Solution
AI for Large Law Firms: From Tools to Orchestration
At 500+ lawyers, the question is no longer which AI tools to buy — you have shipped them. The question is why the assistant’s time never reaches billing, why precedents never flow back into your RAG index, and why client alerts still take three days.
For large law firms, the highest-ROI AI work is orchestration, not another platform: connecting the AI stack the firm already runs — internal assistants, RAG systems, iManage or NetDocuments, Relativity, Elite 3E or Aderant — into the daily workflows where revenue is made and lost. The four gaps that matter: regulatory alerts matched to live matters, AI usage captured into billing, closed-matter knowledge fed back into the index, and diligence reports drafted from completed review.
The problem
You’ve already solved the hard problem
A firm at your scale has typically built one of the most advanced AI programs in its market: an internal generative AI assistant, a proprietary RAG system over the knowledge base, an enterprise DMS in the cloud, partner-level AI training, a governance group. That was the hard part, and it is done.
What is not done is the connection layer. The assistant’s usage never reaches the billing system. The RAG index retrieves brilliantly but never learns from the deal that closed last week. A regulator publishes a circular and the alert still travels by email and WhatsApp before a client hears anything. A three-week Relativity review ends and a senior associate starts writing the report by hand. Four gaps — all operational, none about AI capability.
The solution
Where automation removes the friction
The four gaps orchestration closes
Regulatory intelligence: new circulars classified by topic, cross-referenced against active matters tagged in your DMS, and turned into a draft client alert in the responsible lawyer’s queue within 15 minutes — instead of 3–4 days of manual triage. AI usage billing: a background timer turns every AI-tool session into a draft time entry against the right matter; industry studies put the leakage this closes at 26% of potential revenue.
Knowledge activation: when a partner marks a matter final, the key negotiated clauses are extracted, tagged by practice and sector, and pushed into your RAG index — so the next team gets them surfaced automatically. Diligence-to-report: when review completes in Relativity, tagged documents are synthesised into a structured report draft with citations, cutting 16–24 hours of senior-associate writing to 4–6 hours of review.
Why orchestration beats buying another platform
Every additional platform adds a silo, a migration, and a per-seat invoice — and still doesn’t connect to the rest of your stack. Orchestration inverts that: the workflows run on the systems you already own, inside your environment (your Azure tenancy or equivalent), under your access controls and ethical walls, with every AI action logged. Your governance committee gets a complete audit trail of AI usage per matter as a by-product, not a separate project.
It also respects how large firms actually adopt technology: practice group by practice group, with measured results. Every workflow has time-tracking built in, so the ROI conversation happens on real data — alerts triggered, hours captured, precedents created, drafting time saved — not vendor projections.
Where to start: one practice group, one workflow
The discovery call maps your specific workflows: which practice generates the most regulatory alerts, which billing system you run, where knowledge is currently getting lost, what your document review volume looks like. Then a proof of concept of one workflow — your choice — built on your environment and integrated with your existing systems. Measure, then scale practice by practice. The investment to start is a 20-minute conversation.
Example workflows we build
- Regulatory alerts: circular → classification → matter matching → draft client alert in 15 minutes
- AI billing capture: tool sessions → draft time entries in Elite 3E / Aderant
- Knowledge activation: matter close → clause extraction → RAG index ingestion
- Diligence-to-report: Relativity review complete → cited report draft
- Per-matter AI usage audit trail for governance
- ROI dashboard: hours captured, alerts shipped, precedents created
The results
The commercial impact
Our approach
From manual to automated
- 01Discovery
Map your workflows: which matters generate the most regulatory alerts? Which billing system? Where is knowledge getting lost? What review volume do you handle?
- 02Proof of concept
A working prototype of one workflow — regulatory alerts, billing capture, or knowledge loop — on your cloud environment, integrated with your DMS, assistant and billing systems.
- 03Measure
Real results tracked from day one: time saved, alerts triggered, billing entries captured, precedents created. Real data, not estimates.
- 04Scale practice by practice
Start with one practice group, prove the ROI, expand firm-wide.
Why a custom build beats off-the-shelf
- Orchestrates the stack you already own — assistant, RAG, DMS, review platform, billing — instead of adding another silo.
- Runs inside your environment under your security model, ethical walls and audit requirements.
- Rolls out practice group by practice group with measured ROI, matching how large firms actually adopt.
- No per-seat tax — fixed-price builds scoped to workflows, not headcount.
Frequently asked questions
We have an innovation team. Why not build this in-house?
Many of our clients have strong internal teams — and keep them focused on the firm’s proprietary AI capabilities, which is where their leverage is. Orchestration is integration work across DMS, billing, review and AI systems: high-value but not differentiating to build, and faster to buy as a fixed-price outcome. We build it on your environment, document it, and your team owns it from day one.
Does this disturb our iManage security model or ethical walls?
No. Every workflow operates through your existing access controls — matter-level permissions and ethical walls included. The orchestration layer reads and writes through the same APIs your systems already expose, inside your boundary, with every action logged.
How does this interact with our AI governance program?
It strengthens it. The billing-capture workflow produces a complete per-matter log of AI prompts, responses and outputs — which is precisely the audit trail governance committees and, increasingly, clients ask for. AI usage across the firm becomes a report, not a survey.
What does a proof of concept look like?
One workflow, one practice group, built on your environment and integrated with your real systems, typically live within 4–8 weeks depending on integration scope. Success metrics are agreed before the build starts, and results are measured on live matters.
Which workflow should we start with?
It depends on your practice mix — that is what discovery maps. As a pattern: firms with heavy regulatory practices start with alerts; firms pushing AI adoption hardest start with billing capture, because it pays for everything else; M&A-heavy firms start with diligence-to-report.
What does it cost?
Engagements are fixed-price and scoped to the outcome. Every engagement is fixed-price with ROI targets agreed up front, backed by our 90-day ROI guarantee. Book a free audit for a clear price and ROI estimate.