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Integration guide · Updated August 2026

Private AI with NetDocuments

A useful NetDocuments AI layer should retrieve the right legal work product for the signed-in user, preserve the document system's permissions and matter context, cite every source, and keep consequential work under human review.

NetDocuments is the system of record; AI is the working layer

NetDocuments organizes documents, email, versions, workspaces, matter metadata, and access rules. A private AI system should not create a shadow repository with a weaker security model. It should connect through a supported route, retrieve only what the authenticated user may see, and return answers that point back to the governing document and version.

NetDocuments describes ndConnect as its interoperability path for approved AI tools. Its standard flow is user-driven document selection; its enterprise flow uses MCP for agent-driven retrieval. Those official options should be evaluated before proposing a custom connector.

What the architecture must preserve

Identity and permissions

Every retrieval is scoped to the authenticated user, including ethical walls and matter-level restrictions.

Matter context

Workspace, client, matter, document type, author, version, and status metadata stay available for filtering and ranking.

Source provenance

Answers link to the exact source and version so a lawyer can inspect the evidence instead of trusting generated prose.

Retention boundaries

Prompts, retrieved text, embeddings, logs, and generated output follow an approved retention and deletion policy.

Auditability

The team can reconstruct who asked, which sources were retrieved, what the system returned, and what a reviewer changed.

Human control

The system assists research and drafting; it does not silently file, advise, approve, or send consequential work.

High-value workflows

Start where the answer already exists in controlled firm content and the reviewer can verify it. Good first candidates include precedent search, matter chronology, clause comparison, deposition or contract summaries, policy lookup, and drafting an internal outline from a selected document set. Avoid a vague “chat with everything” launch: broad access makes evaluation, permissions, and user expectations harder to control.

A six-step pilot

  1. 01Choose one bounded workflow, such as precedent search or first-pass document summarization.
  2. 02Define which workspaces, matters, document types, versions, and users are in scope.
  3. 03Select the supported connection path and document what data each service can receive and retain.
  4. 04Build a representative evaluation set with expected sources, correct answers, and known failure cases.
  5. 05Require citations back to the source document and route weak or conflicting answers to a reviewer.
  6. 06Measure retrieval quality, permission behavior, reviewer corrections, latency, and adoption before expanding.

Acceptance checks before rollout

  • A restricted user cannot retrieve or infer content from a blocked matter.
  • Every material answer includes a resolvable source citation and document version.
  • Deleted, superseded, or re-permissioned content is removed from retrieval on an agreed schedule.
  • The evaluation set includes ambiguous questions, conflicting documents, and no-answer cases.
  • Users can report a bad answer, see the limitation, and reach the responsible reviewer.
  • The firm has approved the vendor, data flow, retention, incident process, and user policy.

Official references

Verify product availability and governance details against NetDocuments' current documentation. Start with the official ndConnect integration overview and NetDocuments Legal AI Assistant overview. Product capabilities and commercial terms can change.

Map a controlled NetDocuments AI pilot

Bring the workflow, document boundary, existing tools, and security constraints. Rai Rai will map the architecture, evaluation plan, and human-review path before a build begins.

Map my private AI pilot