AI USE CASE
Enterprise Knowledge Graph and Semantic Search
Connect documents, code, and conversations into a searchable knowledge graph for knowledge workers.
What it is
An AI-powered knowledge graph indexes and links documents, codebases, Slack threads, and wikis so employees can retrieve precise answers rather than raw files. Semantic search surfaces related context across silos, reducing duplicated work and accelerating onboarding.
Data you need
The organization needs a corpus of structured and unstructured internal content, documents, wikis, code repositories, and communication logs, with consistent metadata and access controls.
Required systems
- data warehouse
- project management
Why it works
- Start with two or three high-value content domains (e.g., engineering docs + support tickets) before expanding.
- Integrate the search interface directly into existing daily tools to minimise friction and drive adoption.
- Assign a dedicated knowledge owner responsible for taxonomy, access policies, and ongoing curation.
- Instrument search queries and failed retrievals from day one to continuously improve relevance.
How this goes wrong
- Content sprawl across too many disconnected sources makes ingestion and deduplication prohibitively complex.
- Without strong data governance, the graph surfaces outdated or confidential information to the wrong users.
- Low employee adoption if the search UX is not embedded in existing tools like Slack, Teams, or the IDE.
- Graph quality degrades rapidly without a process for continuous ingestion and entity resolution updates.
When NOT to do this
Do not attempt this if your organization's documents are stored in more than five different systems without a unified identity or permissions model, the integration overhead will dwarf any search benefit.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.