Strategy, architecture, build, integration, deployment, governance, and managed operations across CLM, DMS, e-discovery, document automation, knowledge management, and AI.
Strategy, architecture, build, integration, deployment, governance, and managed operations across CLM, DMS, e-discovery, document automation, knowledge management, and AI.
The framework that lets a firm expand AI without creating exposure: an acceptable-use policy, input restrictions, output-review standards, and vendor due diligence, written for your obligations rather than lifted from a template.
Explore → MCP · connect the stackWe connect Claude and other AI to the software a legal team runs on: DMS, CLM, practice management, and Microsoft 365. Using the Model Context Protocol, the AI works inside your systems instead of beside them in a separate window.
Explore → Review · due diligence at scaleAI-assisted review for due diligence, disclosure, and contract analysis, set up so the output is checkable against the source, the privilege risk is contained, and a junior can stand behind every call the system makes.
Explore → Claude · deployed and governedWe stand up Claude for law firms and in-house teams, wired into the systems you already run, governed against privilege and confidentiality rules, and adopted by the people who actually have to use it. Not a browser tab the firm forgets about by month two.
Explore →Roadmaps, technology architecture, build-versus-buy decisions, and the rationale that defends them. We separate the question of what to do from the question of which vendor to use.
Independent shortlisting and scoring against criteria the client owns. CLM, e-discovery, contract AI, document assembly, knowledge management, generative AI, agentic systems.
Custom development where the off-the-shelf product does not fit. API integrations across DMS, matter management, billing, and CRM. Workflow automation in Power Automate, n8n, and bespoke services. RAG pipelines, prompt engineering, and BYOK AI architectures.
Pilot scoping, change management, training, and 30, 60, 90-day adoption tracking. Most deployments fail in the quarter after go-live. That is where we stay.
AI governance frameworks, approved-use catalogues, data classification, retention, residency, and audit. Aligned to DPDP, GDPR, and sector-specific obligations.
Standing legaltech operations on retainer. CLM stewardship, AI prompt and pipeline maintenance, vendor renewal management, and the second-line operating cadence most teams do not have the headcount to run.
For an infrastructure conglomerate operating across six business units and two jurisdictions. Three integrations built in-house. Adoption stable at 87 percent twelve months after go-live.
Spanning six legal workflows for a listed enterprise. Risk taxonomy, training curriculum, and an audit cadence that survives the original team rotating out.
For an in-house team of forty lawyers, on a privacy-first architecture. Eight document workflows automated. Full audit trail. Keys held by the client.
For a regional disputes firm, on a Figma-to-PDF document automation stack with a custom review workflow built for legal. Two hundred and forty templates productionised, with managed maintenance on retainer.
Varies. A strategy engagement might run six to ten weeks. A CLM selection and migration runs four to nine months. A managed-operations retainer is open-ended and reviewed at twelve-month intervals. Each engagement is scoped against a deliverable, a date, and a measurable outcome before we start.
Both. The bench is split between advisors and engineers, and the line between the two is porous. A single engagement can move from a vendor evaluation into a custom integration into managed operations without changing teams.
No. The advisory is fee-for-service. Software licensing relationships are direct between the client and the vendor.