A company knowledge graph can help an AI assistant when the question depends on relationships: which offer serves an audience, which approved claim supports a message, or which source established a fact. Similar passages alone may not represent those relationships clearly enough.
That does not make a graph the default choice for every retrieval system. Begin with the questions people need answered. Introduce structure when it makes those questions easier to answer, verify, and maintain.
Separate search relevance from company meaning
Text search and vector similarity help locate material. They do not, by themselves, establish that a claim is current, approved, or applicable to a particular offer. Those are product and knowledge-management decisions.
Narravo's Living Brand Graph organizes company knowledge with relationships, sources, provenance, and review states. Its value in the portfolio story is the connection between business context and controlled knowledge changes. It is not presented as a benchmark proving one graph approach outperforms every document-retrieval architecture.
Start with a small knowledge model
For an illustrative consultancy, useful records might include an audience, an offer, a claim, and a source. Define what each record means before creating a large set of entity types.
Scroll sideways to view the full table.
| Relationship | Question it helps answer | Maintenance responsibility |
|---|---|---|
| Offer serves audience | Which offer fits this buyer? | Product or commercial owner |
| Claim supported by source | What evidence supports this sentence? | Source reviewer |
| Claim applies to offer | Where may we use this claim? | Offer owner |
| Fact replaces older fact | Which version is current? | Knowledge editor |
| Suggestion awaits review | Is this information approved? | Authorized reviewer |
The table describes a fictional design example. The exact records and relationships should follow your organization's terminology and questions.
Make provenance a first-class concern
A relationship needs context about where it came from and who approved it. A model extracting a relationship from a document produces a candidate interpretation. It does not establish that the organization endorses that interpretation.
Keep candidate knowledge distinguishable from approved knowledge. Store enough source information for a reviewer to inspect the evidence. Decide what happens if a source is corrected, removed, or becomes inaccessible.
An assistant should not continue presenting a withdrawn claim merely because an embedding or a graph edge survives. The update path has to cover every retrieval representation that can expose the knowledge.
Combine retrieval methods deliberately
Some requests need an exact record lookup. Others need full-text search or passage similarity. A question about linked offers may benefit from traversing approved relationships and then retrieving the original source material.
Treat the graph as one part of the retrieval architecture. Do not force every question through the same path. Compare the simpler alternative using representative questions and the evidence needed for each answer.
This article uses "company knowledge graph" for structured business relationships. It does not imply that Narravo implements a particular branded GraphRAG research pipeline or that those names are interchangeable.
Evaluate relationships as well as answers
Check that each important relationship has the expected direction, source, status, and access boundary. Then inspect whether the assistant uses it appropriately.
A useful evaluation question might ask for a claim about an offer whose supporting source has been withdrawn. The expected result should reflect that withdrawal. Another might request information from a different company; the retrieval layer must enforce the company boundary.
Decide who keeps the graph useful
The operating plan needs owners for approving knowledge, resolving duplicates, changing relationships, and reviewing suggestions. A detailed graph with no maintenance workflow can become a confident representation of outdated assumptions.
Narravo's review model provides a concrete example of separating suggested learning from approved knowledge. Connecting company knowledge through MCP explains how approved context can travel to compatible AI clients.
For your own system, start with a source inventory and a set of real questions. Knowledge-system consulting can establish whether a document index, structured records, a graph, or a combination fits the problem.
Prepared with AI assistance using Paul’s documented project work and the linked sources. Examples are illustrative unless identified as project records.