for reliability?
Scenarios call the model live, so all three mechanisms apply, including statistical multi-run. Conversations and agent traces are fixed artifacts, so their reliability comes from the calibrated standard and multi-judge agreement. To put a frozen finding under multi-run, reproduce it as a scenario.
Turn eval results into a release decision in minutes.
Compare the evidence, catch regressions, and decide what is ready to ship.