Autoblocks AI provides a cloud-based platform for product teams to collaboratively test and evaluate their generative AI language models using expert feedback and user data. The platform enhances model accuracy by curating high-quality test datasets and aligning automated evaluation metrics with human preferences, ensuring reliable and effective AI product performance.
Funding
$2M 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
Generative AI language models often lack accuracy and reliability due to insufficient testing and evaluation, leading to unpredictable outputs and hindering the development of effective AI products. Traditional testing methods struggle to align automated evaluation metrics with human preferences and expert feedback, resulting in models that don't meet user expectations.
Solution
Autoblocks AI offers a cloud-based platform designed for product teams to collaboratively test and evaluate their generative AI language models, ensuring enhanced accuracy and reliability. The platform facilitates the curation of high-quality test datasets and aligns automated evaluation metrics with expert feedback and user data, bridging the gap between model performance and real-world expectations. By providing tools for experimentation, monitoring, and debugging, Autoblocks AI enables teams to identify and address issues, optimize prompts, and improve the overall quality of their AI-powered products. The platform integrates seamlessly with existing codebases and frameworks, allowing for flexible testing and evaluation throughout the development lifecycle.
Target Audience
The primary target audience includes AI product teams, particularly those working on generative AI language models, who need a collaborative platform for testing, evaluating, and improving the accuracy and reliability of their AI-powered products.
Features
- Collaborative testing and evaluation platform for generative AI language models
- Tools for curating high-quality test datasets based on user feedback and online evaluations
- Experimentation environment with SDKs to surface any part of the pipeline into a UI
- Alignment of automated evaluation metrics with human preferences through expert feedback
- Integration with any codebase and framework via flexible SDKs for tracing events, testing app behavior, managing prompts, configs, and custom models
- Monitoring and guardrails to ensure a safe and trustworthy user experience
- Debugging tools to identify the root cause of bugs and rapidly prototype solutions
- AI product analytics to connect AI product state to user outcomes and uncover opportunities for improvement
- Prompt management to enable prompt collaboration
- RAG (Retrieval-Augmented Generation) and context engineering to optimize context pipelines