Prototype
- Scattered evaluation examples
- Manual, ad-hoc review
- Releases shipped on intuition
Why this matters
You bring what you have — assistant, prompts, documents, logs, examples. We turn them into a production-grade evaluation system.
Reliable product
- Judges calibrated to your bar
- Reusable golden datasets
- Every release gated by tests
Bring what you have · We build the rest together
From production reality to product trust
Every messy production case gets a matching evaluator
What real users do
Users ask messy questions Rubrics calibrated for real-world inputs Documents are incomplete Grounding evaluators that flag missing context The agent must reason across steps Multi-step reasoning judge
What silently breaks
Plausible but wrong answers Factuality + correctness judge Fix one case — breaks another Regression dataset growing per release RAG retrieves the wrong context Grounding tests for every retrieval change
Bring your existing AI — we build the quality system around it.
Walkthrough
Watch the evaluator build flow
See how a prototype assistant gets a calibrated evaluator, golden dataset, and regression workflow.
Before / after
What changes when you have a real evaluation layer
- Manual spot checksDefined quality bar
- Scattered examplesReusable golden datasets
- Users find the edge casesEdge cases caught first
- Intuition-based releasesEvidence-based releases
- Black-box pass / failExplained pass / fail
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
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
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
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.
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.