AI for legal teams that respects privilege and isolation.
AI that reads like a careful junior associate · without crossing matters.
Legal AI fails when it confuses one matter with another. A model that learns from one client's documents and applies those patterns to a different matter is a privilege violation waiting to happen. We build for isolation by default.
Our legal work trains a separate model per matter, on the documents that matter has authorized, behind the access controls the firm already enforces. Every suggestion carries a citation back to the source clause, and every human edit trains the next version.
From contract review and clause extraction to e-discovery triage and compliance monitoring, the wins are measurable: faster turnaround, higher accuracy, more time for the work only a lawyer can do.
Four places to start.
Each of these has shipped in production for a real legal customer. Pick the closest match to your situation.
Contract review
The AI reads the contract, flags unusual clauses, drafts a summary, and highlights what a senior associate should look at. The associate always makes the final call.
Clause extraction and playbook matching
Encode the firm's own playbook as rules, then run every new contract through it. The system tells the associate exactly where the contract deviates from house style.
E-discovery and document review
First-pass relevance review on large document sets, with explanations for every coding decision. Senior reviewers spend their time on the edge cases, not the obvious ones.
Compliance monitoring
Watch regulatory changes, internal policies, and external communications for risks before they become findings.
Three steps. No surprises.
The same shape as every Fornext engagement · from legal to banking to restaurants. You see real progress in weeks, not quarters.
Encode the playbook, not just the documents
We sit with senior partners and translate their judgment into rules the model can apply. The result is AI that thinks like your firm, not like a generic large language model.
Isolated training, isolated inference
Every matter gets its own model. Data from matter A never trains the model that serves matter B · ever. Access controls mirror your existing matter permissions.
Side-by-side review, citations always
The associate sees the original document and the AI's annotations together. Every suggestion links back to the source. Edits flow back into training so the system gets measurably better.
The questions legal leaders ask us.
These come up on every legal discovery call. The answers are real, not sales-deck answers.
Have a different question? Ask usCan the model ever confuse documents across matters?
No. We train a separate model per matter, with access controls that mirror your existing matter permissions. Cross-matter learning is structurally impossible · not just policy.
How accurate are the clause annotations?
On engagements where we have encoded the firm's playbook, accuracy runs above 96% on common clause types. We publish the numbers per engagement, including the failure modes.
What happens to the training data afterwards?
You own it. When the engagement ends, we either transfer the model and the training data to your environment, or destroy them per your data-retention policy. The choice is yours and documented in the MSA.
Can we keep using our existing document management system?
Yes. We integrate with iManage, NetDocuments, SharePoint, and most other DMSs. The AI sits next to your existing workflow, not on top of it.
AI that earns the trust of clinicians, regulators, and patients.
Real-time intelligence on the factory floor · without ripping out what works.
Real-time decisions · with explanations regulators accept.
Voice agents that pick up the phone · in any language, 24/7.
Talk to us about your legal project.
One short call. We'll tell you what we'd do, what it would take, and what it would cost · even if we end up pointing you somewhere else.