Try Hebbia
Overview
Hebbia's core insight is that the hard problem in professional services is not answering a question about one document — it is answering the same question about four hundred documents and being able to defend every answer. Its flagship product, Matrix, is built for exactly that: a grid where rows are documents and columns are questions, filled in parallel with the source for each cell.
That interface choice does more work than it appears to. A chat interface forces sequential questioning and hides what was not asked; a matrix makes coverage visible. You can see immediately which contracts have the unusual indemnity clause, which filings mention the contingency, and which cells the system could not answer — and the last of those is often the most valuable output.
The architecture was redesigned in 2025 around specialised sub-agents, separating retrieval from output formatting to improve precision on complex queries. Hebbia raised $130 million in Series B funding and is deployed at asset managers, law firms, banks and Fortune 100 companies — environments where being confidently wrong has consequences measured in litigation rather than user complaints.
Key Features
Matrix Grid Interface
Documents as rows, questions as columns, answers filled in parallel — coverage becomes visible rather than implicit.
Cell-Level Source Attribution
Every answer cites the specific passage it came from, which is the minimum standard for legal and financial work.
Specialised Sub-Agent Architecture
Retrieval and output formatting handled by separate agents, improving precision on complex multi-part queries.
Hundreds of Documents at Once
Built for corpus-scale analysis rather than one-document question answering.
Regulated-Industry Security
Deployed at banks, law firms and Fortune 100 companies, with the controls those environments require.
Explicit Coverage Gaps
Shows which questions it could not answer for which documents, which is frequently the most important finding in diligence.
Pros & Cons
Advantages
- The matrix interface makes coverage visible in a way chat never can
- Cell-level attribution meets professional evidentiary standards
- Genuinely built for corpus scale rather than scaled up from single documents
- Sub-agent redesign improved precision on complex queries
- Proven in the most demanding regulated environments
Disadvantages
- Enterprise pricing well beyond individual practitioners
- Learning curve — the matrix model is unfamiliar at first
- Answer quality depends heavily on question formulation
- Narrow: designed for document-heavy professional work
Pricing Plans
| Plan | Price | Key Features |
|---|---|---|
| Enterprise | Custom | Priced on seats, document volume and deployment scope |
Best Use Cases
Hebbia Excels At:
- Due diligence across large document sets
- Contract review at portfolio scale
- Regulatory and litigation discovery
- Any work where you must answer the same question across hundreds of documents
May Not Be Ideal For:
- Single-document analysis, where a general assistant suffices
- Small firms without enterprise budget
- Work where the difficulty is judgement rather than volume
How It Compares
Hebbia vs Rogo
Hebbia bets the bottleneck is reading across a corpus; Rogo bets it is the whole deal-team task list and produces finished banker deliverables. Diligence-heavy work favours Hebbia; execution and formatting favour Rogo. Several firms run both.
Hebbia vs a general AI assistant
General assistants handle one document at a time and hide what they did not check. Matrix makes coverage explicit across hundreds of documents with per-cell sources — which is the difference between a useful tool and a defensible one.
Final Verdict
Our Recommendation
Hebbia solves a real and expensive problem: reading everything, and proving you did. The matrix interface is a genuinely better abstraction than chat for corpus-scale work, because it makes gaps visible instead of leaving them unasked, and per-cell source attribution meets the evidentiary bar that legal and financial work actually requires. It is expensive and narrow, and it rewards careful question design more than most tools. For diligence, discovery and portfolio-scale contract review, it is the strongest option available.