AI for Law Firms
Closing packets and leases read into fields, and an answer desk over the firm’s own precedent with matter permissions enforced.
What this is for a law firm
A law firm’s product is documents, and much of the repetitive work is moving values between them. The closing packet, the lease, the production, the intake call that has to become a matter. The same date and the same party name get keyed three times before anyone bills an hour.
The work here points a model at the firm’s own material — precedent, prior matters, the standard clauses — and at the recurring documents that get read and retyped. It runs in a Microsoft or AWS account the firm owns, and every request is logged against a named user.
Nothing learns from client files. Documents are read at the moment a question is asked or a packet is processed, and a lawyer signs everything that leaves the firm. What the system produces is a draft or a queue, never a decision.
The permission check is a step, not a setting. An answer returns only what the person asking is already entitled to open.
What gets built
Scoped by a practice area and a document type, which is why these finish. One packet type is a project; the whole file room is not.
Closing packets read into fields
Dates, parties, amounts, legal descriptions and obligations pulled into a structured record, so the same values are not copied by hand into three systems. A person checks the fields beside the original page.
Leases and contracts abstracted
Commencement and expiry, rent and escalation, options, notice periods and who is responsible for what, into a table checked against the original. One lease type at a time, because each has its own language.
An answer desk over the precedent bank
Ask what the firm has done before on a point and get an answer naming the matter and the document. Built over the document management system the firm already runs rather than beside it.
Intake into a matter record
Enquiries arriving by email, form or phone turned into a structured intake record with the conflict details captured, and anything ambiguous flagged rather than filled in.
Call and meeting notes into the file
A client conversation becomes a structured file note written to the matter, reviewed by the fee earner who took the call. Transcription is the commodity part; the schema and the write-back are the build.
Production triage
A large production sorted, classified and ordered so the review starts with what matters most. Every call that counts stays with a person, and the ordering is auditable rather than a black box.
First drafts from approved material
Recurring correspondence and standard documents drafted from the firm’s own precedent, with the source passage shown beside each drafted section.
Somebody who owns it afterwards
Model versions, retrieval quality and spend reviewed on a set cadence once the build is done. A retrieval index degrades quietly as the precedent bank grows.
How the work runs
Decide the matter type
One conveyancing packet or one lease type is a project. Every document the firm holds is not, and starting there is how these stall before anything reaches a fee earner.
Settle the permission position first
Who may see which matter, and what the document management system actually enforces today. This gets fixed before anything is indexed, because an index built over the wrong permissions has to be rebuilt.
Build narrow and measure
Extraction and retrieval are run against documents whose correct answers are already recorded, and the result is a number rather than an impression.
Put it in front of fee earners
Real matters, real questions, with corrections fed back into the build rather than collected in an email thread nobody reads.
What decides whether it survives contact with a matter
Legal material carries boundaries that ordinary document systems do not. These are the parts the work includes, and none of them appear on a competitor’s page.
Matter boundaries are enforced by identity
An assistant returns what the person asking is entitled to see. Ethical walls and matter-level access are configured in the identity system before anything is indexed, not afterwards, because retrieval inherits whatever permissions it was built over.
Every drafted sentence shows its source
Output is grounded in the firm’s own approved material and the passage it drew from is displayed beside it, so checking a draft takes seconds rather than a search. A draft that cannot show its source is not usable work product.
Retrieval quality is the whole product
Whether an answer is right depends on how documents were split, what was indexed, and how often the index is rebuilt as the precedent bank changes. Chunk size has no standard answer and gets tuned against your own material.
The same question can return two answers
Generative output is not deterministic, so the build includes a set of questions with known-correct answers that get re-run on every change. Checking one answer by hand proves nothing.
The index is a copy of the file
Where a retrieval index lives, how long it is kept and who can reach it are decided with the same care as the matter file itself, and written into the retention schedule.
Model versions expire
Deployed models carry published retirement dates and the vendor sets the migration schedule. Anything built against a pinned version has a date on it from the day it ships, and somebody has to own that date.
Who this is for
Real estate, title and closing practices
The same values keyed into three systems on every transaction, at a volume that makes one document type worth building for.
Firms with a deep precedent bank
Decades of prior work that two people know how to navigate and everyone else emails them about.
Practices with heavy intake
Enquiries arriving faster than anybody can open a matter, including evenings and weekends when nobody is in to take them.
Firms already on Microsoft 365
Identity, the document store and much of the licensing are usually in place already, which is most of the groundwork for anything built here.
Frequently asked questions
No. The model reads documents at the moment a question is asked and does not learn from them. On Azure, prompts and completions are not used to train foundation models without your instruction.
Through the identity system, and before anything is indexed. The assistant returns only what the person asking is already entitled to open, and that access gets reviewed as part of the build rather than assumed to be correct.
It drafts from the firm’s own approved material and shows the passage each section came from. What goes out is what a lawyer has read and signed.
In a Microsoft or AWS account the firm owns. CloudCentric builds and administers it; the subscription and the documents stay in the firm’s name.
It reads from wherever the matters already live — SharePoint, iManage, NetDocuments or a file server — rather than asking the firm to move anything. Where a system exposes no interface, that gets established before the work is scoped.
A single document type is normally three to six weeks. An answer desk over an existing estate is similar, and most of that time goes into permissions and retrieval quality rather than the model.
Name the packet that gets retyped
Or the question everybody asks the same partner. Either one is a place to start.