ValidationHow it worksUse casesKey benefits & featuresFAQCost OptimizationAI Research DashboardBlog
Build evaluatorFull walkthrough

Evaluation Builder

Know if your AI change is safe to ship

Argmin AI builds a calibrated evaluator that gates every model, prompt, or agent change against the cases your team cannot afford to regress.

Create release barFull walkthrough

First evaluator free · No card required

Run the quality bar before the product change reaches users.

Before you ship

Watch the release-check flow

See how a proposed AI change is checked against calibrated cases before it moves into release review.

Demo · calibration flow7 min to your first evaluator

Validation

Quality gate before optimization

Argmin AI Pareto cost reduction chartArgmin AI Pareto cost reduction chart

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Safety maintained

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Edge cases

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Evaluators

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Optimization

Internal Case Study: Mental Health Conversational AI

Main challenge: Reduce cost only after safety and quality had a measurable gate

Results

  • Pre-release evaluator suite
  • 400 edge cases before accepting changes
  • Safety tracked separately from cost
  • Optimization accepted only after quality checks passed

Process

How it works

A calibration flow for teams that do not have a clean golden dataset yet.

Inputs

Bring task, docs, traces, and hypotheses

Start with the AI task, domain docs, selected traces, and a few hypotheses about what good looks like. No golden dataset is required upfront.

TaskDomain docsSelected tracesQuality hypotheses
Bring task, docs, traces, and hypotheses
Argmin AI picks cases and analyzes evaluator mistakes
Cases

Argmin AI picks cases and analyzes evaluator mistakes

The platform finds normal, edge, and high-risk examples and surfaces where the evaluator disagrees with experts, so review time is spent on cases that actually move agreement.

Review

Experts review, confirm or correct calls

Experts review and correct evaluator calls Argmin AI drafts first — never from a blank page.

Experts review, confirm or correct calls
Corrections improve the evaluator and become the eval set
Calibrate

Corrections improve the evaluator and become the eval set

Every correction sharpens the evaluator and updates the calibrated eval set, quality rubric, score anchors, and calibration history.

Run

Test every AI change

Use the evaluator on prompt edits, model switches, RAG updates, routing changes, and agent releases.

Prompt editsModel switchesRAG changesAgent releases
Test every AI change

Put a quality gate in front of AI releases

Use one calibrated evaluator across experiments, CI, release review, and optimization.

First evaluator free · No card required

Key benefits & features

Release Bar

Release Bar

Turn product standards into checks that run before prompt, model, retrieval, or agent changes ship.

Core Cases

Core Cases

Keep the small set of cases that would change a release decision if they regressed.

Accepted Standard

Accepted Standard

Experts shape the rubric and corrections, so the evaluator reflects the standard your team actually trusts.

Decision History

Decision History

Keep a record of what changed, what failed, what passed, and why the evaluator was trusted.

Prompt edits / Model switches / RAG updates / Agent releases

FAQ

No. Existing labels help, but they are not a precondition. Argmin AI starts from your task, domain docs, selected traces, and expert corrections during calibration.
Usually selected traces, representative outputs, product constraints, and domain docs. You decide what is shared. We can work under NDA and with tighter infrastructure constraints when needed.
No. Synthetic cases can expand coverage, but the calibration anchor should come from your real traces and your experts' corrections.
No. The evaluator drafts calls first, Argmin AI picks the cases that matter, and experts confirm or correct. Labeling becomes review, not a blank-page grind.
A calibrated eval set, quality rubric, runnable evaluator, core regression cases, and calibration history your team can reuse across AI changes.
Yes. The evaluator creates the quality bar first. After that, Argmin AI can optimize prompts, models, routing, retrieval, and agent architecture without flying blind.