Confident AI provides an end-to-end platform for evaluating large language model (LLM) applications. It enables engineering and AI teams to run automated evaluations using a library of pre-built and custom metrics, track regressions across model versions, and benchmark outputs against ground-truth datasets. The platform supports both unit-test-style evaluations during development and continuous monitoring of live production traffic. Teams can use it to detect hallucinations, measure answer relevancy, assess faithfulness in retrieval-augmented generation (RAG) pipelines, and score outputs on safety and toxicity dimensions. Confident AI integrates with the open-source DeepEval testing framework, allowing developers to write evaluation tests in Python and run them in CI/CD pipelines. Results are surfaced in a centralized dashboard where stakeholders can review failing test cases, compare prompt or model variants, and collaborate on quality improvements. The platform is aimed at organizations building LLM-powered products who need systematic, reproducible quality assurance workflows rather than ad-hoc manual review.
Target audience and deployment
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- Self-hosted
- API
Key features
Use cases
- Evaluate LLM outputs automatically
- Test RAG pipelines for accuracy and faithfulness
- Detect regressions across model or prompt versions
- Integrate LLM tests into CI/CD pipelines
- Monitor live production LLM traffic
- Benchmark and compare AI models
Best for
- AI engineers who need to systematically test and validate LLM application quality
- ML teams who need to prevent regressions when iterating on prompts or models
- Platform teams who need to enforce automated quality gates in LLM CI/CD pipelines
- Product teams who need visibility into live LLM performance and safety in production
Integrations
Developer
GitHub, DeepEval
AI models included
OpenAI, Anthropic, Azure OpenAI, Mistral, Llama
Other
LangChain, LlamaIndex