TL;DR: Hebbia’s Matrix turns mountains of PDFs, contracts, emails, spreadsheets, and filings into answerable questions with citations—in a spreadsheet-like interface that lets lawyers ask one question across thousands of documents at once. It’s agentic (breaks work into steps), orchestrates multiple models, and is already rolling out at top firms. In 2024 Hebbia raised $130M (Series B) to scale this approach.
Updated: August 2025
Why this matters now
Every firm is drowning in unstructured matter data: VDRs, diligence rooms, email archives, precedent folders, and vendor portals. Traditional search (“Ctrl-F + filters”) wasn’t built for cross-document questions like:
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“Show every instance where the change-of-control clause requires written consent and summarize exceptions.”
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“Across 9,000 pages of exhibits, where did the counterparty cap liability or exclude consequential damages?”
Matrix is built for exactly that: ask a question once, get answers for every document, each with source-linked citations you can verify.
What is Matrix (in plain English)?
Think of Matrix as an AI spreadsheet for answers:
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Rows = documents (contracts, filings, emails, decks, transcripts, spreadsheets).
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Columns = questions you define (“governing law?”, “term?”, “termination for convenience?”).
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Cells = agent outputs (answers) with citations back to the evidence.
Under the hood, Matrix decomposes your prompt into steps, runs specialized agents, and stitches a defensible result you can audit—rather than a one-shot chat reply.
Matrix is model-agnostic and can route tasks to different models (including advanced reasoning models) for the part they’re best at—useful when legal accuracy, math, or long-context reading demands different strengths.
What legal teams actually do with it
Real deployments are showing up in BigLaw and in-house:
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M&A / financing diligence: sweep hundreds of agreements, extract critical terms, and surface outliers for attorney review—report in minutes, not weeks.
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Deal support at scale: large firms are adopting Matrix to accelerate extraction, trend analysis, and negotiation support across internal and external data sources.
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Litigation & investigations: comb productions for smoking-gun language; summarize transcripts; map positions across exhibits with links back to the record.
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Regulatory survey work: ask a normalized set of questions across multi-jurisdictional materials; export a table with citations.
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Knowledge reuse: build “question sets” (columns) once; reuse on every diligence room or matter to ensure consistency.
OpenAI reported that Matrix users have been processing rapidly growing volumes of pages and leaning on long-context reasoning for finance/legal work—evidence that these workflows are moving from pilot to daily use.
Where Matrix fits vs. research copilots
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Matrix = corpus-first. It answers questions across your documents, with table outputs and per-document citations—amazing for diligence, audits, and discovery-style tasks.
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Research copilots = authority-first. They reason over a publisher’s corpus to produce memos with citations.
Most firms end up wanting both: a copilot for black-letter research and Matrix for everything inside the firm’s own (or the counterparty’s) files.
Five capabilities that make Matrix different
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One-to-many Q&A: Ask a single question once; get answers for every document, organized in a table you can export.
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Agentic decomposition: Breaks complex prompts into steps; improves decomposition over time as processes repeat.
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Model orchestration: Routes parts of the job to the model that’s best at them (reasoning, extraction, math).
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Citations by default: Every answer points back to source text so reviewers can audit quickly.
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Proven in legal & finance: Momentum and usage growth across professional workflows signal production-grade performance.
Fast pilot blueprint (10–14 days)
Day 1–2: Scope
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Pick one high-leverage workflow (e.g., change-of-control and assignment extraction across seller contracts).
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Define 8–12 questions (columns) and success metrics (precision on top 200 docs; hours saved).
Day 3–5: Ingest & configure
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Load a contained corpus (one matter room); set user access; turn on logging.
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Draft your question set; test on 20–30 docs; refine phrasing.
Day 6–9: Run at scale
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Execute across the full room; reviewers spot-check citations and tag false positives.
Day 10–14: Report & expand
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Export the table, create a short before/after time study, document playbook steps, and decide the next corpus.
Prompts you can copy
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Clause sweep (diligence):
“For each contract, extract governing law, change-of-control, notice period, assignment, MFN, cap on liability, and indemnity carve-outs. Return a table with one row per document. Include citations (section + quote). Flag missing fields.” -
Regulatory cross-read:
“Across all policy PDFs, answer: (1) does it mention [term], (2) mandatory/optional, (3) cited statute or rule, (4) effective date. Provide a compliance risk note per doc with evidence.” -
Transcript digests:
“For each deposition transcript, list admissions relevant to [issue], contradicting statements, and page-line cites. Summarize in 3 bullets per witness.”
Implementation notes & guardrails
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Governance: Keep access scoped; enable SSO and logs; bind runs to matter numbers.
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Citations-only policy: Require links and quotes in every cell used for downstream work.
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Redaction/PII: Treat uploads like productions—apply the same confidentiality and retention rules.
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Change management: Start with one squad (5–8 power users), publish a question-set library, and run a brown-bag to share wins.
Buying signals (what to ask vendors)
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Document scale & latency: How many docs per run and expected turnaround?
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Model control: Can we select or restrict models used by the orchestrator?
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Citation fidelity: How are quotes anchored to source text?
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Auditability: Per-run logs, prompts, and cell-level provenance.
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Data security: Storage, encryption, retention, and residency options.
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Integrations: DMS, VDR, cloud storage, and export formats.
Why we believe it’s a 2025 breakout
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Capital + momentum: Hebbia raised $130M Series B in 2024 to scale Matrix—strong backers and hiring pace.
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Usage growth: External reporting shows rapid increases in pages processed and adoption in finance/legal workloads.
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Visible legal wins: Public case studies and partnerships signal real-world traction.
Legal teams don’t just need “chat that cites”—they need answers across their own documents that hold up to partner review, opposing counsel, and the court. Matrix delivers exactly that with agentic decomposition, per-document answers, and built-in citations. If your firm lives in VDRs, diligence rooms, or massive productions, this is likely the fastest path to measurable AI ROI in 2025. Start with one high-value question set, prove the time savings, and scale from there.
