An AI agent that automatically builds a knowledge graph of a company's internal data. It ingests documents, emails, and chat logs to map relationships between people, projects, clients, and concepts, creating a searchable 'brain' for the organization.
In any organisation of reasonable size, knowledge is scattered across shared drives, email, chat channels, project tools and people's heads. New staff do not know who worked on a past project or where the final version of a proposal lives. Experienced staff answer the same questions repeatedly. Keyword search returns long lists of files without showing how they relate, and valuable context disappears when someone leaves.
An AI knowledge graph builds a connected map of the organisation's information. It reads documents, project records and permitted communications, identifies entities such as people, projects, clients, products and topics, and records the relationships between them. Staff can then ask questions in plain language, such as who led a particular project or which documents relate to a client, and get answers with links to sources. Retrieval-augmented generation lets an LLM summarise across the graph while respecting existing access permissions.
Connectors ingest content from document stores, project management tools, CRM records and, where approved, email and chat metadata or selected channels.
NLP models extract entities such as people, clients, projects and topics, and resolve duplicates so variations of a name point to the same node.
Relationships, for example 'worked on', 'authored' or 'relates to client', are stored in a graph database such as Neo4j, with links to source documents.
Users query through enterprise search or a chat interface, and RAG combines graph relationships with document passages to answer with citations.
Access controls mirror the source systems, so users only see entities and documents they already have permission to view, and owners correct errors.
A new employee needs to know who the expert on 'Project Titan' is. They query the corporate knowledge graph, which instantly shows the project lead, key team members, and links to all relevant documents, dramatically reducing ramp-up time.
Knowledge Graphs (Neo4j), NLP, RAG, Enterprise Search Tools
Ingesting emails and chats captures personal information about staff, clients and third parties, so the Privacy Act 1988 applies, and in New South Wales and the ACT, workplace surveillance laws may require notice before employee communications are monitored. Limit sources to what is needed and exclude personal or HR-sensitive channels. Legal professional privilege and client confidentiality obligations mean some material must be excluded or tightly restricted. Permission mistakes can expose sensitive data to the wrong staff and may create a notifiable data breach. APRA-regulated entities should assess the system under CPS 234. Host the graph and search index in an Australian cloud region where possible.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Search finds documents that contain your words. A knowledge graph understands entities and relationships, so it can answer questions like which people worked with a client on a given type of project, and show connected documents. Combined with an LLM, it gives summarised answers with sources instead of a list of files to open one by one.
No, and many organisations deliberately exclude email content. Documents, project tools, CRM records and selected public chat channels provide rich data on their own. If email is included, consider metadata only or opt-in mailboxes, and address workplace surveillance notice obligations and privacy concerns before connecting it.
Connecting multiple sources, extracting and resolving entities accurately, mapping permissions and testing answers with real users all take time. Each source has its own data quality issues. Organisations usually start with one or two repositories and a focused use case, such as an expert finder, before expanding.
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