Rather than relying on a single AI model, Matrix orchestrates multiple AI agents in parallel, leveraging OpenAI’s o3‑mini, o1, and GPT‑4o all at once. The result: an “AI associate” that can perform in seconds what used to take entire teams days or weeks, and deep research that can process any amount of offline data to automate 90% of finance and legal work.

Working with early clients, the Hebbia team recognized the key limitation in today’s AI-powered research isn’t the models themselves - it’s information retrieval over the world’s private information.

While web search almost always retrieves answers from online sources, Retrieval-Augmented Generation (RAG)-based tools struggle for offline documents. Oftentimes, answers aren’t explicitly stated in documents, so traditional search falls short.

Hebbia instead built a distributed orchestration engine that enhances accuracy for deep research tasks in finance and law.