Knowledge Graph
Give connected data a shared meaning.
An account in your CRM, a customer in your database and a person in a conversation may describe the same relationship. A knowledge graph connects those records so agents can follow the business story across systems.
Connect identities with care.
Start with reliable identifiers and the relationships your systems already describe. Shared names or email addresses can be ambiguous. Make those uncertainties visible before treating two records as the same person.
Business identities
People, accounts, organisations and the identifiers each system gives them.
Related activity
Purchases, cases, payments, events and conversations connected to those identities.
Source evidence
The records behind a conclusion, with enough context to check the answer.
Make the relationships useful.
Ask which customers need attention, what changed on an account or which records explain a discrepancy. The graph gives the agent a route through the relevant information, while business definitions keep different events distinct. A purchase, a payment and attendance are different facts.
A foundation for agents and workflows.
Give the same connected context to a question, an investigation or an MCP-connected tool. Build toward workflows that carry the correct customer, evidence and permissions from one task to the next.
Questions, answered.
Is the graph another copy of every system?
The graph describes identities and relationships. The underlying data can come from connected sources or an agreed data lake; the storage and refresh design is scoped separately.
Can it match every record automatically?
Matching depends on available identifiers and source quality. Ambiguous matches need an explicit decision; a matching name alone is not enough.
Planning an evaluation
Agree the scope before you start.
An evaluation establishes the source schemas, identity links and questions to validate. Broader automatic matching and graph coverage are agreed as part of the rollout.
Discuss your data relationships