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AI for law firms

Contract review and research on models the firm controls.

Privileged documents should not be sent to someone else's model. So legal AI here runs on models the firm controls: a custom model fine-tuned on your own markups for contract review, and an open-source model in your cloud for searching past work. Both point to their sources, and a practitioner decides.

// what matters here

What is different in this industry.

01

Client documents stay with the firm

The review model runs on a server you own and the knowledge assistant in your cloud account. No client document is sent to a model provider or used to train anyone else's model.

02

Your positions, not a generic one

The review model learns from your own markups and playbook, so it flags what your firm would flag rather than an average of the market.

03

Every answer has a source

The knowledge assistant links each answer to the memo or precedent behind it, so it can be checked before it is relied on.

04

Practitioners remain responsible

The tools flag, find and suggest. Advice and judgement stay with your practitioners, as your professional obligations require.

// example projects

Priced examples for this service.

Each one is hypothetical, labelled as such, and priced live from our rate card. Open any of them in the estimator and make it yours.

Example projectWeb appAICustom LLM

On-premises contract review

Hypothetical. Not a client, not a result.

// The problem

Reviewing a counterparty's paper against the firm's positions takes a senior reviewer hours, and juniors miss clauses the firm always pushes back on.

// What we would build

A custom model fine-tuned on the firm's own marked-up agreements and playbook, running on a GPU server in the firm's office or data centre so client documents never leave, that flags each clause against the firm's position for the reviewer to decide.

// What is in it

  • A model fine-tuned on your past markups and your written playbook
  • Each clause marked as accepted, flagged or outside your positions, with the reason
  • Suggested fallback language from your own precedents, for the reviewer to accept or reject
  • Measured against agreements your reviewers have already marked, before every release
  • Runs on a GPU server you own, with nothing sent to a model provider

// Stack

  • PyTorch
  • Python
  • Microsoft Word add-in
  • PostgreSQL
  • Lenovo GPU server on premises

// estimate

Build
≈ US$137,000 to US$208,000, delivered within 31 weeksCAD 195,000 to 296,300
Hosting
≈ US$176 a monthCAD 250 a month
Support
≈ US$2,500 a monthCAD 3,565 a month
Hardware
≈ US$47,800 to US$197,000CAD 68,000 to 280,800

Prices in your currency are estimates from today's Bank of Canada rate. All invoicing is in CAD or USD.

AI route
Custom LLM
Model running cost
≈ US$176 a monthCAD 250 a month

Timeline by milestone

Discovery
2.9 to 3.4 weeks
Data and evaluation plan
0.4 weeks
Design approved
2.4 to 3.9 weeks
Core features
1.9 to 2.9 weeks
Full build
1.4 to 1.9 weeks
Training
6.4 to 11.4 weeks
Testing and fixes
0.4 weeks
Evaluation and red-teaming
3.9 to 4.4 weeks
Launch
0.9 to 1.4 weeks
Production
0.9 to 1.4 weeks

Outside our hands, and added to the calendar

Hardware delivery, after it is ordered
2 to 6 weeks

How it is paid

Deposit 20%
CAD 38,760 to 59,020
Discovery 1.5%
CAD 2,907.00 to 4,426.50
Data and evaluation plan 7.9%
CAD 15,310.20 to 23,312.90
Design approved 1.5%
CAD 2,907.00 to 4,426.50
Core features 4.6%
CAD 8,914.80 to 13,574.60
Full build 3.1%
CAD 6,007.80 to 9,148.10
Training 23.9%
CAD 46,318.20 to 70,528.90
Testing and fixes 1.5%
CAD 2,907.00 to 4,426.50
Evaluation and red-teaming 15.9%
CAD 30,814.20 to 46,920.90
Launch 1.5%
CAD 2,907.00 to 4,426.50
Production 8.6%
CAD 16,666.80 to 25,378.60
Holdback, 30 days after launch (10%)
CAD 19,380 to 29,510
GPU time for training and testing, at cost
CAD 1,200. Billed up front, at cost, outside the milestones
GPU server for AI, for the model
CAD 68,000 to 280,800. Billed up front, at cost, outside the milestones

// questions

Questions we get about this.

Will the contract review model invent case law?

It does not cite case law at all. It compares clauses with your playbook and your own precedents and shows where each suggestion came from.

How does the assistant respect ethical walls?

Each search runs with the user's own access rights from the document management system, so a walled matter is invisible to people outside the wall, in the answers as well as the search results.

How many marked-up contracts does it need?

Usually a few hundred agreements of the same type marked by your reviewers, plus the written playbook. We measure on your documents early and tell you if more would help.

// next

Not quite your project?

Tell us what you have in mind. We will come back to you with a range and the questions that would narrow it. Or book a call and talk it through.

Legal AI: contract review models and knowledge assistants | Atheron Network Labs