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.
Your AI worked in the demo. In production, every update can quietly make it worse, and you find out from users.
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
From demo to production-grade, with evidence
0.0%
Safety maintained while optimizing cost
0%
Cost optimization
0
Edge cases
0
Evaluators
Internal Case Study: Mental Health Conversational AI
Main challenge: Build the quality bar before optimizing cost or model
Results
- 9-judge evaluator suite
- 450-item edge-case stress test
- Clinical safety maintained
- 87% cost reduction after quality was measurable
What this proves
The evaluator is not a prompt pasted into a spreadsheet. It is a calibrated quality system built before optimization decisions affect the product.
Get the case study PDF: how Argmin AI built the evaluator before optimizing
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Walkthrough
Watch the evaluator build flow
See how a task becomes a runnable evaluator your team can trust before agent changes ship.
Evaluation that understands your product, not just your model.
Key benefits & features
Quality definition
Translate vague “is this good?” into specific dimensions: correctness, grounding, completeness, tone, format, domain rules.
Edge cases & failure modes
Find ambiguous requests, conflicting documents, multi-step reasoning traps, and high-risk scenarios, then cover them with test cases.
Custom LLM judge
Tailored to your assistant, domain, and quality bar. Output is not just a score, it explains what passed, what failed, and why.
Regression testing
Test every meaningful change before release: prompts, models, RAG, context compression, tools, agent workflows, guardrails.
Quality definition / Edge cases / Custom judge / Regression tests
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.