Knowledge graphs and grounded AI
We connect your data into a knowledge graph, a living model of your organisation, so AI answers from your own facts and shows where each one came from.
Talk to an architect- Intelligence05 What should we do?
- Insights04 Why, and what comes next?
- Knowledge03 How does it all connect?
- Information02 What is happening?
- Data01 What do we hold?
- Open by designUnder every layer
Can we trust what the AI says?
Every answer cites the records behind it, and the system says when the evidence is missing rather than guess.
Every fact linked, defined and sourced.
- 01Ontology
A shared vocabulary of your business: what each kind of thing is, how it connects and the rules it follows. People, dashboards and AI use the same words in the same way.
- 02Knowledge graph
Your data connected by that vocabulary: each real thing appears once, linked to everything it touches, with the source and date of every fact.
- 03Semantic layer
One agreed definition of every measure, so a dashboard, a model and an AI assistant give the same number.
- 04Grounded AI
AI that answers by retrieving facts from the graph and the agreed measures, cites them, and says when the data cannot answer.
WHAT YOU SEE → AI answers that cite your own records, and say so when the data cannot answer.
AI that shows its working.
We connect your data into a knowledge graph, a connected model of your organisation, so AI answers from your own facts and shows where each one came from.
- STEP 01A question
“Which customers are we most likely to lose this quarter, and why?”
- STEP 02What the words mean
The ontology fixes what “customer”, “lose” and “quarter” mean in your business.
- STEP 03The connected facts
The knowledge graph links every fact to each customer, with its source and date.
- STEP 04Agreed measures
Models score each risk; churn is calculated the one agreed way.
- STEP 05An answer, with sources
The customers, the reasons, and the records behind each one.
- STEP 06Your people decide
The decision and its result go back into the graph.
16%54%
In one benchmark, GPT-4 answered 16% of 43 enterprise questions correctly when it queried a SQL database directly, and 54% when the same data was described by a knowledge graph. One insurance data model; not a promise. Sequeda, Allemang and Jacob, 2023
When the graph holds no evidence, the answer says so.
Chosen for you, not for us.
Examples, not a preferred list. We use open-source components where they fit, and name every standard and licence in the design.
| Capability | Typical tools and standards |
|---|---|
| Ontology design | W3C OWL, RDFS and SKOS; SHACL for rules; industry models such as FIBO and HL7 FHIR |
| Knowledge graph | Apache Jena, Apache AGE, GraphDB, Stardog, Amazon Neptune, Neo4j; SPARQL, Cypher or ISO GQL; W3C PROV-O |
| Semantic layer | dbt Semantic Layer, Cube or the platform’s own; Apache Ossie (incubating) |
| Grounded AI | GraphRAG patterns; open-weight or commercial LLMs with tool use; tests for accuracy and groundedness |