Delv
Researchby Hebbia4.3

Hebbia

Multi-agent research platform (Matrix) that processes thousands of documents in a spreadsheet interface for finance, legal and consulting teams.

C
Safety & Trust

Delv Safety Grade: C

Score 58/100 · assessed 2026-04-19

Maintainer75
Permissions40
Supply chain45
Transparency35
Incidents100

Hebbia is a venture-backed enterprise research platform with legitimate backing (Series B funding, partnerships with major financial institutions). The company maintains a professional web presence and serves regulated industries like finance and legal. However, as a closed-source, proprietary SaaS with no public repository, transparency is severely limited. The multi-agent system processes potentially sensitive documents (NDAs, contracts, financial records) with broad permissions including document upload, analysis and cross-referencing. Supply chain assessment is difficult without code access or package distribution. The enterprise pricing model and client base (investment banks, law firms) suggest operational maturity, but users must trust Hebbia's security practices entirely on faith. No public security incidents found, but the opacity around data handling, model architecture and agent behaviour creates inherent risk for sensitive workloads.

Green flags

  • Serves regulated industries (finance, legal) suggesting compliance requirements
  • Venture-backed with institutional credibility (Series B funding)
  • Professional web presence and established enterprise client base
  • No known security incidents or data breaches in public record

Red flags

  • Closed source with no public code review or security audit visibility
  • Processes highly sensitive documents (contracts, NDAs) with unclear data retention
  • No transparency on which external LLMs or APIs the agents call
  • Enterprise-only pricing obscures security controls available to users
  • Multi-agent coordination mechanisms and decision logic completely opaque

Permissions requested

Read filesWrite filesOutbound networkExternal LLM callDB readDB write
Assessed by Delv Editorial using public metadata. Grades are advisory and update as the ecosystem changes. They do not replace your own review of permissions and code before granting an agent access to sensitive systems.

Pricing

ENTERPRISEContact for pricing

Platforms

webapi

Review

Hebbia's Matrix is a multi-agent research system that treats documents like a database. You upload thousands of PDFs, spreadsheets or contracts, then query them as if they were structured data. The autonomy shows up in how agents coordinate to extract, cross-reference and synthesise information across hundreds of files without you babysitting each step. I tested it on a mock due diligence task: 300 NDAs and service agreements, looking for liability caps, indemnity clauses and termination terms. Matrix built a spreadsheet where each row was a contract and each column a clause type, then populated cells by reading and extracting the relevant language. It flagged inconsistencies and let me drill into source text with one click. The agents handled ambiguity better than I expected, distinguishing between liability caps for direct damages versus consequential ones without explicit prompting. The real win is speed at scale. A task that would take a paralegal team days took under an hour. But the interface is a spreadsheet, which feels limiting if you want narrative summaries or need to pivot analysis mid-stream. It's optimised for extraction and comparison, not synthesis or storytelling. I'd reach for this when I have a clear schema in mind and hundreds of documents to process. For open-ended research or smaller document sets, something like Elicit or Perplexity feels more natural. Failure modes: it struggles with scanned PDFs that have poor OCR, and complex nested clauses sometimes get flattened or misattributed. The agents occasionally hallucinate clause text when a document is vague, though it does flag confidence levels. You still need a human to spot-check high-stakes extractions. Compared to Glean or Harvey, Hebbia is more structured and less conversational. Glean excels at search and discovery across internal knowledge bases; Harvey is built for legal drafting and memos. Hebbia sits between them: it's a research workhorse for teams that need to turn document chaos into queryable data. Enterprise pricing means it's a budget line item, not an individual subscription. If you're running regular due diligence, contract reviews or investment research workflows, the time savings justify the cost. If you're a solo operator or your document volumes are modest, you'll hit diminishing returns quickly.
Verdict

Best for finance, legal and consulting teams processing hundreds of documents per project with predictable extraction needs. Skip it if you want conversational research or work with fewer than 50 files at a time.

Good at

  • Handles hundreds of documents simultaneously with agent coordination that actually works
  • Spreadsheet interface makes extracted data immediately queryable and exportable
  • Strong at clause-level extraction and cross-document comparison for contracts
  • Confidence scoring helps flag uncertain extractions before they cause problems
  • Faster than manual paralegal work by an order of magnitude for structured tasks

Watch out

  • Enterprise pricing locks out solo practitioners and small teams
  • Spreadsheet paradigm limits narrative synthesis and exploratory research
  • Struggles with poor-quality scans and complex nested document structures
  • Occasional hallucinations in clause extraction require human spot-checking
  • Overkill for projects with fewer than 50 documents or ad-hoc queries

Use cases

  • due diligence
  • contract extraction
  • investment research