Evaluation for LLM Agents

Synthetic data driven evaluation, debugging, and continuous delivery for Agents, RAG, and Generation

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Over 2M LLM evaluation scenarios generated

Over 5M designers are using Clonify

Over 2M LLM evaluation scenarios generated

Ecosystem Connected

Eval + Fine Tuning

Deliver Faster with Clear Behaviors

Okareo mitigates risk for teams developing with LLMs/ML and enhances developer productivity. It offers visibility into model and prompt health across teams, fostering confidence that LLMs are consistently improving and trapping deterioration over time.

20+ Built-in Checks

Unlimited Custom Evaluators

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CI/CD Ready

Continuous Model Improvement From Error Discovery

Okareo automatically generates and curates data for fine-tuning based on discovered errors. Connect and automate the LLM app build cycle from defining behavior to improving the model in production.

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Build Multi-Model Products

Agent, RAG, Multi-Turn Chat, Any LLM Task

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Features

Reliable AI starts during development

Scenario Generation

Generate scenarios that map the boundaries of your model, prompt, function, or chat task.

Prompt Tuning

Fine Tuning

Build trustworthy prompts faster by assessing specific model behaviors of your LLM and tune using behavioral analysis.

Isolate model concerns and synthetically generate the data you need to fix them.

Evaluations

Draw from a library of checks and analytics tuned for specific model types -- Classification, Retrieval, Generation, etc..

Automate in CI

Establish baseline metrics, discover side-effects due to context changes and stabilize end-to-end validation with CI workflows.

Fine Tuning

Isolate model concerns and synthetically generate the data you need to fix the issue across any model provider

Model Health Cards

Share model health internally or with customers through easy to understand model health cards.

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Recent Blogs

How to add LLM Evaluation to your CI workflow

Learn about continuous evaluation and CI/CD LLM evaluation approaches

Optimizing Your RAG - Choose an Embedding Model That Fits Your Data

Explore embedding models based on the type of data retrieval you are building your RAG aronud

Prompting a Driver for Effective Multi-turn Eval

Learn more about task and chat based LLMs and how to evaluate behavior and performance

How to add LLM Evaluation to your CI workflow

Learn about continuous evaluation and CI/CD LLM evaluation approaches

Optimizing Your RAG - Choose an Embedding Model That Fits Your Data

Explore embedding models based on the type of data retrieval you are building your RAG aronud

Prompting a Driver for Effective Multi-turn Eval

Learn more about task and chat based LLMs and how to evaluate behavior and performance

How to add LLM Evaluation to your CI workflow

Learn about continuous evaluation and CI/CD LLM evaluation approaches

Optimizing Your RAG - Choose an Embedding Model That Fits Your Data

Explore embedding models based on the type of data retrieval you are building your RAG aronud

Prompting a Driver for Effective Multi-turn Eval

Learn more about task and chat based LLMs and how to evaluate behavior and performance

How to add LLM Evaluation to your CI workflow

Learn about continuous evaluation and CI/CD LLM evaluation approaches

Optimizing Your RAG - Choose an Embedding Model That Fits Your Data

Explore embedding models based on the type of data retrieval you are building your RAG aronud

Prompting a Driver for Effective Multi-turn Eval

Learn more about task and chat based LLMs and how to evaluate behavior and performance

Future of AI

Start for Free

Join the trusted

Future of AI

Start for Free

Join the trusted

Future of AI

Start for Free