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Customer and supplier 360
One connected view of every account across CRM, ERP, support and contracts, resolved to a single entity.
Enterprise knowledge graph
The enterprise knowledge graph your AI agents decide on.
ElixirData connects customers, contracts, assets, policies and past decisions into one governed knowledge graph. Agents get the relationships behind the data, not just the rows, and every decision they make is added back to the graph as precedent.
- Ontology-led
- GraphRAG-ready
- Decision graph built in
- VPC and on-prem
Graph view · ACME-0042
5 nodes · 4 edges
Decision · credit-hold release
Approved under Policy FIN-07, linked to 3 prior decisions on the same customer.
Illustrative example.
Works with
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Why a graph
Your data has the rows. It’s missing the connections.
A warehouse stores rows and columns. A vector database finds similar text. Neither holds explicit relationships or meaning, so agents can’t tell who and what is connected.
You don’t replace either one. The knowledge graph adds the layer they are missing.
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01
Siloed records
The same customer or supplier looks different across ERP, CRM, support and contracts.
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02
Hidden connections
Multi-hop questions, such as which suppliers of a part also have open quality incidents, need several joins across systems.
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03
Meaning left implicit
Definitions live in schemas and people’s heads, so agents guess what a field or a relationship means.
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04
No precedent
Past decisions are not recorded as data, so agents can’t learn how similar cases were decided.
What is a knowledge graph?
Six places a knowledge graph pays off
A knowledge graph models data as entities, the relationships between them and the properties of each, usually organised by an ontology that defines the types and rules.
An enterprise knowledge graph applies this across your systems, linking ERP, CRM, documents and data platforms into one connected, governed view of the business.
Supply chain lineage
Trace parts to suppliers, plants, shipments and customers, and see what a disruption touches.
Compliance and KYC networks
Map ownership, directors and transactions to spot hidden links for KYC, AML and sanctions checks.
IT dependency maps
Services, hosts, owners and changes in one graph, for impact analysis and faster incident response.
Context for AI agents
Agents query who and what is connected, which policies apply and what was decided before, before they act. See context engineering.
Policy and contract intelligence
Link clauses and obligations to the entities they govern, so changes flow to the decisions they affect.
How we build one
Start from the decisions, not the whole enterprise
We model only what your priority decisions need first, then grow the graph use case by use case.
Design the ontology
Define entity types, relationships and rules with your domain owners, reusing standards where they exist.
Ingest and resolve
Connect ERP, CRM, data platforms and documents through Unify Data, then match and merge records that refer to the same customer, supplier or asset.
Govern and keep fresh
Access control down to node and edge, lineage for every fact, and change-data capture to keep the graph current.
Put agents on it
Expose the graph to agents through governed tools, and start recording their decisions as precedent.
What’s different
Most graphs describe what exists. We also record what was decided.
Every agent or human decision becomes a first-class node, linked to the context it used, the policy it was checked against, who approved it and what happened next.
| Warehouse and vector search | ElixirData decision graph | |
|---|---|---|
| Stores | Rows, columns and embeddings of text | Entities, relationships and properties |
| Best at | Aggregates, reporting and finding similar content | Multi-hop questions and context |
| Meaning | Implicit in schema and joins, or statistical similarity | Explicit, defined by an ontology |
| Explainability | Query logic, or nearest neighbours | High: the path is the answer |
| Role for AI agents | Facts, metrics and relevant passages | Who and what is connected, and what’s allowed |
| Decisions | Not recorded as data | Stored as nodes, with decision traces as precedent |
GraphRAG explained
Retrieval that follows relationships
GraphRAG uses a knowledge graph alongside, or instead of, vector search, so the model answers with the full picture and can cite the path it used.
01
Understand the question
Identify the entities and relationship types it is about.
02
Traverse the graph
Pull the connected subgraph, filtered by the user’s or agent’s access rights.
03
Add supporting text
Combine with vector search over the linked documents.
04
Answer with the path
The model responds, citing the nodes and edges it relied on.
Results
Built for production from week one
Figures as published on elixirdata.co.
FAQ
Knowledge graphs, answered
Straight answers on knowledge graphs, ontologies, GraphRAG and decision graphs. Related: context engineering.
A map of your data as things and the connections between them, such as “this customer has this contract, governed by this policy”, so people and AI can follow those connections.
The ontology is the schema: the types, relationships and rules. The knowledge graph is the actual data organised by that ontology.
Not for simple tasks. For decisions that depend on how customers, contracts, assets and policies relate, a graph gives agents reliable, explainable context that text search alone can’t.
Retrieval-augmented generation that retrieves entities and relationships from a knowledge graph, often combined with vector search, so answers cover multi-hop questions and cite their path.
No. The warehouse stays the home for metrics and reporting. The graph links entities across it and your other systems.
ElixirData’s extension of the knowledge graph that stores decisions as nodes, linked to their context, policy, approver and outcome, so agents can learn from precedent and auditors can see the full chain.
Property graphs queried with Cypher or GQL (Neo4j is the best-known), RDF and OWL queried with SPARQL, and Amazon Neptune, alongside lakehouse and vector stores such as Snowflake and Databricks.
We start with the entities behind one or two priority decisions rather than the whole enterprise. ElixirData deploys as managed SaaS, in your VPC, or on-prem and hybrid for regulated and sovereign workloads.
See your business as a graph
In one working session we model the entities behind one priority decision and show what your agents could see, and decide, with it.