In the demo
- Hand-picked examples pass
- Manual spot checks
- Quality “feels right”
Why this matters
Your AI assistant worked in the demo. Production is where you find out whether every update makes it better or worse.
In production
- Prompt updates change behavior
- Models drift, RAG breaks
- Edge cases multiply silently
Quality rubrics · Edge cases · Golden datasets · LLM-as-a-judge workflows
From failure to fix
Every production failure gets a matching evaluator
When you change the system
Prompt updates change behavior Behavioral rubrics scored per prompt change Model upgrades affect reasoning Custom LLM judge calibrated to your reasoning bar RAG changes break grounding Grounding tests for every retrieval change
When it runs in production
Tool calls fail in unexpected ways Tool-use evaluators verifying args and outcomes Context compression removes important details Completeness checks against the full source Edge cases multiply as usage grows Regression dataset that grows with each new failure
Bring your agent, prompts, and examples — Argmin AI turns them into a structured evaluation layer.
Walkthrough
Watch the evaluator build flow
See how a task becomes a runnable evaluator your team can trust before agent changes ship.
Validation
From demo to production-grade, with evidence
0.0%
Safety maintained
0
Edge cases
0
Evaluators
0%
Optimization
Internal Case Study: Mental Health Conversational AI
Main challenge: Build the quality bar before optimizing cost or model
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
Quality definition
Translate vague “is this good?” into specific dimensions: correctness, grounding, completeness, tone, format, domain rules.
Edge cases & failure modes
Find ambiguous requests, conflicting documents, multi-step reasoning traps, and high-risk scenarios — then cover them with test cases.
Custom LLM judge
Tailored to your assistant, domain, and quality bar. Output is not just a score — it explains what passed, what failed, and why.
Regression testing
Test every meaningful change before release: prompts, models, RAG, context compression, tools, agent workflows, guardrails.
Quality definition / Edge cases / Custom judge / Regression tests
What we build together
From your raw materials to a runnable evaluation system
Quality definition
Translate product expectations into measurable evaluation criteria — not vague “is it good?” scoring.
Edge cases & failure modes
Map ambiguous requests, conflicting docs, multi-step traps, and high-risk scenarios. Then test for them.
Golden evaluation dataset
A reusable dataset built from real examples, synthetic edge cases, and regressions from past failures.
Custom LLM-as-a-judge
A judge calibrated to your assistant, domain, and quality bar. Explains what passed, what failed, and why.
Regression testing workflow
Test every prompt, model, RAG, tool, and agent change before release. Ship with evidence, not intuition.
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.