01LAW
Clause search
Find every agreement with a given clause or deviation across the DMS.
LLM.co · Private AI & Custom AI DevelopmentPrivate AI for legal teams
Contract review, clause search and first drafts built on your own precedent, running where privileged documents already live. Ethical walls and matter permissions stay intact.
FIG. — Office, 1957LLM.co
Lawyers cannot paste client files into a public chatbot, and most firms' outside counsel guidelines say so in writing. Private AI removes the question: the model, the documents and the logs stay inside the firm.
We build on the document management system you already use and mirror its matter security, so the AI respects the same walls your people do.
| Systems | iManage, NetDocuments, SharePoint, Outlook |
|---|---|
| Controls | Matter permissions, ethical walls, audit trail |
| Outputs | Issue lists, redlines, summaries, clause tables |
| Deployment | On-premises or private cloud |
What we build
Common legal builds.
02LAW
Contract review
First-pass issue lists against your playbook, with citations to the language.
03LAW
Drafting from precedent
First drafts assembled from the firm's own forms and prior work.
04LAW
Discovery triage
Classify and summarize large productions before human review.
Why firms choose private AI for law firms and legal departments
Many legal AI products send documents to a vendor's cloud and a model provider behind it. For many firms that conflicts with outside counsel guidelines, engagement letters and client security questionnaires that limit where client data may go. Private AI for law firms runs the model inside the firm's own environment, so client files, prompts and outputs stay where privileged material already lives.
The professional rules point the same way. ABA Model Rule 1.6 asks lawyers to make reasonable efforts to prevent unauthorized disclosure of client information, and the comments to Rule 1.1 tie competence to understanding the benefits and risks of relevant technology. ABA Formal Opinion 512 applies those duties to generative AI. A system the firm controls makes those efforts easier to show. Your general counsel decides what the rules require in a given matter.
Workflows that fit legal AI today
The strongest uses involve reading large amounts of the firm's own work product and producing something a lawyer reviews anyway.
- Clause search across the DMS, including deviations from standard language and missing provisions.
- First-pass contract review against the firm's playbook, with each issue tied to the exact language.
- Drafting from precedent, assembled from the firm's forms and prior deals.
- Discovery triage that classifies and summarizes productions before attorney review.
- Diligence tables that pull defined terms, change-of-control and assignment provisions into a single sheet.
Matter security, ethical walls and the audit trail
A firm-wide search tool that ignores matter security creates a new conflict problem. We read permissions directly from iManage, NetDocuments or SharePoint, so a user only gets answers drawn from matters they can already open. Ethical walls apply to the AI the same way they apply to the people.
Every query is logged with the user, the matter, the passages retrieved and the answer. Your risk and IT teams can review usage, respond to client audits and apply the firm's retention schedule to AI interactions.
What to pilot first
Start with one practice group and one document type, such as NDAs, commercial leases or vendor agreements. Load the playbook, score the system on closed matters, and compare its issue lists to what associates actually flagged. This gives partners a measured view of accuracy before anyone uses it on live work.
Avoid starting with legal research that depends on citing outside authority, or anything filed with a court without full attorney review. Those uses carry the highest risk of a confident wrong answer reaching a client or a judge.
How LLM.co builds it
Our custom AI development for legal teams uses open-weight models deployed on-premises or in the firm's own cloud tenancy. The firm owns the code, prompts, playbook logic and evaluation sets. Nothing calls a third-party model API unless the firm approves it for a specific, non-confidential task.
How it works
Four steps, each one reviewed.
01
Connect the DMS
iManage or NetDocuments, with matter-level permissions mirrored.
02
Load the playbook
Your positions and fallbacks become the review standard.
03
Evaluate
Score on closed matters before anyone uses it on live work.
04
Deploy privately
On-premises or in the firm's cloud tenancy.
Questions
Common questions.
Does private AI protect attorney-client privilege?
Keeping documents inside the firm's environment avoids disclosing them to a third-party model provider, which removes one common concern. Whether privilege or work-product protection applies to a given use is a legal question for your firm. We design the system to support whatever position your general counsel takes.
Will the AI respect ethical walls and matter permissions?
Yes. Permissions are read from your document management system, so a user only receives answers drawn from matters they can already open. When a wall changes in the DMS, the AI follows the change. Every query is logged with the user and matter for later review.
Which document management systems do you integrate with?
We connect to iManage, NetDocuments, SharePoint and Outlook through their supported interfaces. Matter metadata, permissions and version history come across with the documents, so search results and drafts reflect the current state of each file.
How does this relate to ABA guidance on generative AI?
ABA Formal Opinion 512 discusses competence, confidentiality, supervision and fees when lawyers use generative AI. A private deployment addresses confidentiality risk from outside providers, and logging supports supervision. Your firm still sets the policies, reviews outputs and decides how the rules apply to its practice.
Can legal AI review contracts against our own playbook?
Yes. Your preferred positions, fallbacks and walk-away points become the review standard. The system produces a first-pass issue list with citations to the contract language, and a lawyer accepts, edits or rejects each item before anything goes to the client.
Can we use private AI for law firms without buying hardware?
Yes. Many firms start with dedicated GPUs in their own cloud tenancy, which keeps data in an account the firm controls. Moving on-premises later is an option once usage is steady. We size both during discovery so the firm can compare them.
What should a firm pilot first?
One practice group and one high-volume document type, such as NDAs or leases, scored on closed matters. Avoid first pilots that cite outside legal authority or produce court filings, since those carry the highest risk if an error slips past review.
Related
More industries.
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