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Build evaluatorFull walkthrough

Evaluation Builder

Build evals without a golden dataset upfront

Argmin AI turns your selected traces and expert corrections into a calibrated eval set — no golden dataset required upfront.

Start calibrationFull walkthrough

First evaluator free · No card required

Start from real traces and keep control of the data.

No golden set needed

See how labels are created during calibration

Argmin AI selects cases, drafts calls, and turns expert corrections into reusable labels without asking for a golden set first.

Demo · calibration flow7 min to your first evaluator

Validation

Real traces first, synthetic cases second

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: Use real quality signals before trusting generated cases

Results

  • Selected traces seed the calibration set
  • Synthetic cases expand edge coverage
  • Expert corrections become reusable labels
  • Every label stays tied to the calibration history

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

Do not wait for a perfect golden set

Build the first reliable eval set while the evaluator is being calibrated.

First evaluator free · No card required

Key benefits & features

No Upfront Golden Set

No Upfront Golden Set

Start with the data you already have: docs, selected traces, known failures, and hypotheses.

Data Stays Directed

Data Stays Directed

Your team decides which traces and docs are used. Calibration is not a blind scrape of production traffic.

Hard Cases First

Hard Cases First

Argmin AI asks for review on cases that expose disagreement, risk, or policy boundaries.

Reusable Ground Truth

Reusable Ground Truth

Corrections become versioned calibration data instead of one-off review work.

Selected traces / Expert corrections / Versioned calibration data

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.