Integration
iManage & NetDocuments AI Workflow Automation
Add AI on top of the document management system your firm already runs — document profiling, extraction, and private RAG search — inside your security model, with an audit trail on every action.
iManage and NetDocuments automation adds an AI layer on top of your existing DMS: it auto-classifies and profiles incoming documents, extracts the data your matters need, and lets your team search and ask questions across filed documents — without rekeying, and without anything leaving the environment you control.
The problem
Why firms keep their DMS — and don’t have to choose
iManage and NetDocuments are the system of record for a reason: security, ethical walls, matter-centric structure. The problem is they store documents but don’t reason over them — so profiling, classification and lookup stay manual, and a standalone AI tool that lives outside the DMS never gets adopted because it breaks the firm’s workflow and security model.
We build the AI layer on top of the DMS instead of beside it. Documents are auto-classified and profiled into the right matter, data is extracted, and your team can search and ask questions across filed material — all inside iManage or NetDocuments, with the access controls and ethical walls you already enforce.
The solution
Where automation removes the friction
What we automate on iManage / NetDocuments
Auto-profiling and classification of incoming documents to the correct client/matter; extraction of key data and metadata; and a private RAG search layer so a lawyer can ask “what does our filed work say about X” and get a cited answer from the firm’s own documents — not a public model’s guess. Low-confidence actions route to a human; everything is logged.
Security & audit, inside your model
Everything runs inside your environment with role-based access that mirrors your matter permissions and ethical walls, and a full audit trail on every classification, extraction and answer. Documents never leave your boundary or train a public model.
Example workflows we build
- Auto-classify & profile incoming documents to the right matter
- Extract data and metadata from filed documents
- Private RAG search across matters, with cited answers
- Bulk back-classification of legacy documents
- Audit-trail and access-control layer over every AI action
The results
The commercial impact
Our approach
From manual to automated
- 01Map your DMS & security model
We map how matters, folders, access controls and ethical walls are structured before any build.
- 02Build on top of the DMS
Auto-profiling, extraction and a private search/RAG layer wired into iManage or NetDocuments via their APIs.
- 03Tune with human-in-the-loop
We validate classification and extraction on real documents and route low-confidence items to review.
- 04Deploy & audit
Go live inside your environment with role-based access and a full audit trail on every AI action.
Why a custom build beats off-the-shelf
- Built on iManage/NetDocuments — no new platform for associates to learn or trust.
- Respects your ethical walls and matter-level access controls.
- Answers from your own filed documents (private RAG), not a generic model.
- Every action is logged for an audit trail.
Frequently asked questions
Do we have to replace iManage or NetDocuments?
No — that’s the point. We build on top of your existing DMS via its API, so your security model, folder structure, ethical walls and access controls stay exactly as they are.
Can the AI search across our filed matters and answer questions?
Yes. We add a private RAG layer that retrieves the relevant passage from your own filed documents and cites it, so answers are grounded in the firm’s work rather than a public model’s guess.
How is confidentiality and ethical-wall compliance handled?
Access mirrors your matter-level permissions and ethical walls, everything runs inside your environment, and every AI action is logged. Documents never leave your boundary or train a public model.
Can it back-classify our existing document store?
Yes — bulk back-classification of legacy documents is a common first project, with human review on low-confidence items.
How long does it take?
A focused profiling/extraction build goes live in 4–6 weeks; adding private RAG search across a large matter store is typically 8–12 weeks depending on volume.
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.