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PRIVATE AI FOR LAW FIRMS

Self-hosted legal AI software inside your firm's tenant

Private artificial intelligence deployed inside the firm's tenant for contract review, contract generation, legal research, deposition summarization, and matter-corpus chat — Harvey AI capability at SMB and mid-market economics. NDA, OCG, ABA Op 512, and bar confidentiality rules satisfied by default. Matter content never leaves the firm's perimeter.
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Matter content, embeddings, audit logs, and chat history stay inside the firm’s tenant. No third-party data processor.

4–6 wks

End-to-end deployment timeline from kickoff to lawyers running review and generation workflows in production.

BYO-LLM

OpenAI, Anthropic, Gemini via the firm’s enterprise contract for non-sensitive work; self-hosted Llama, Mistral, or Qwen for matter-confidential workflows.

What SMB and mid-market law firms get from private legal AI

Six outcomes 30–300 lawyer firms get from a private legal AI deployment that vendor-cloud platforms (Harvey AI, Hebbia, Kira / Litera, Luminance, eBrevia) can’t match on the contractual stack.

Matter file ingestion

PDFs (scanned + native), Word, Excel, contract redlines, deposition transcripts, court filings, OCR'd correspondence, and structured exhibits — every messy real-world legal document the vendors quietly drop chunks of.

Clause-library calibration

The firm's market positions, preferred clauses, and matter-type playbooks loaded into the system. Anomaly detection flags deviations during review; the firm's preferred language surfaces automatically during generation.

Citation enforcement

Every clause suggestion, review flag, redline, and answer links to a source paragraph in the firm's matter corpus. Defensible to the partner, client, GC, post-close auditor, or bar reviewer.

OCG + bar compliance by default

Matter content stays inside the firm's tenant. No third-party data processor. ABA Opinion 512 risk evaluation, OCG AI-use disclosure, NDA AI clauses, UK SRA, Federation, and Law Council rules pass review automatically.

BYO-LLM routing

OpenAI, Anthropic, or Gemini via the firm's enterprise contract for general work; self-hosted Llama, Mistral, or Qwen for matter-confidential workflows. Same UX for the lawyer; different model on the back end based on the matter.

Per-matter access controls

Each matter gets its own access policy mapped to the firm's SSO group membership. Ethical screens, matter walls, and folder-level permissions survive into the AI layer. Every query, retrieval, and response logged for audit.

Why Harvey, Hebbia, and Kira fall short for SMB and mid-market firms

Vendor legal AI platforms (Harvey AI, Hebbia, Kira / Litera, Luminance, eBrevia, Spellbook, LexisNexis Protégé, Casetext CoCounsel) all ship comparable workflow depth for contract review, contract generation, legal research, and matter-corpus chat. The pricing is built for the AmLaw 100 list, and the processing happens in the vendor’s cloud.

That cloud boundary is the deciding factor for SMB and mid-market firms in 2026. Any matter mix that touches engagement-letter restrictions, NDAs with AI-use clauses, outside counsel guidelines, or jurisdiction-specific confidentiality duties (ABA Opinion 512, UK SRA, Canadian Federation of Law Societies, Australian Solicitors’ Conduct Rules) makes vendor-cloud processing a hard sell — or an outright restriction.

A private legal AI deployment inside the firm’s tenant resolves the contractual stack by default. Same workflow capability as the vendors; SMB / mid-market economics; matter content never leaves the firm’s perimeter.

A private RAG deployment is the answer for any corpus that’s too sensitive, too large, or too domain-specific for vendor “chat with files” products. Same chat-with-citations UX, tuned retrieval, BYO embedding and BYO LLM. Documents stay in your tenant, answers come back cited, and your compliance team gets the audit trail they need.

Inside a private legal AI deployment — the 8 capabilities we build

Eight capabilities the firm’s private legal AI stack delivers end-to-end — from matter document ingestion through grounded answers with inline citations to the source paragraph.

1. Matter file ingestion — every document type

PDFs (scanned and native), Word, PowerPoint, Excel, contract redlines, deposition transcripts, court filings, emails, OCR’d correspondence, structured exhibits, and tables with footnotes. The messy real-world inputs vendor RAG quietly drops chunks of — we don’t.

2. Embeddings generated inside the firm's perimeter

Choose the embedding model: OpenAI text-embedding-3 via the firm’s enterprise contract, Cohere or Voyage if licensing fits, or BGE-M3 / E5-Mistral / domain-tuned variants self-hosted inside the firm’s tenant when matter content can’t touch a vendor API. The embedding pass runs entirely behind the firm’s perimeter.

3. Self-hosted vector store sized for the firm's matter corpus

pgvector (when Postgres is the right answer), Qdrant, Weaviate, or Milvus deployed in the firm’s tenant. Tuned for legal-document structure — long-form contracts, multi-section depositions, dense market-terms playbooks, cross-matter citation chains, and matter-type templates.

4. Retrieval calibrated to the firm's playbook

Hybrid search (BM25 + vector), cross-encoder reranker, query rewriting, and multi-query fanout where it pays off. Calibrated against the firm’s market-terms playbook so deviations surface during review and matches surface during generation — not the generic median tuning vendor RAG ships with.

5. Grounded answers with paragraph-level citations

Every clause suggestion, review flag, redline, and chat answer links back to the source paragraph in the firm’s clause library or matter corpus. A suggestion that doesn’t map to a source is flagged as model-generated rather than playbook-sourced; the drafting attorney sees that distinction in the UX. The review burden shifts from “verify everything” to “verify the model-generated suggestions specifically.”

6. BYO-LLM — vendor cloud for general, self-hosted for matter-confidential

Plug in OpenAI, Anthropic, Gemini, or AWS Bedrock via the firm’s enterprise contract for general / non-matter work. Self-hosted Llama, Mistral, or Qwen via vLLM, SGLang, or Ollama for matter-confidential workflows that can’t touch a vendor API. Same UX for the lawyer; different model on the back end based on the matter’s confidentiality posture.

7. Air-gapped, on-prem, or in the firm's existing VPC

The full stack — ingestion, embeddings, vector store, generation, audit logging — runs in the firm’s AWS, AWS GovCloud, Azure, on-prem environment, or a firm-controlled tenant in London, Toronto, or Sydney. For air-gapped or sovereign work, the data path runs without an outbound internet connection. We’ve shipped to sovereign-cloud, classified, and on-prem environments.

8. Per-matter access control and full audit log

Each matter gets its own access policy mapped to the firm’s SSO group membership. Ethical screens, matter walls, and folder-level permissions survive into the AI layer. Every query, retrieval, clause suggestion, and model response is logged for OCG, ABA Op 512, SRA, Federation, and Law Council review. The firm gets a destruction certificate at matter close.

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Talk to a private legal AI expert

Bring us the firm’s matter mix — transactional, commercial, employment, regulated-client — the engagement-letter and OCG constraints the firm operates under, and the workflows that need AI. We’ll walk through the deployment shape that fits, the timeline (typically 4–6 weeks), and what it costs vs Harvey AI annual licensing.

Ask us about

  • Private RAG deployment — ingestion, embeddings, vector store, generation
  • Legal matter files, M&A data rooms, regulatory and policy libraries
  • Hybrid retrieval tuning with recall@k measured on your eval set
  • Self-hosted embeddings and self-hosted LLM serving for sensitive corpora
  • Air-gapped and on-prem deployment for classified or regulated environments
  • Per-corpus access control, audit logs, and citation-enforced generation

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    Need experts to collaborate with for your AI/ML journey? Drop us an email and we will get in touch

    When to choose self-hosted legal AI over vendor cloud

    Harvey AI, Hebbia, Kira / Litera, Luminance, eBrevia, Spellbook, LexisNexis Protégé, and Casetext CoCounsel cover the legal AI workflow surface area. For top-tier firms with AmLaw 100 budgets and no engagement-letter friction, vendor licensing is a reasonable default.

    For SMB and mid-market firms (30–300 lawyers, 50–500 contract matters a year, mixed confidentiality posture), a private legal AI deployment inside the firm’s tenant wins on three dimensions vendor licensing can’t match: contractual-stack compliance by default (no per-matter OCG review, no third-party data processor), SMB / mid-market economics (per-deployment cost below one year of major-tool licensing; cumulative cost gap widens every year), and operational simplicity (the managed engagement handles deployment, calibration, updates, and ongoing ops — no in-house AI ops staff required).

    Frequently asked questions

    Workflow capability is comparable — contract review, contract generation, legal research, deposition summarization, matter-corpus chat all run through the same retrieval + LLM patterns the vendors use under the hood. The structural difference is the trust boundary: vendor platforms process the firm's confidential matter content inside the vendor's cloud; a private deployment runs entirely inside the firm's tenant. That decides OCG review, NDA AI-use clauses, ABA Opinion 512 risk evaluation, and the SMB / mid-market cost comparison.
    Contract review (incoming) — clause extraction, market-terms comparison, anomaly detection against the firm's playbook, disclosure-schedule reconciliation. Contract generation (outgoing) — first-draft generation, clause-library suggestion, redline generation, matter-type templates. Legal research — corpus search across the firm's prior work product, internal memos, and matter-specific document sets. Deposition summarization. Matter chat over the firm's full corpus. Custom workflows for specific practice areas (M&A diligence, personal injury medical chronology, demand-letter generation, etc.).
    Yes — that's the structural point. The deployment lives inside the firm's tenant; matter content never leaves the firm's perimeter; embeddings are generated by a self-hosted embedding model inside the perimeter; no third-party data processor sees client content. Result: no AI data-processor disclosure under OCGs, no AI-restriction violation under NDAs, defensible risk evaluation under ABA Formal Opinion 512, US state-bar advisories (CA, NY, IL, TX, FL, DC), UK SRA, Canadian Federation of Law Societies, and Law Council of Australia guidance.
    The managed private deployment comes in below one year of major-vendor licensing, with the ongoing managed service materially lower than the equivalent vendor annual license. Roughly comparable in year one and dramatically better from year two on because the deployment is already paid while the vendor license keeps renewing. Plus the firm gets the full workflow stack (review + generation + research + matter chat) in one calibrated deployment instead of stacking multiple vendors.
    Yes to both. We deploy into the firm's existing AWS, AWS GovCloud, Azure, or on-prem environment — or a firm-controlled tenant we provision in the firm's preferred region (London, Toronto, Sydney, etc.). For air-gapped or regulated work, we pair the pipeline with self-hosted embedding models and self-hosted LLM serving (vLLM / Ollama on GPUs inside the perimeter). The full data path runs without an outbound internet connection.
    Standard end-to-end timeline is 4–6 weeks. Weeks 1–2: tenant deployment + SSO + audit logging. Weeks 2–3: corpus ingestion (engagement letters, NDAs, M&A agreements, employment, commercial, licensing). Weeks 3–4: retrieval tuning + clause-library / playbook calibration to the firm's market positions. From week 4 forward, lawyers run review and generation workflows in production. No AI ops team required — the managed engagement covers ongoing operation (model updates, connector additions, version upgrades, quarterly playbook reviews). The firm's IT team typically authorizes the tenant on Day 1 and confirms audit-log retention. Everything between is on us.

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    Additional resources

    AI Transformation Workshop

    Half-day strategy workshop to map your corpus, embedding choice, LLM routing, and private RAG deployment shape. Book a workshop →

    AI Strategy Session

    60-minute scoping call. We’ll talk through your corpus, document mix, and sensitivity profile, then sketch the right private RAG deployment. Book a session →

    AI Consultant vs In-House Team

    Honest tradeoffs on bringing a private RAG deployment in-house versus engaging a partner for build + retrieval-tuning + managed retainer. Read the comparison →

    Ready to deploy private AI for the firm?

    45-minute strategy session, no commitment. We’ll scope the deployment for the firm’s matter mix, contractual constraints, and workflow needs — and give a directional read on what it costs vs Harvey AI annual licensing.

    Book a 45-min Assessment
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