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What is the Intent Graph?

The Intent Graph is the shared data layer where customer context lives. Structured facts that every touchpoint reads and writes — so context compounds instead of resetting.

It's the technical foundation of Intent-Native CX: a thin layer that sits alongside your existing stack, giving every system access to the same customer context.

Architecture developed by MING Labs.

What the Intent Graph Stores

Data Type Example Source
Explicit intent "Looking for all-mountain ski, intermediate level" Navigator conversation
Inferred intent "Price-sensitive based on questions asked" Product finder behavior
Context "Arrived from 'best skis for moguls' AI query" URL/referrer analysis
Journey state "Has shortlist of 3 skis, requested dealer contact" Session aggregation
Qualification "Budget 800-1200, buying timeline: this season" Form + conversation

How the Intent Graph Works

Design Principles

Integration Pattern

Every touchpoint does two things:

  1. Read: Query the graph for known context about this customer.
  2. Write: Append new facts learned during this interaction.

That's it. No schema changes to your CRM. No data migration. Just read and write.

Frequently Asked Questions

Is the Intent Graph a database?

It's an abstraction over storage. The underlying implementation is event-sourced (facts are appended, never mutated) and can be backed by any persistent store. The API is what matters — read context, write facts.

How is this different from a CRM?

CRMs store relationship history. The Intent Graph stores intentional state. CRM: "This customer bought X last year." Intent Graph: "This customer is researching Y for Z use case right now."

What about privacy?

The Intent Graph is designed for consent-first data capture. Facts are tagged with consent context. Customers can request full export or deletion. It's GDPR-compliant by architecture, not by bolt-on.

Explore the architecture →