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

Evaluation Builder

An evaluation system for agentic AI you can trust

Argmin AI turns your domain docs and expert-reviewed traces into a calibrated evaluator for every agent change. No golden dataset required upfront.

Build evaluatorFull walkthrough

First evaluator free · No card required

Watch a calibrated evaluator get built from real traces.

Walkthrough

Watch the evaluator build flow

See how a task becomes a runnable evaluator your team can trust before agent changes ship.

Demo · calibration flow7 min to your first evaluator

Validation

Evaluation made optimization safe

Argmin AI Pareto cost reduction chartArgmin AI Pareto cost reduction chart

0.0%

Safety maintained

0

Edge cases

0

Evaluators

0%

Optimization

Internal Case Study: Mental Health Conversational AI

Main challenge: Build the quality bar before reducing cost

Results

  • 9-judge evaluator suite
  • 400-item edge-case stress test
  • Clinical safety maintained at 97.6%
  • 87% cost reduction after quality was measurable

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

Build evals before you develop agentic AI

Create the quality bar first, then change models, prompts, routing, and agents with evidence.

First evaluator free · No card required

Key benefits & features

Calibrated Eval Set

Calibrated Eval Set

Labels are created during calibration from selected traces and expert corrections, not demanded upfront.

Review, Not Grind

Review, Not Grind

Argmin AI drafts evaluator calls and picks the cases; experts confirm, correct, and add reasons.

Core Regression Cases

Core Regression Cases

Keep the cases your AI cannot afford to break across prompts, models, RAG, and agent changes.

Runnable Artifact Bundle

Runnable Artifact Bundle

Get the evaluator, rubric, eval set, score anchors, and calibration history your team can inspect.

No golden set upfront / Expert corrections compound / Test every AI change

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.