Hebbia

Hebbia is an AI research platform for finance, legal and consulting teams. It answers questions across filings, transcripts and internal documents, builds comparison matrices across companies, and publishes analysis for whole deal teams, with sources attached to each result and connections to market data providers.

Hebbia is an AI research and analysis platform aimed squarely at the finance and professional services world. The homepage positions it as institutional intelligence for investors, bankers, lawyers, consultants and large corporates, with logos from asset managers, investment banks, law firms and Fortune 500 companies. Rather than a general chatbot, the product is organised around the documents and data that these teams already work with: filings, transcripts, presentations, spreadsheets and internal deal files. The core workflow runs through matrices. A matrix is a grid of questions across a set of companies, and each cell holds the sourced answer: revenue, growth, valuation multiples, leverage, credit ratings, shareholder registers, leadership changes or market chatter. Behind each answer sit the source documents, so analysts can check where a figure came from. Hebbia also describes an agent called Max, framed as a new top bucket analyst, and a publishing layer that turns individual analysis into shared, firm-wide work across deal teams and projects. In practice a team would load a project with its documents, connect external data sources, then build matrices or ask questions across the whole set at once. Outputs can be exported into PowerPoint slides and tables, which fits the way pitch books and committee materials are actually assembled. The homepage also gives a notable caveat: some matrix rows state that a figure was not fully extractable from the available filing excerpts, which is a useful signal that the tool reports gaps rather than smoothing over them. It competes with general purpose assistants and with research tools bundled into financial data terminals. Its differentiator is the combination of licensed data connections, document level sourcing and collaborative project structure. Pricing is not published, and access starts with a demo request, so this is an enterprise sale rather than self-serve software. If you are a beginner, start with a demo request and a single project loaded with a handful of documents, then let the team's own files set the pace before scaling.