AI that respects privilege, confidentiality and your duty of competence
Legal teams are being asked to move faster on review, drafting and intake while carrying professional-responsibility duties that no vendor demo addresses. The constraint is not capability — it is provenance and confidentiality.
The pressure
What is forcing the pace in legal
- Clients are pushing back on hours billed for work the market now assumes is machine-assisted, while outside counsel guidelines increasingly regulate AI use directly.
- In-house departments face rising matter volume against flat headcount, with intake and triage absorbing senior attorney time.
- Discovery and contract volumes have outgrown linear review, but sampling-based defensibility arguments must still hold up.
The constraint
What the work has to satisfy
- Attorney-client privilege and work product must be preserved end to end — including in vendor logging, retention and training data.
- Professional-responsibility duties of competence, supervision and candor mean a human attorney remains accountable for every AI-assisted output, with supervision documented.
- Citation integrity is non-negotiable: every proposition needs a retrievable source, and hallucinated authority is a sanctionable event, not a bug.
- Client confidentiality and outside counsel guidelines frequently prohibit third-party model training and cross-matter data mixing.
Where we start
The workflows that pay first
High volume, high variance, and far enough from the consequential decision that automation is defensible.
Contract review and first-pass markup
Playbook-driven redlining with a deviation report, leaving the negotiation position with the attorney.
Matter intake and triage
Structured intake that classifies, routes and drafts the opening summary, cutting senior attorney time on scoping.
Research and citation verification
Retrieval restricted to verified sources, with a citation-check gate that blocks any output containing unresolvable authority.
Discovery prioritization
Responsiveness and privilege pre-screening that ranks documents for human review rather than replacing it.
How the practices apply
Our five practices, read for legal
- Navigate
- Separate the work that can be machine-assisted from the work that carries a professional duty a model cannot hold.
- Deploy
- Build inside matter-segregated environments, with privilege-preserving logging agreed with your GC before the first prompt.
- Amplify
- Place technologists and legal operations talent who understand privilege boundaries as a design constraint.
- Measure
- Track realization, cycle time and review cost per matter, not model metrics nobody bills against.
- Sustain
- Citation-integrity benchmarks and supervision records that satisfy a bar inquiry or a client audit.
Proof
Legal engagements
Situation, approach, what we built, and the constraint the solution had to satisfy.
Roles we deploy
Who shows up
- Legal Technology Architect
- Forward Deployed AI Engineer
- Legal Operations Analyst
- AI Governance Analyst
Next step
Book an AI readiness review for legal
Four weeks, one ranked opportunity register, and a governance structure your risk function can live with.
Start the conversation