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AI for organizational knowledge

Help people find answers across the knowledge they can access.

Connect an AI knowledge assistant to selected internal sources, preserve existing access boundaries and return answers with useful source context—so employees can verify what they find.

Delivery framework

From business need to production-ready AI

  1. Employee asks a work question
  2. Search approved and accessible sources
  3. Retrieve relevant passages and references
  4. Prepare an answer with source context
  5. Escalate gaps or conflicting guidance

Business-led, with people accountable for decisions and approvals.

A workflow shaped around your operation

Connect the work, not just the conversation.

A useful knowledge experience joins information across approved repositories while keeping the user’s identity and source permissions in the retrieval path.

Find

Policy and procedure guidance

Help employees locate current internal guidance and follow links to the authoritative source.

Search

Cross-repository discovery

Search selected content from collaboration sites, document stores and internal knowledge bases through one experience.

Access

Role-aware answers

Use the employee’s permitted source set to retrieve information relevant to their work and responsibilities.

Improve

Knowledge gap signals

Identify unanswered or conflicting questions for content owners to investigate and improve.

Designed to fit existing systems

Meet the process where it already lives.

Integration scope depends on the organization’s architecture, available interfaces and access policies. These are common connection points to assess—not assumed integrations.

Start by identifying the system of record, the users who need context and where an approved outcome should be recorded.

Systems and information to consider

  • Document repositories and collaboration platforms
  • Knowledge bases, policy libraries and internal portals
  • Identity and access systems for permission-aware retrieval
  • Content ownership and freshness metadata
  • Employee interfaces such as intranet or collaboration tools
Human oversight and data controls

Keep consequential decisions with accountable people.

Enterprise search is only trustworthy when source permissions, content freshness, evidence and clear limits are addressed together.

Explore AI Governance

Controls to define with your team

  • Enforce source-level access permissions for each user’s retrieval results
  • Show source names, dates or links so employees can check the underlying guidance
  • Identify stale, missing or conflicting content rather than presenting certainty
  • Define owners for content access, index refresh and issue escalation
Evaluate before expanding

Measure the workflow, including its exceptions.

Choose a baseline and review method before deployment. Metrics should reflect process quality and customer or employee outcomes, not just the volume of automated activity.

Retrieval relevance

Evaluate whether retrieved passages answer representative employee questions and include the authoritative source.

Answer grounding

Sample responses for support from cited material, clarity about gaps and appropriate abstention.

Knowledge task completion

Measure whether employees find needed guidance and complete the intended task with fewer search steps.

Content health

Track stale or conflicting source findings, unanswered query themes and content-owner follow-up.

No outcome is assumed. Appropriate targets and evaluation methods depend on your baseline, data quality and operating context.

Connected capabilities

Apply the right AI capability to the use case.

These established AI services can provide building blocks for the solution, selected to fit its workflow and controls.

All AI services

Next step

Explore a practical enterprise knowledge workflow.

Bring a real process, its system boundaries and the people responsible for review. We can help define a scoped approach and how to evaluate it.

Discuss Enterprise Knowledge