Ontology and schema design
We model the entities, attributes and relationships that describe your domain, so the graph reflects how your business actually connects.
Knowledge Graph Development
Knowledge Graph Development
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
Turn scattered data into a connected map of your domain. Noseberry designs and builds knowledge graphs, modelling your entities and their relationships, so search, recommendations and AI can follow real connections rather than guess. The structured foundation that grounded AI and GraphRAG are built on.
Book a free knowledge graph sessionDefinition
What is knowledge graph development?
Knowledge graph development is the design and build of a graph that models your data as entities and the relationships between them, rather than as isolated rows in tables. It covers ontology design, ingesting and mapping data, resolving duplicate entities, storing and querying the graph, and governing it over time. The result is a connected, queryable map of your domain that powers relationship-aware search and recommendations and gives AI a structured foundation to ground its answers in.
Key takeaways
- A knowledge graph models your data as entities and the relationships between them, not just rows in tables.
- It gives AI and search a connected, queryable map of your domain, so answers can follow real relationships.
- The work covers ontology design, data ingestion and entity resolution, querying, and governance.
- Knowledge graphs are a strong foundation for grounded AI and are what GraphRAG builds on.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
Scope
What knowledge graph development includes
From ontology to a governed, queryable graph your applications and models can rely on.
Data ingestion and mapping
We pull data from your sources and map it into the graph, transforming records into connected nodes and edges.
Entity resolution
We match and merge duplicate or fragmented records, so one real-world thing is one node, not five.
Graph storage and querying
We build the graph on a fit-for-purpose store with query and API access, so applications and models can use it.
Search and recommendations
We power relationship-aware search, discovery and recommendations that flat data cannot support.
Governance and maintenance
Quality checks, lineage and update pipelines, so the graph stays accurate and current as data changes.
Who this is for
Built for teams whose data is all about relationships
Data and product teams who need to connect, search and reason over related data, or to ground AI in a structured model of their domain.
Signs you need it
- Your data is connected in reality but siloed in tables that lose those relationships.
- You need search or recommendations that follow relationships, not keywords.
- You want to ground AI in a structured map of your domain.
- Answering real questions means joining many sources and you have no unified view.
- You are planning GraphRAG and need the graph underneath it.
How we work
Model, build, serve, govern
1
Model the domain
We design the ontology: the entities and relationships that matter to you.
2
Ingest and resolve
We load data into the graph and resolve duplicate entities.
3
Build the graph
We stand up the graph store with query and API access.
4
Serve
We connect search, recommendations or AI to the graph.
5
Govern
We add quality checks and update pipelines to keep it current.
Why Noseberry
Why choose Noseberry for knowledge graphs
Ontology done right
A graph is only as useful as its model. We design ontologies that reflect your real domain.
Built for AI
Graphs designed to ground AI and power GraphRAG, not just to sit in a database.
Engineering depth
Ingestion, entity resolution and pipelines from our data engineering practice.
Proven at scale
2M+ lives touched, 15+ Fortune 500 clients, 250+ solutions across 20+ countries.
Frequently Asked Questions
A knowledge graph models your data as entities (people, products, places, concepts) and the relationships between them, stored as a connected graph rather than flat tables. It lets applications and AI follow real relationships to answer questions.
Relational tables lose the relationships that matter when questions span many entities. A knowledge graph makes those relationships first-class, so relationship-aware search, recommendations and grounded AI become possible.
It gives AI a structured, queryable map of your domain to ground its answers in, reducing hallucination and enabling reasoning over connected facts. It is also the foundation for GraphRAG.
The ontology defines the entities, attributes and relationships in your graph. Get it right and the graph is powerful and extensible; get it wrong and the graph is hard to query and maintain. It is the most important early decision.
Yes. We ingest and map your existing sources, resolve duplicate entities, and build the graph, then add pipelines to keep it current.
It is primarily a data capability that most often serves AI, so it sits in our data practice and is linked from AI. It underpins grounded AI and GraphRAG.
Connect your data into a graph
Book a free session and we will model the graph your domain needs.
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