Knowledge graph development
A knowledge graph turns scattered records, documents and spreadsheets into one connected model of your business that answers questions no single system can. We design the model, build the pipelines that fill it, and run it in production.
When this is the right service
- The same customer, product or asset exists in five systems with five different identifiers.
- Analysts spend days joining exports by hand to answer one question.
- An AI assistant needs reliable, structured facts about your domain instead of guessing from text.
- Regulators or partners ask for lineage: where did this number come from and what does it depend on?
What we do
- 1
Domain modeling
Workshops with your experts to capture the entities, relationships and rules that matter, written down as an ontology or graph schema your team can read.
- 2
Graph choice
RDF triple store or labeled property graph, picked for your queries, your tooling and your people. We work with both and tell you honestly which fits.
- 3
Ingestion pipelines
Mappings from relational databases, APIs, CSV exports and documents into the graph, with validation (SHACL or schema checks) so bad data is caught before it lands.
- 4
Query and API layer
SPARQL or Cypher endpoints, GraphQL or REST APIs on top, and dashboards or search where people need them.
- 5
Production operations
Containerized deployment, backups, monitoring, access control and CI/CD, so the graph is a system, not a prototype.
Tools and standards
- RDF, RDFS, OWL
- SPARQL and SHACL
- GraphDB, Apache Jena, Stardog, Blazegraph
- Neo4j and Cypher
- R2RML and RML mappings
- Python and Node.js pipelines
- Docker, Kubernetes, GitHub Actions
What you get
- Graph schema or ontology with documentation
- Loaded, validated knowledge graph
- Ingestion pipelines with tests
- Query endpoints and APIs
- Deployment and runbook
Frequently asked questions
How long does a knowledge graph project take?+
A focused first version with one or two data sources and real queries usually takes six to ten weeks. We prefer to ship a small graph that answers real questions and grow it, rather than model everything first.
RDF or Neo4j?+
RDF fits when you need standards, shared vocabularies, reasoning or data exchange with other organizations. Property graphs fit when developers want a fast start and path queries dominate. We have built both and will recommend one after seeing your data and questions.
Can the graph feed an LLM or AI agent?+
Yes. That is one of the main reasons companies build one today. See our GraphRAG and AI agents service.
Related services
Ontology Engineering
Ontologies, taxonomies and vocabularies that give your data shared meaning across teams and systems.
GraphRAG and AI Agents
LLM applications grounded in your knowledge graph, so answers are traceable and agents act on facts.
AI Product Development
Full stack teams that ship AI powered web and mobile products: React, React Native, Node, voice and real time.
Talk to us about knowledge graph development
Tell us about your data, your product or your AI use case. We reply within one business day with honest first thoughts.
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