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.
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
A judge measured against your experts
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 a judge experts trust before changing models or prompts
Results
- Scored to match expert review, case by case
- 9-judge evaluator suite over 450 edge cases
- Disagreements routed to experts, then reused
- Clinical safety maintained through changes
What this proves
A generic judge can say whether an answer looks reasonable. Argmin AI answers a harder question: does this answer meet your product's standard, on your cases, judged the way your experts would judge it?
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 LLM judge get built
See how raw traces become an LLM-as-a-judge your team can trust and run before every release.
A judge that scores like your team, not a generic prompt.
Key benefits & features
Calibrated to your experts
The judge is built from your criteria, examples, and expert corrections, not a generic template.
Surfaces the cases that matter
Disagreements, unclear calls, and risky limits go to review, so expert time is spent where it changes the score.
Explained pass or fail
Every decision comes with criterion-level scores and a reason, not just a single number.
Versioned and reusable
Rubric, cases, and corrections are versioned and reused on every prompt, model, RAG, or agent change.
Calibrated / Reviewed / Explained / Versioned
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
An LLM as a judge is only reliable if it is tuned and regression-tested against cases your team trusts. Argmin AI builds that calibration and history, so the score is defensible.
No. Start with traces, examples, docs, and known failures. The labels are built during calibration, as experts confirm or correct the judge's calls.
That is the point. The judge is built from your criteria, examples, policies, and expert decisions, not a generic rubric.
Yes. The output shows criterion-level decisions and the reason for each pass or fail, not just a single score.
Yes. You can check grounding, tool calls, trajectories, and the final answer, not only plain text responses.
No. This evaluates LLM behavior and output quality. It does not script browser UI or replace general software QA.