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LLM Observability
Monitor, Trace, A/B Test, and get real-time production performance insights with best-in-class LLM Evaluations.
Not Your Typical LLM Observability Solution
Confident AI's observability is evaluation-first, meaning you'll be able to automatically detect unsatisfactory responses with unparalleled accuracy using best-in-class LLM evaluations powered by DeepEval.
Easily monitor and A/B test LLM applications
Confident AI offers advanced logging for anyone to recreate scenarios in which monitored LLM responses were generated in, and allows you to easily A/B test different hyper-parameters for your LLM system in production (e.g. prompt template, models).
Setting up monitoring typically takes less than 10 minutes of your time, and integrates with any systems via API calls through DeepEval.
Real-time LLM evaluation powered by DeepEval
Automatically grade incoming LLM response you're monitoring on Confident AI. These evaluations covers any use case, LLM systems (e.g. RAG, Chatbots, Agents), and can be enabled by a few clicks. Custom evaluation LLMs available on request.
This allows you to safeguard against unwanted risks, and to be alerted of bad responses that might have been exposed to end users.
Flexible LLM tracing to debug any LLM application
From retrieval data to accessing different APIs, Confident AI allows you to pinpoint where things have gone wrong through detailed tracing.
One line tracing integrations are available for 5+ LLM frameworks such as LangChain, LlamaIndex, and custom tracing can be easily integrated to support LLM applications that are not built with any frameworks.
Collect user feedback to identify unsatisfactory interactions
Confident AI allows your team to either collect feedback from human annotators on the platform, OR directly from end users interacting with your LLM application via API calls.
When combined with real-time evaluations, your team can easily identify the scenarios in which your LLM underperforms.
Don't believe us? See it for yourself.
