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.
LLM-as-a-Judge
MCP ready*LLM-as-a-judge that matches your team's rubric and experts
A generic LLM as a judge only says whether an answer looks reasonable. Argmin builds one that scores against your product's actual standard.
From 10-20 of your traces, free:
- Quality rubric
- Custom LLM judge
- Eval set
- Failure report
First evaluator free · No card · Your data stays private
Watch an LLM judge get built from your real traces.
Outcome
What you get
Dataset · aligned
A golden test dataset
A trusted, labeled set built for you during calibration, so you don't spend weeks or months building one yourself. Enough to test the AI agent you are building.
Validation
A judge measured against your experts
0.0%
Safety maintained
0
Edge cases
0
Evaluators
0%
Optimization
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 400 edge cases
- Disagreements routed to experts, then reused
- Clinical safety held at 97.6% 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?
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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
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.
What Argmin builds
From judge prompt to production evaluator
Quality rubric
Criteria, score anchors, examples, and failure modes written for your product, not a generic template.
Calibration cases
Real traces, known failures, edge cases, and expert-selected examples the judge is measured against.
Custom LLM judge
A runnable evaluator that scores outputs and explains every pass or fail decision.
Expert correction loop
Cases where the judge disagrees, is unsure, or hits a risky limit go to review, never a blank page.
Regression workflow
The judge runs before every prompt, model, RAG, tool, or agent change ships.
Why this matters
Anyone can write a judge prompt in an afternoon. The hard part is knowing whether it is right on the cases that matter, not just the easy ones.
Generic judge prompt
- Rewards fluent answers
- Misses domain failures
- No record of why it changed
Expert-tuned Argmin judge
- Scored against your standard
- Surfaces risky and unclear cases
- Versioned rubric and history
Custom rubric · Expert calibration · Regression dataset · Versioned judge
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.
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.
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.