Find risky LLM behavior
before your users do.
Evaluate logs and golden datasets against safety, quality, and compliance rubrics. Get evidence your team can act on.
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Upload logs
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Map columns
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Select rubrics
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Review findings
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Export report
//The Problem
LLM apps fail quietly.
Manual QA misses the pattern.
Manual prompt testing does not scale
A few happy-path checks cannot reveal hallucinations, unsafe advice, refusal failures, or policy-breaking outputs across real user behavior.
Provider behavior changes over time
A model, prompt, or workflow change can turn last month's safe output into this month's regression. Teams need repeatable baselines.
Compliance needs evidence
Dashboards full of raw logs do not answer review questions. Teams need clear findings, scored cases, and reports they can share.
Core question
"Can we prove this AI system is safe, stable, and compliant before customers or auditors discover the gaps?"
RedCriterion starts here
//How It Works
Offline AI evaluation
before runtime enforcement.
RedCriterion evaluates historical logs, test sets, and golden datasets without production routing, API keys, or infrastructure changes.
Map your dataset
Upload CSV or JSONL, then map prompt, response, expected output, and metadata columns.
Run domain rubrics
Evaluate outputs against safety, quality, compliance, hallucination, and policy-specific criteria.
Export evidence
Review case-level findings, recurring failure patterns, and a report your team can retest later.
//Product
RedCriterion is the product.
Evidence is the output.
Find where your LLM application is failing before production risk becomes customer risk.
Not another LLM gateway.
Runtime gateways help route and control live LLM traffic. RedCriterion helps teams evaluate whether AI behavior is safe, compliant, and stable before any runtime integration is required.
Apply for reviewWhat you receive from an evaluation
Pre-launch AI QA
Test your LLM feature before it reaches customers.
Compliance readiness
Generate structured evidence for internal review, customer diligence, or audit preparation.
Risk discovery
Find recurring unsafe, hallucinated, non-compliant, or policy-breaking outputs.
Drift checks
Re-run the same dataset after prompt, model, or provider changes.
Share 100-500 logs.
Get an evidence report.
We are onboarding a small number of teams building real LLM applications. Share anonymized prompt-response logs or a golden dataset for founder-assisted RedCriterion evaluation.
Best fit
- AI SaaS teams
- Healthtech, fintech, or legaltech teams
- Internal AI product teams
- Teams preparing for enterprise review
- Teams unsure whether outputs are safe or stable
Data handling
- No production access required
- No production API keys required
- No proxy deployment required
- Anonymized datasets are accepted
- Founder-assisted review is available before upload
From evidence to enforcement
RedCriterion is the first layer. Over time, TalosRed will expand from offline evaluation into policy generation, drift monitoring, and optional runtime enforcement. The starting point is simple: prove the risk before enforcing the rule.