Without infrastructure
- Only “correct” gets measured
- Edge cases live in someone's head
- Every release argues what's better
What you get
Define what “good” means. Generate the right test cases. Build judges that reflect your product. Regression-test every change before production.
With Argmin AI
- Reusable rubrics, datasets, judges
- Edge cases tested before they ship
- Version comparisons show what changed
Define · Generate · Build · Regression-test
Each dimension of quality, infrastructure for it
“Correct” is not enough. We build the quality infrastructure
Correctness & substance
Accurate Correctness rubric with explanations Grounded in the provided context Grounding tests against the source Complete enough for the user's task Completeness checks per request type
Safety, policy & domain
Safe + compliant + on-brand Safety, compliance, and tone evaluators Aware of uncertainty Calibrated confidence judges Aligned with domain-specific rules Domain-specific quality bar
Generic evals tell you if an answer looks reasonable — we tell you if it works for your product.
Walkthrough
Watch the evaluator build flow
From existing AI agent and prompts to a calibrated, runnable evaluation system.
Validation
Custom judges, calibrated to your standard
0.0%
Safety maintained
0
Edge cases
0
Evaluators
0%
Optimization
Internal Case Study: Mental Health Conversational AI
Main challenge: Build the quality infrastructure before changing models or cost
Results
- 9-judge evaluator suite
- 400-item edge-case stress test
- Clinical safety maintained at 97.6%
- 87% cost reduction after quality was measurable
Get the case study PDF: how Argmin AI built the evaluator before optimizing
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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.
Key benefits & features
Map your task
Analyze the assistant, users, workflows, and risks. Pinpoint where failure is most costly before scoring anything.
Calibrated judges
Task-specific LLM-as-a-judge prompts, calibrated against your team’s quality bar with reusable golden datasets.
Gate every release
Compare versions, catch regressions, and protect quality across prompts, models, RAG, and agents.
Decision history
Every correction, calibration, and version comparison is retained — quality becomes inspectable infrastructure.
Map / Calibrate / Gate / Inspect
How it works
From existing AI assets to a calibrated quality layer
Define the quality rubric
Translate product expectations into structured dimensions: correctness, grounding, tone, domain.
Generate edge cases
Surface the cases manual testing misses — messy, conflicting, multi-step, policy-sensitive.
Build the golden dataset
A reusable dataset from real examples, expert cases, synthetic edges, and past failures.
Calibrate the judge
Task-specific LLM judges, calibrated against your team's quality bar with structured reasoning.
Regression test every release
Compare prompt, model, RAG, agent, and guardrail changes before they ship.
Use cases
What you can evaluate
01/12
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.