Ask iManage brings AI directly into the iManage platform, allowing users to work with AI natively inside iManage with no configuration and no content leaving the system. MCP enables governed iManage content to be accessed securely by external AI tools through a single open endpoint. Many organizations use both: Ask iManage for in‑platform AI, and MCP to connect iManage knowledge to the AI tools they already use.
Knowledge in Motion
Governed Knowledge. AI-ready.
iManage MCP Server securely connects governed iManage content to the AI tools you choose. Replace custom integrations with one open protocol, keep content in place, without bulk exports or replication, and ensure permission-bound, auditable AI access as your AI strategy evolves.
Standardized AI Connectivity
One MCP (Model Context Protocol) connection replaces multiple custom API integrations. Configure once and connect any MCP-compliant AI tool — reducing IT effort while avoiding vendor lock-in.
Access Without Bulk Extraction
iManage customers can request that their AI vendors access their content via the iManage MCP Server rather than bulk exporting documents. Customers should work with their vendors to understand what data leaves iManage through the connection and how it is handled.
Governed & Auditable Access
All AI access is authenticated, permission-bound, and logged. Ethical walls, security policies, and audit requirements are enforced automatically.
A single secure gateway between AI and iManage
MCP provides a standardized integration layer between iManage and AI systems. Instead of maintaining separate integrations per vendor, organizations deploy one secure endpoint that scales across current and future AI tools.
Open, vendor-neutral architecture
Built on an open protocol, MCP avoids dependency on any single AI ecosystem. Your AI strategy remains flexible, extensible, and resilient as new models and platforms emerge.
iManage stays the source
Documents are retrieved under the user's existing iManage permissions — nothing outside their access can be retrieved. Every retrieval is logged
Permission-bound by design
MCP enforces existing iManage permissions and ethical walls for every AI interaction. Access is authenticated and logged, ensuring defensible AI use in regulated environments.
Future-proof AI integration foundation
As AI adoption accelerates, MCP ensures your integration strategy compounds rather than fragments — eliminating the rebuild cycle as tools and models change.
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How is MCP different from Ask iManage?
How does MCP keep content secure and governed?
With MCP, content stays in iManage — AI systems access documents in place through governed, permission‑bound requests. Content is never bulk exported, replicated, or stored in third‑party AI data stores, ensuring iManage remains the single source of truth. All AI access is authenticated and governed by existing iManage permissions and ethical walls. Every interaction is logged and auditable, giving organizations full visibility into how AI tools access legal content and ensuring compliance with regulatory and internal governance requirements.
MCP is vendor‑neutral and designed to work with any MCP‑compliant AI system, including generalist AI platforms and legal‑specific AI tools. Organizations can add, switch, or onboard new AI tools without building new integrations or re‑architecting their environment.
Is MCP Suitable for regulated and highly governed environments?
Yes. MCP is designed for regulated organizations that require permission‑bound access, auditability, and strict control over where content resides. By keeping content in iManage and logging every AI interaction, MCP enables AI adoption without compromising governance or compliance.
Why not build custom API integrations for each AI tool?
Custom point‑to‑point integrations are costly, slow to build, and brittle as AI tools evolve. Each new AI system becomes a separate IT project. MCP replaces this with a single standardized connection that works across AI tools, reducing integration sprawl and future‑proofing AI adoption.
Making Knowledge Work
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