Flow AI provides a system for evaluating and enhancing large language model (LLM) applications through open language model judges that align with user-defined criteria. The platform automates the selection and development of specialized models, addressing the inefficiencies and biases of manual evaluations while enabling scalable, cost-effective assessments throughout the AI lifecycle.
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.
PAFounders
Product
Problem
Evaluating and improving large language model (LLM) applications is challenging due to the inefficiencies and biases of manual evaluations. Existing evaluation tools often lack the necessary specialization and meta-evaluation capabilities, leading to misalignments between human and LLM assessments. The reliance on closed-source models also raises privacy concerns and increases costs.
Solution
Flow AI offers a system for evaluating and enhancing LLM applications using open language model judges aligned with user-defined criteria. The platform automates the selection and development of specialized models, providing scalable and cost-effective assessments throughout the AI lifecycle. By employing meta-evaluation techniques, Flow AI maximizes the correlation between human evaluations and LM judge outputs, ensuring accuracy and reliability. The system facilitates the creation of custom evaluators that are fast, controllable, and aligned with specific criteria, enabling AI teams to build superior AI products.
Target Audience
Flow AI targets AI builders and teams developing generative AI products across various domains and use cases, particularly those seeking to improve their LLM applications with advanced evaluation tools and specialized models.
Features
- Open evaluation models specialized in evaluating LLM systems, capable of handling custom criteria and different evaluation paradigms, including pairwise ranking and direct assessment with scoring scales.
- Automatic generation of evaluation criteria to streamline the evaluation process.
- Model merging techniques to develop new LMs that maximize key evaluation metrics without requiring extensive training or GPU resources.
- Meta-evaluation process to maximize the correlation between human evaluations and LM judge outputs.
- User-friendly SDK and compatibility with existing frameworks for easy integration.
- Model and judge version history for tracking changes and ensuring transparency.
- Open-source merging library for greater flexibility and customization.