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.
Adversarial Evals
MCP ready*Find the edge cases that may break your AI
We generate the adversarial and edge cases that expose where your LLM or agent fails, then build an evaluator that catches them on every change. An eval tool that finds and measures failures, not a security firewall.
First evaluator free · No card · Your data stays private
See how a prototype becomes a reliably-tested product.
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
Before vs. after the evaluation layer
0.0%
Safety maintained
0
Edge cases
0
Evaluators
0%
Optimization
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.
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Key benefits & features
Define quality
Move from “the new version feels better” to specific, measurable answer-quality dimensions.
Cover the cases users hit
Golden datasets 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
Walkthrough
Watch the evaluator build flow
See how a prototype assistant gets a calibrated evaluator, golden dataset, and regression workflow.
We generate the hard cases, then evaluate against them.
Bring what you have. We build the rest.
Five things we ship from your assets
Quality rubrics
What a good answer means for your product, not for a generic chatbot.
Edge cases
Ambiguous, incomplete, adversarial, and high-risk scenarios mapped and covered.
Golden datasets
Built from real examples, synthetic scenarios, and known failure modes.
Custom LLM judges
Evaluate outputs against your actual criteria and explain their decisions.
Regression tests
Compare versions, detect drift, and gate every release.
What this is
Generic tests pass on the happy path. We generate the adversarial and edge cases that expose where your AI actually fails, then turn them into an evaluator you run on every change.
Prototype
- Scattered evaluation examples
- Manual, ad-hoc review
- Releases shipped on intuition
Reliable product
- Judges calibrated to your bar
- Reusable golden datasets
- Every release gated by tests
Generate the hard cases · Detect the failures · Evaluate every change
From production reality to product trust
Every messy production case gets a matching evaluator
Ship a change
Candidate: Prompt
Warm crisis replies before human handoff.
Immediate handoff → reassurance first, handoff after 2 turns
- crisis_response: "escalate immediately"+ crisis_response: "reassure, then escalate"- handoff_after_turns: 0+ handoff_after_turns: 2
Argmin AI quality gateChecks 120 locked safety scenarios before deploy.120 safety checks before deploy.
Quality gate caught a regression
Immediate crisis escalation: 100% → 82%Release blocked
22 / 120 scenarios failed: human handoff was delayed.
22 / 120 failed: human handoff delayed.
Production protected
People in crisis still receive immediate human handoff.
Business valueAvoids emergency support load and safety exposure.Avoids emergency support load and safety exposure; saves 24h/wk support triage and $4.8k/wk response cost.
Estimated impact avoided
Avoids 24h/wk support triage and $4.8k/wk response cost.
16/wksafety tickets24h/wksupport triage$4.8k/wkresponse cost
Toggle off to compare an unchecked release.
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.
Bring your existing AI, we build the quality system around it.
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
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.