Okareo provides a platform for machine learning teams to automate model evaluation and fine-tuning through scenario generation and continuous integration workflows. This enables developers to ensure reliable model performance and improve LLMs and NLP applications by quickly identifying and addressing model behavior issues.
Funding
$4.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Machine learning teams face challenges in consistently evaluating and fine-tuning models, particularly large language models (LLMs), leading to unreliable performance and difficulty in identifying and addressing behavioral issues. Current methods often lack automation and the ability to generate diverse scenarios for comprehensive model assessment.
Solution
Okareo provides a platform designed to automate model evaluation and fine-tuning, enabling machine learning teams to ensure reliable AI performance. The platform facilitates scenario generation to map model boundaries and prompt tuning to build trustworthy prompts by assessing specific model behaviors. It offers a library of checks and analytics tailored for various model types, including classification, retrieval, and generation, and allows for continuous integration (CI) workflows to establish baseline metrics and stabilize end-to-end validation. Okareo also supports fine-tuning by isolating model concerns and synthetically generating data to address issues across different model providers.
Target Audience
Okareo's primary customers are machine learning and AI teams developing with LLMs and other ML models, seeking to mitigate risk, enhance developer productivity, and ensure consistent model improvement.
Features
- Scenario generation to map the boundaries of models, prompts, functions, or chat tasks.
- Prompt tuning for assessing specific model behaviors and building trustworthy prompts.
- Library of checks and analytics tuned for specific model types (Classification, Retrieval, Generation, etc.).
- Automated CI workflows for establishing baseline metrics and stabilizing end-to-end validation.
- Fine-tuning capabilities to isolate model concerns and synthetically generate data for issue resolution.
- Model health cards for sharing model health internally or with customers.
- Unlimited custom evaluators.