Automating 90% of finance and legal tasks using AI agents

If you work in finance, law, or any field drowning in documents, Hebbia's new deep research capability claims to take roughly 90% of that manual workload off your plate. Built on OpenAI's models, the feature promises to handle the hours-long grind of reading, cross-referencing, and summarizing that has long defined these professions. This review explains what Hebbia's deep research actually does, who benefits most, and where you should keep your expectations grounded.

What Is Hebbia?

Hebbia is an AI platform designed specifically for knowledge-heavy industries such as investment banking, private equity, asset management, and corporate law. Unlike general-purpose chatbots, it is built around a simple reality of these jobs: professionals spend enormous amounts of time digging through pitch decks, contracts, filings, and internal reports to find a handful of relevant facts.

The company's core product, often referred to as Matrix, lets users ask questions across entire libraries of documents at once. Instead of opening fifty PDFs one by one, you type a question and Hebbia returns answers with citations pointing back to the exact pages it drew from. That traceability matters in regulated industries, where an answer you cannot verify is an answer you cannot use.

What the Deep Research Feature Does

The new deep research capability, powered by OpenAI's models, extends this idea from quick lookups to full multi-step investigations. Rather than answering a single question, the system can take on a broader task — for example, analyzing a company's competitive position across a stack of market reports, or reviewing a set of contracts for problematic clauses — and work through it methodically.

In practice, this means the AI plans its own research steps, searches across your documents, gathers what it finds, and assembles a structured output such as a memo, table, or summary. Hebbia claims this approach automates around 90% of the work that analysts and associates traditionally perform manually in these situations. That figure comes from the company itself, so treat it as a marketing benchmark rather than an independent measurement — though it reflects a genuine shift in how much of the process the tool can carry end to end.

Why the OpenAI Partnership Matters

Under the hood, Hebbia relies on OpenAI's models, which gives it strong reasoning and language capabilities for interpreting dense, technical documents. But the platform's real differentiator is not the raw model — everyone has access to those. It is how Hebbia structures the work around the model: breaking large tasks into steps, keeping track of sources, and presenting results in the formats professionals actually need, like comparison tables and annotated excerpts.

For finance and legal users, the difference between a generic chatbot and a purpose-built tool often comes down to trust. Hebbia's citation-first approach means every claim in an output can be traced to a source document, which is essential when your work gets reviewed by a partner, a compliance team, or a regulator.

Where It Fits Into Daily Work

Typical use cases include:

• Due diligence: sweeping through data-room documents to surface risks, inconsistencies, and key deal terms.
• Contract review: flagging unusual clauses or missing provisions across large contract sets.
• Market and competitor analysis: pulling relevant data points from earnings materials, filings, and research reports into a single view.
• Memo drafting: producing first-draft summaries that a human then refines and signs off on.

The pattern in all of these is the same: the AI does the reading and the gathering, while the human does the judgment. That division of labor is where the 90% figure becomes believable — not because the AI replaces the professional, but because it removes most of the mechanical steps between receiving a task and delivering a draft.

Limitations Worth Knowing

A few caveats are worth keeping in mind. First, the 90% automation claim applies to specific kinds of document-heavy tasks; strategic thinking, client relationships, and final judgment calls remain firmly human territory. Second, like any AI system, outputs should be verified — Hebbia's citations make verification faster, but they do not eliminate the need for it. Third, Hebbia is an enterprise-focused product aimed at firms rather than individual consumers, and pricing details are not publicly listed in a simple self-serve format, so access typically involves contacting the company.

It is also worth noting that the broader "deep research" category is heating up, with several AI vendors releasing their own long-form research agents. Hebbia's edge is vertical: it is tuned for the document types, workflows, and accuracy standards of finance and law, rather than trying to serve everyone at once.

Who Should Use It / Who Should Skip It

Use it if: you work at a financial institution, law firm, or corporate team where teams routinely spend days reading and synthesizing large document sets. The time savings on due diligence, contract review, and research memos can be substantial, and the citation-based outputs fit naturally into professional review processes.

Skip it if: you are an individual user looking for a general-purpose AI assistant for everyday questions, or if your work involves mostly short documents and quick lookups. In those cases, a standard chatbot or a general research tool will likely cover your needs at a lower cost and with less setup.

The bottom line: Hebbia's deep research is a serious step toward automating the document grind in finance and law. It will not replace the professionals who use it, but it may well redefine what a productive analyst or associate looks like.

Meta description: Hebbia's deep research, powered by OpenAI, automates up to 90% of document-heavy finance and legal work. Here's how it works and who it's for.

Tags: Hebbia, deep research, OpenAI, legal tech, fintech AI

Featured image: An abstract, friendly illustration of layered documents transforming into a clean, glowing summary panel, rendered in soft blues and warm neutrals, with no people, logos, or text.