AI that reads contracts, flags risks, and never sleeps.
Law firms and in-house legal teams can eliminate the time-consuming assembly work from contract review, compliance monitoring, and research - without sacrificing accuracy, auditability, or professional accountability.
The challenge
Why AI for Legal?
The legal industry's AI problem isn't a capability problem - it's a trust problem. Every prior attempt at AI in legal failed on the same issue: outputs that couldn't be verified, cited, or staked a professional reputation on. Off-the-shelf tools hallucinated case references. Partners rejected drafts they couldn't trace. The technology moved on; the adoption didn't. The solution isn't to automate legal judgment. It's to automate the work that doesn't require legal judgment - contract parsing, regulatory scanning, research compilation, document cross-referencing - and give lawyers their time back for the work that does. With the right architecture, AI becomes the fastest junior associate in the firm. One that never sleeps, never misses a reference, and always shows its work.
Common pain points
Contract review that doesn't scale with deal flow
A standard NDA takes 45–90 minutes of qualified lawyer time. Complex commercial contracts take days. When deal volume spikes, the bottleneck is always review - and it can't be solved by working longer hours.
Compliance monitoring is reactive, not proactive
Regulations change across multiple jurisdictions with no warning. Tracking what changed, when, and what it means for clients requires dedicated resource - and the answer still arrives days after the update, not hours.
Research that should take minutes takes hours
Finding relevant precedents, statutes, and commentary requires expensive associate time - often for questions with clear, citable answers if you know exactly where to look in a database that covers millions of documents.
Previous AI tools hallucinated and destroyed trust
Off-the-shelf tools generated confident, wrong answers - citing cases that don't exist, misquoting provisions, confusing jurisdictions. One incident erases months of adoption effort. Trust, once broken in a legal context, is very hard to rebuild.
Audit trail requirements block AI adoption
Professional indemnity obligations require that every AI-assisted action is traceable and defensible. Generic AI tools provide no evidence chain. That single gap makes most AI tools commercially unusable in legal practice.
Use cases
Agent workflows for Legal
Real workflows we design, build, and deploy - not theoretical concepts.
Contract Clause Analysis & Risk Scoring
Trigger
New contract uploaded to document management system
Workflow
Parse document into clause-level segments → retrieve semantically similar clauses from the firm's clause library → score each clause against defined risk criteria → generate a risk-annotated summary with flagged clauses and recommended redlines
Outcomes
Risk-scored contract review in minutes, not hours. Lawyers review flagged clauses, not the entire document.
Typical engagement: 72% reduction in first-pass review time on standard commercial contracts.
Systems involved
- Document management system (iManage, NetDocuments)
- Clause library / vector store
- Risk scoring engine
- Redline generation
Human oversight
Human lawyer reviews and approves every flagged clause and redline suggestion before any communication to counterparty.
Multi-Jurisdiction Regulatory Compliance Monitoring
Trigger
Scheduled daily scan or real-time regulatory feed update
Workflow
Ingest updates from official regulatory feeds across relevant jurisdictions → map each change to the firm's internal policy library and client matter register → identify compliance gaps → draft a gap analysis memo with specific action items and affected clients
Outcomes
Proactive compliance gap detection before clients are affected. Regulatory updates turned into actionable memos within hours, not days.
Compliance team shifts from reactive monitoring to proactive client advisory.
Systems involved
- Regulatory feed APIs (EUR-Lex, FCA, SEC, etc.)
- Internal policy library
- Client matter register
- Email / collaboration tools
Human oversight
Every gap analysis memo is reviewed and signed off by a qualified professional before client communication.
Legal Research Assistant
Trigger
Lawyer submits a research query about precedents, statutes, or legal interpretation
Workflow
Semantic search across case law databases and internal knowledge bases → retrieve the most relevant precedents and statutory references → synthesise findings into a structured research summary with inline citations → surface conflicting authorities or jurisdiction-specific nuances
Outcomes
Research time reduced from hours to minutes. Every finding links directly to its source document - verifiable in one click.
Associates spend time on analysis and strategy, not retrieval.
Systems involved
- Legal databases (Westlaw, LexisNexis)
- Internal knowledge base
- Citation engine
- Semantic search index
Human oversight
System flags confidence levels. Low-confidence retrievals are explicitly marked and routed to human verification.
Due Diligence Data Room Analysis
Trigger
M&A or transaction data room made available for review
Workflow
Index the entire data room → map documents against a customisable due diligence checklist → identify missing documents, anomalies, and material issues → generate a structured DD report with issue severity ratings and source references
Outcomes
Data room analysis in days instead of weeks. Material issues surfaced systematically, not by chance.
Deal teams spend time on negotiating issues, not finding them.
Systems involved
- Data room platforms (Intralinks, Datasite)
- Due diligence checklist library
- Document classification engine
- Report generation
Human oversight
All material issues are reviewed and validated by a senior lawyer before inclusion in client-facing DD reports.
Matter Summary & Client Update Generation
Trigger
Scheduled weekly summary or partner requests a client status update
Workflow
Retrieve all activity records, correspondence, and filings for the matter → synthesise into a structured narrative update → flag upcoming deadlines, outstanding actions, and open issues → produce a draft ready for partner review and client dispatch
Outcomes
Client updates drafted in minutes. Partners review and personalise rather than writing from scratch.
Partner time on administrative reporting reduced; client communication frequency and quality both improve.
Systems involved
- Practice management system
- Document management system
- Calendar and deadline tracking
- Email integration
Human oversight
Partner reviews and approves every client update before dispatch. Confidential matter details never leave the firm's environment.
Our approach
How we work in Legal
The legal industry's AI problem isn't a capability problem - it's a trust problem. Every prior attempt at AI in legal failed for the same reason: outputs that couldn't be verified. Lawyers won't stake their professional indemnity on an answer they can't trace. Our response to that constraint is citation-first architecture: every output the system generates must link directly to a source document, case reference, or regulatory provision before it reaches a lawyer. That single principle drives every design decision - from how we structure knowledge bases, to how we set confidence thresholds, to how we design human review flows. The goal isn't to replace legal judgment. It's to make everything that isn't legal judgment happen so fast it's invisible.
Citation-first, always
Every AI output links to a specific source document, clause, or regulatory reference. No claims without evidence. Lawyers verify in seconds, not hours. If the system can't cite a source, it says so rather than guessing.
Your data never leaves your perimeter
Client matter data never leaves the firm's cloud environment. We deploy inside your Azure or AWS tenancy using your own API keys and storage. No third-party training on client data. NDA signed from the first meeting.
AI drafts - lawyers decide
Our systems handle information retrieval, assembly, and first-pass drafting. Every substantive output has a human lawyer in the approval flow. The AI makes the preparation faster; the professional retains the responsibility.
Complete audit trails for PI compliance
Every AI-assisted action is logged: source documents used, confidence scores, timestamps, and any human overrides. Audit evidence is generated automatically, not reconstructed after the fact.
Our non-negotiables
What we never do in Legal AI
Trust is built by constraints as much as capabilities. These are ours.
We never build tools that deliver legal advice to end clients without qualified attorney oversight
We never use client matter data to train or fine-tune third-party models - ever
We never surface documents outside a user's authorised access tier
We never deploy a system without a defined escalation path for low-confidence outputs
We never go live without an evaluation harness testing for hallucination on domain-specific legal questions
Proven results
What we've delivered in this space
Numbers from real engagements - not estimates or benchmarks from someone else's project.
Faster contract review
First-pass AI review with risk scoring on standard commercial contracts, human lawyer sign-off on flagged clauses.
Research query time
Semantic search over case law and internal databases with cited, source-linked answers and confidence indicators.
Audit trail coverage
Every AI-assisted output carries a full evidence chain satisfying PI and regulatory compliance requirements.
Recommended services
What we typically build for Legal teams
Questions we always get
Common questions from Legal teams
Ready to scope a Legal AI project?
Book a 30-minute discovery call. We'll tell you what's feasible, what's realistic, and what to build first — with a clear timeline and cost estimate.