Insights
Field notes on legal intelligence: knowledge graphs, tenant-resident AI, governed agents, and the craft of making a firm's knowledge compound.
Subscribe via RSSWhen a partner leaves, the firm should keep what they knew
Retirement takes decades of a partner's judgment with it. What actually keeps the reasoning inside the firm.
Every closed matter should make the next one cheaper
Why matter exhaust should compound like capital, and what structuring it actually takes.
An AI adoption playbook for firms that bill by the hour
Six moves that survive billable-hour economics, risk committees, and partner skepticism.
Agents can do legal work. Governance decides whether they should.
Scoped permissions, approval gates, and audit trails that make agents deployable at a firm.
The cure for hallucinated citations is grounding, not hope
Grounded retrieval and source-linked answers end fabricated authority. Better models alone do not.
Closing conditions do not slip because lawyers are careless
A live obligations register that keeps conditions, consents, and covenants on schedule.
An AI-assisted due diligence checklist for M&A
Scoping the data room, review tiers, red flags, and verification discipline for real deals.
Drafting with AI where attorneys actually work: in Word
What attorney-grade drafting requires when the AI lives inside the document itself.
Knowledge management is finally becoming infrastructure
When AI reads every matter, knowledge management stops taxing your busiest people.
Your DMS is the system of record. AI should treat it that way.
Permission-aware ingestion and query-time enforcement for iManage and NetDocuments.
AI contract review that never leaves your tenant
Tenant-resident review changes accuracy, privilege posture, and the client consent conversation.
The SOC 2 questions legal AI vendors hope you skip
Type II scope, subprocessors, retention, and the answers that actually matter.
Tenant isolation is the security question that decides the rest
Keys, networks, and query-time control: what single-tenant AI really means in practice.
Private legal AI vs public chatbots: what actually differs
Privilege, retention, grounding, and audit: the four gaps that decide the question.
RAG or knowledge graph? For legal work, the answer is both.
Passage retrieval finds text. Graphs understand relationships. Serious systems use both.
A legal ontology, explained without the jargon
The shared vocabulary of entities and relationships that lets software reason about legal work.
What a legal knowledge graph actually does for a law firm
How matters, documents, and precedent become a queryable institutional memory.