Task-first evaluator setup
Define success criteria, business rules, and documentation instead of starting with a labeled dataset. The platform uses those inputs to build the first evaluator draft.
Argmin AI turns your rules, docs, and examples into AI evaluations you can run before release—without custom code or an ML team.
Argmin AI is an AI evaluation product for teams that need to check whether an AI feature still works before they ship changes. Its homepage positions the platform around quality evaluation without requiring an ML team or custom evaluation code.
The workflow starts from your task, rules, documents, and a few examples. Argmin AI turns those inputs into an evaluator, helps surface the cases that matter, and calibrates the checks against expert judgments so the same standard can be reused across releases.
Define success criteria, business rules, and documentation instead of starting with a labeled dataset. The platform uses those inputs to build the first evaluator draft.
Argmin AI identifies gaps, edge cases, and risky answers in your real cases so review time goes to the examples that affect agreement most.
Each check becomes a clear rule with a scale and examples for each level, including both generic quality checks and business-specific rules.
The system compares evaluator results with expert answers, highlights disagreements, and tightens the rule based on accepted or rejected corrections.
The calibrated evaluator can run before prompt, model, retrieval, or tool-call changes so regressions are caught before release.
Scores come with criterion-level explanations, and the rubric, cases, and corrections are versioned for reuse across future changes.
Use the platform to check support replies against refund, returns, escalation, or compliance rules before customers see the answer.
Apply it to health, safety, or crisis-related outputs where one bad answer can cause harm and needs review before release.
Use it to verify that a bot or assistant stays inside policy when policies live in docs, drive folders, or internal knowledge bases.
Run the calibrated evaluator before shipping prompt, model, retrieval, or tool changes so regressions are caught in evaluation rather than in production.
Use the same rubric repeatedly for large batches of cases when a human review queue would be too slow or inconsistent.
Argmin AI is positioned for teams that want to evaluate AI workflows before release using their own rules, docs, and examples. The source does not list a minimum team size, but it explicitly says no ML team is required.
The homepage says you start from the task, success criteria, and docs. Argmin AI then syncs your knowledge base, finds relevant cases, and helps calibrate an evaluator that can run before each prompt, model, retrieval, or tool-call change.
The product produces criterion-level scores and reasons for each decision, along with a readiness score and rule-level breakdowns in the examples shown on the homepage.
The source says the calibrated evaluator can be released as an endpoint your code calls before prompt, model, retrieval, or tool-call changes. It also says the rules, cases, and corrections are versioned and reused.
The source does not provide full pricing details. It does state that the first evaluation is free and no credit card is required on the homepage, while the pricing page currently returns a 404.
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