OpenAI

Automating 90% of finance and legal work with agents

2025-03-20 · 00:00 UTC ·3 min read

Brief

Hebbia's Matrix is a multi-agent AI platform that combines OpenAI models (o3‑mini, o1, GPT‑4o) with a distributed orchestration engine to overcome RAG limits on offline documents, claiming up to 90% automation and 92% accuracy (vs 68% baseline). It processes full documents, routes sub-tasks to specialized models, cites sources, and reports major hours-and-cost savings across banking, private equity, credit, and law workflows.

Source evidence

title: Automating 90% of finance and legal work with agents
contenttype: article
publication: OpenAI
published: 2025-03-20T00:00:00
sourceurl: https://openai.com/index/hebbia

word_count: 690

Hebbia’s deep research automates 90% of finance and legal work, powered by OpenAI

Investors, bankers, consultants, and lawyers spend countless hours combing through market and equity research, virtual data rooms, contracts, and regulatory filings to make high-stakes decisions.

Hebbia(opens in a new window) set out to change that with Matrix, a multi-agent AI platform designed to handle the most complex financial and legal workflows end-to-end.

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.

“We’re not just building a chatbot. We’re creating an agentic operating system that tackles the world’s most complex 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.

The engine overcomes the limitations of RAG and effectively gives OpenAI’s models an “infinite” context window, creating the most accurate deep research agent for high value offline data.

Hebbia with o1 achieves 92% accuracy—up from 68% with out-of-the-box RAG—on a rigorous benchmark spanning both quantitative and qualitative tasks across complex legal and financial documents.

Powered by OpenAI o1’s advanced reasoning and Hebbia’s agent orchestration engine, their Matrix platform:

  • Breaks down complex queries into structured analytical steps
  • Intelligently routes tasks to the best AI model for the job
  • Processes full documents rather than just excerpts
  • Synthesizes answers with full citations for transparency
  • Runs larger LLM processing jobs than any other AI application tool
  • Builds a self improving index that can proactively update users

The result is a platform of AI agents that can draft investment committee memos, interpret intricate legal clauses, and extract multi-step insights from an effectively infinite number of documents.

Hebbia’s approach to multi-agent orchestration—rather than a single-agent chatbot—has delivered significant value to customers:

  • Investment bankers save
    30–40 hours per dealcreating marketing materials, prepping for client meetings, and responding to counterparties. - Private credit teams
    automate the extractionof loan terms and covenants, eliminating days of manual contract review and massive third party spend. - Private equity firms save
    20–30 hours per dealon screening, due diligence, and expert network research. - Law firms reduce credit agreement review time by
    75%, saving**$2,000 per hour**in legal fees.

However, value isn’t only limited to efficiency gains. Firms are also doing things that they never could have done before.

For example, private equity firms and bankers alike are leveraging more historical data than any human** **alone could synthesize by using Matrix’s infinite effective context window. Lawyers have even started to use Matrix in live deals to reference past deal structures and identify new negotiation levers in real time.

Across these use cases, Hebbia’s customers are rapidly increasing their AI adoption since Matrix’s launch. In the last month, legal and finance professionals processed more unstructured data with Hebbia’s platform than the previous 12 months combined.

Hebbia’s multi-agent system allows professionals to use deep research for nuanced questions over the world’s most complex, secure, and offline data. With OpenAI’s o1 for reasoning, GPT‑4o for general processing, and smaller models for targeted tasks, Hebbia can continuously refine how AI handles professional work at scale.

As business AI adoption grows, the real differentiator isn’t model size or speed—it will be how well AI can integrate into real workflows and deliver accurate, defensible insights.

With OpenAI’s models powering Matrix, finance and legal teams are gaining deeper insights, faster workflows, and a competitive edge in decision-making.

“Working with OpenAI allows us to redefine AI tooling in the workplace. Together, we’re introducing agents that achieve the promise of enterprise AI.”