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.
Argmin AI + Agent Quality Builder
Know when your AI workflow is ready to ship. No ML team needed.
Describe the workflow. Add docs, rules, and a few examples. Argmin builds a business-specific eval you can run before each release.
Private by default · First eval included · No card
See how a workflow becomes a reusable product-quality check.
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
Start from the product, not ML setup
Bring the agent workflow, docs, policies, and example outputs. Argmin turns them into checks your team can understand.
Business-specific by default
The eval scores what matters in your product: correct answer, right tool use, grounded evidence, tone, format, and policy fit.
Readable verdicts
Every result says what passed, what failed, and why. Product owners can review the verdict without reading eval code.
Run it before each release
Use the same eval when you change a prompt, model, RAG source, tool call, or workflow step.
Product inputs / Business rules / Readable verdicts / Release checks
Workflow
Watch the agent eval get built
See how a product workflow, docs, and examples become a reusable quality check for AI agent changes.
Product-quality checks for AI workflows, built from your examples.
What Argmin builds
From workflow notes to agent-quality checks
Business rules
Criteria, examples, policies, and failure modes written in product language.
Workflow cases
Normal paths, edge cases, risky cases, and known failures your agent should handle.
Reusable eval
A runnable check that scores the answer, tool use, evidence, and outcome.
Review loop
Cases that disagree or sit near a business boundary go to product review.
Release check
Run the eval before prompt, model, RAG, tool, or workflow changes ship.
Why this matters
Your AI feature looked great in the demo. Then a customer hit an edge case and the answer was just wrong, and you found out from them, not from your own tests.
Before a product eval
- A few outputs get skimmed by hand
- Business rules live in docs and memory
- Customers discover the edge cases
With Agent Quality Builder
- Your workflow becomes a reusable eval
- Failures are scored against your rules
- Each verdict explains what broke
Business rules · Workflow checks · No ML team
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
Yes. You describe the agent workflow, add docs or policies, and review examples in product language. Argmin handles the eval structure behind the scenes.
A generic judge scores what sounds reasonable. Argmin builds checks around your workflow, business rules, examples, and corrections, then keeps that history for future releases.
No. Start with a task description, docs, and a few examples. Your review decisions become the evaluation set over time.
A reusable eval for your AI workflow: business-specific criteria, cases to run, pass/fail reasons, and a versioned history of what changed.
Yes. You can check final answers, intermediate steps, tool calls, retrieved evidence, and workflow outcomes.
You can create the first eval before committing. After that, usage is credit-based.