Knowledge Hub use cases

note

Knowledge Hub is an Early Access release. Refer to the Early Access disclaimer for details.

Boomi Knowledge Hub is designed for scenarios where AI agents need to retrieve accurate, grounded answers from enterprise data. The following use cases are illustrative examples of the kinds of problems Knowledge Hub helps you solve.

Use Knowledge Hub when

• You want AI agents to answer questions using your organization's actual data, not general training knowledge.
• Your enterprise data is spread across multiple systems (file storage, wikis, CRMs, support tools) and agents need a single retrieval point.
• You need cited, source-backed responses from agents rather than hallucinated answers.
• You want to control which agents can access which Knowledge Bases, and monitor retrieval and ingestion activity centrally.
• You want to bring data into an AI-ready index without rebuilding your existing source systems or pipelines.

Example use cases

Example 1: Collections intelligence agent

Situation: A customer is 45 days overdue on $75,000 in annual recurring revenue (ARR). Recent support escalations create risk.

Task: The agent must decide whether to escalate, offer flexibility, or route to recovery.

	Without Knowledge Hub	With Knowledge Hub
Action	Sees only overdue status in the customer relationship management (CRM) system Lacks visibility into past similar cases Defaults to rule-based escalation	Retrieves similar cases across CRM, calls, and tickets Ranks successful versus failed collection approaches Returns evidence to support the decision
Result	Generic outreach Higher risk of customer friction	Context-aware recovery strategy Explainable, auditable decision

Knowledge Hub supplies cross-system evidence that transactional systems cannot provide alone.

Example 2: Security questionnaire agent

Situation: A customer sends a security questionnaire with varied phrasing. Prior answers exist across past responses and documents.

Task: Draft accurate responses by reusing relevant prior answers, even when the wording differs.

	Without Knowledge Hub	With Knowledge Hub
Action	Searches past questionnaires with exact text search Processes each source separately	Searches similar questions and answers across past questionnaires Applies unified context to search all connected sources simultaneously
Result	Misses relevant answers when wording differs Delivers inconsistent responses Requires a slow, multi-step process	Finds relevant answers even when phrasing differs Delivers consistent answers Completes faster with less effort

Next steps

• Refer to Knowledge Hub overview to understand the three capabilities and how Knowledge Hub works.
• Refer to Prerequisites to confirm your account requirements.
• To set up your first Knowledge Base, refer to Quickstart: ingest your first data source and query it.