Business-specific, not generic
Scored against your product's standard, your domain rules, and your workflow — so you can trust a pass, not take a generic “looks reasonable” on faith.
Evaluation Builder
MCP ready*Turn your AI assistant from a promising demo into a reliable product
Rubrics, calibrated test sets, edge cases, and custom judges for regression testing, built around your existing AI.
First evaluator free · No card · Your data stays private
See how a prototype becomes a reliably-tested product.
Your AI demos well, but you can't yet prove it stays reliable on every release.
Why generic judges fail
Generic LLM judges pass the wrong things
A generic judge can tell you an answer sounds reasonable.
It cannot know your task, policies, edge cases, or expert standard until it is calibrated.
Task definition
AnsweredDoes a partial answer pass?
Score anchors
Answered
Edge cases
Covered
Domain model
CoveredBlind to domain synonyms
Release gate
Set
Expert labels
Resolved
Forbidden elements
ResolvedCase 12 vs 41 conflict
Required elements
AnsweredMust it cite its source?
Where trust comes from
A judge grounded in your business and your experts
Built on your experts' vision
Your experts' corrections become the rubric and the labels — it reuses their judgment, it doesn't replace them — so the judge scores the way your team would.
No golden dataset upfront
It cold-starts the judge and a calibrated test set from your traces, plus synthetic and adversarial cases.
Explained, not a black box
Criterion-level scores with a reason for every pass or fail, so you see what broke and why.
A release gate that lasts
Versioned rubric and history; rerun it on every prompt, model, RAG, or agent change.
Outcome
What you get
Evaluator · calibrated
An evaluation system, calibrated
Rubrics, edge cases, and judges tuned to your domain and aligned with your experts. Ready to run on every model, prompt, or agent change so you see what improved and what broke.
Dataset · aligned
A calibrated test set
A lightweight, labeled set built during calibration. You confirm, override, or drop the labels, so it reflects your team's judgment, not the model's. Enough to start testing the AI agent you are building.
Process
How it works
A calibration flow for teams that do not have a clean golden dataset yet.
InputsBring 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.
CasesArgmin 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.
ReviewExperts review, confirm or correct calls
Experts review and correct evaluator calls Argmin AI drafts first, never from a blank page.
CalibrateCorrections 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.
RunTest every AI change
Use the evaluator on prompt edits, model switches, RAG updates, routing changes, and agent releases.
Validation
Before vs. after the evaluation layer
0.0%
Safety maintained while optimizing cost
0%
Cost optimization
0
Edge cases
0
Evaluators
Internal Case Study: Mental Health Conversational AI
Main challenge: Move from manual spot checks to repeatable AI QA
Results
- Quality is defined, not argued
- Edge cases covered before users hit them
- Versions can be compared with evidence
- Failures reproducible from the calibration history
What this proves
Quality stops being a debate. Versions can be compared. Regressions are caught before production. Teams know why an answer passed or failed.
Get the case study PDF: how Argmin AI built the evaluator before optimizing
Enter your email and we'll send the case study PDF.
We process your email to provide access and start the whitepaper delivery flow. You can read our Privacy Policy.
Walkthrough
Watch the evaluator build flow
See how a prototype assistant gets a calibrated evaluator, test set, and regression workflow.
An evaluation system that understands you and your task.
Key benefits & features
Define quality
Move from “the new version feels better” to specific, measurable answer-quality dimensions.
Cover the cases users hit
Calibrated test sets built from real examples, expert cases, synthetic edge cases, and past failures.
Compare with evidence
Custom judges score every release and explain why an answer passed or failed, not just a single number.
Reusable calibration
Every correction is versioned and reused next time you change a prompt, model, RAG pipeline, or agent step.
Quality definition / Real-world coverage / Regression evidence
Your data stays privatePrivate by default
Used only to build and run your evaluator.
We don't train on itNever used to train
Never used to train shared models.
You decide what's sharedYou control sharing
NDA and tighter infra available on request.
1 free run to test1 free test run
No card required. See it work on your data first.
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.