Someone contacts your company. You ask them to explain their situation from scratch. They sigh. "I already told Sarah all of this last week. And Mike before that. And someone on your team three months ago."
You check the CRM. Their record shows a name, email, and four disconnected ticket numbers. No context about what Sarah discussed. No record of the project they mentioned. Nothing about the colleague they said referred them.
Every conversation starts at zero because your systems store people as isolated records, not as connected nodes in a network of relationships.
The missing piece is not more data about this person. It is the map of how they connect to everything else.
INTELLIGENCE LAYER - Assembles the web of connections that transforms isolated records into contextual understanding.
Relationship context captures how entities connect to each other: who introduced whom, which projects involve which people, what past interactions inform current ones, which team members have history together. Instead of treating each record as an island, it builds a graph of connections.
When someone contacts you, relationship context can surface that they were referred by your top partner, that they worked with your competitor last year, that their colleague had an issue you resolved successfully, and that they share a board member with another account. This context changes how you respond.
Relationship context turns "who is this person?" into "here is how they fit into everything we know." It is the difference between treating someone like a stranger and treating them like part of a connected network.
Relationship context solves a universal problem: when you have knowledge about many entities, how do you surface the connections between them so every interaction benefits from the full picture?
Identify entities (people, projects, organizations, events). Map the relationships between them (referred by, works with, previously interacted, shares connection). Store relationships as first-class data. Query the graph when context is needed. Surface relevant connections at the moment of action.
Select an entity to reveal how it connects to everything else in your network.
Identify how entities connect
When new information arrives, extract not just the entities but the relationships between them: "referred by," "works at," "previously worked with," "mentioned in," "attended together." Relationships are as important as the entities themselves.
Store connections as first-class data
Traditional databases store records. Graph databases store relationships. When you query "who is connected to this person," the answer comes in milliseconds because the connections are explicit, not computed through joins.
Present relevant connections at the right moment
When someone interacts with your system, query their relationship graph: who referred them, past interactions, shared connections, related issues. Surface the most relevant relationships to inform the current action.
A new inquiry arrives. The name is vaguely familiar. With relationship context, you instantly see: referred by your top partner, previously worked with your competitor, their colleague had an issue you resolved last month, and they share a board member with another account. This context shapes your response.
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You capture every person, every project, every interaction as separate records. The data exists, but the connections do not. When someone asks "has this person ever worked with us before?" you have to manually search across five different systems.
Instead: Treat relationships as first-class data. When you capture an entity, always capture how it connects to existing entities.
Your system surfaces 47 connections for a single contact. Their second cousin once attended the same conference as someone at your company. The actual referral relationship is buried in noise.
Instead: Weight relationships by type and recency. Direct referrals matter more than distant connections. Recent interactions matter more than five-year-old ones.
You only capture relationships when they are explicitly entered into a form. The email where someone mentioned their colleague, the call note where they named their previous vendor, the chat where they asked about a mutual connection - all ignored.
Instead: Extract relationships from unstructured sources: emails, call notes, chat logs, meeting summaries. The richest relationship data lives outside forms.
You have learned how to map connections between entities so every interaction benefits from the full picture. The natural next step is understanding how all context components come together into a complete package.