The Complete LLM Evaluation Platform
Automated test suites, hallucination detection, adversarial red-teaming, and CI/CD gates - in one platform. Built for teams who need more than vibes-based AI quality assurance.
Platform Capabilities
Every dimension of LLM quality, measured systematically. No manual review steps, no guesswork about whether a change made things better or worse.
Automated Test Suites
Define evaluation datasets with expected outputs, similarity thresholds, and custom scoring rubrics. Run automatically on every model version, compare across runs, and catch regressions before they ship.
Hallucination Detection
Multi-layer detection covering factual confabulation, unsupported claims, citation fabrication, and numerical inconsistency. Catches hallucinations that basic output checks miss entirely.
Red-Teaming Engine
Automated adversarial probing for prompt injection, jailbreaks, policy violations, and PII leakage. 150+ attack vectors, with customizable payloads for your specific deployment context.
CI/CD Integration
Native GitHub Actions, GitLab CI, CircleCI, and Jenkins plugins. Define quality thresholds as code. Block merges and deployments automatically when evaluation scores fall below acceptable levels.
Metrics Dashboard
One dashboard showing accuracy, coherence, tone adherence, response latency, and cost per run across all your model runs. Full version history with side-by-side comparison across model versions and providers.
Universal Model Support
Works with OpenAI GPT-4, Claude, Mistral, Llama 3, Gemini, and any custom fine-tuned model via REST API. Evaluate multiple providers head-to-head with identical test suites.
Define Quality. Measure It. Enforce It.
Quality requirements encoded as data, not documentation. Build evaluation datasets from production logs, synthetic generation, or our library of 200+ pre-built templates — then run them on every deploy automatically.
- Semantic similarity scoring with configurable thresholds (cosine, BLEU, BERTScore, custom)
- Multi-criteria rubrics: accuracy, completeness, tone, format compliance, safety
- Regression detection with statistical significance testing
- Dataset versioning and diff tracking across evaluation runs
Catch What Output Checks Miss
Checking output format says nothing about whether the claims inside are true. Our multi-layer detection pipeline verifies outputs against source documents and factual constraints — not just format templates. Surface-level validation can't catch this.
- Grounding verification against provided context documents
- Citation integrity checking for research and RAG applications
- Numerical and date consistency validation
- Confidence calibration scoring - flagging overconfident incorrect outputs
Find the Vulnerabilities Before Attackers Do
Automated adversarial testing that goes beyond simple banned-word filters. Our red-teaming engine generates realistic attack scenarios based on your deployment context and user base.
- 150+ attack vector library: prompt injection, jailbreaks, role-play exploits, PII extraction
- Industry-specific scenario packs: customer service, legal, medical, financial
- Custom payload generation tailored to your system prompt and context
- Severity scoring and remediation guidance for every identified vulnerability
Works Where You Already Work
Confident AI plugs into your existing development workflow. No new tools to learn. No separate dashboard to check.
Install the SDK
pip install confident-ai
Available for Python, Node.js, and Go. Or use our REST API directly.
Add to Your Pipeline
Drop our GitHub Action or GitLab CI snippet into your workflow file. Configure your quality thresholds in confidentai.yml.
Automatic Quality Gates
Every pull request and deployment runs your evaluation suite. PRs that miss quality thresholds are blocked. Your team gets detailed failure reports - not just a red X.
Start Evaluating Your Models Today
Developer plan is free to start — no credit card, up and running in under 10 minutes. Need something more complex? Our engineering team is worth talking to before you build evaluation infrastructure from scratch.