Data that tells its own story through its connections
Unlike traditional databases that force you to know exactly what you're looking for before you start, Knowledge Graphs let you explore and discover.
Instead of storing data in isolated tables or silos, a Knowledge Graph represents information as an interconnected network of entities and relationships, creating a living map of your organization's knowledge.
What it is
One connected map, not another silo
IntelliStream connects your structured and unstructured sources into one shared model, carries context across every department, and makes the relationships visible, so bottlenecks, patterns, and impact across systems are traceable instead of buried.
In industry this map has a name, the industrial knowledge graph. It is the part of an operational data platform that knows how your operation fits together: which pump feeds which process, which process feeds which reported figure, and who answers for it.
Query the way you think
Ask questions, not query plans.
The data tells its own story through its connections allowing you query data the way you think about it:
How it works
Two building blocks. That's the whole model.
Knowledge Graphs simple in structure consist of just two fundamental components:
Nodes: The "What": Nodes represent the entities in your data such as:
- People: Employees, customers, suppliers, stakeholders.
- Assets: Equipment, facilities, products, inventory.
- Processes: Workflows, procedures, projects, task.
Each node contains properties, the specific attributes that describe it (like name, date, status, or any other relevant information).
Relationships: The "How": are the connections between nodes, carrying meaning and context:
- A Product contains a Component
- A Person works in marketing-department.
Relationships can also have properties (like start date, or component type), adding even more context to the data.
Put to work
Your operation is already a graph
A pressure reading belongs to a pump. The pump serves a process. The process feeds a figure someone reports to a regulator. Your systems store all of this in separate tables. Asset dependency mapping puts the connections back, so the answers are there before the questions get urgent.
Trace every reported figure
When an auditor asks where a number came from, follow it back through every calculation to the raw signals it was built from, with data-quality flags intact. Four hours of manual cross-checking becomes minutes.
Investigate failures in context
When a component fails, the graph shows what sits upstream and downstream: the equipment it serves, the processes at risk, and the people who need to know. Not five browser tabs and a senior engineer's memory.
Onboard new sources once
Connect a new data source to the model once, and every report and dashboard downstream inherits its context. Adding a system stops meaning rewriting everything that sits on top of it.
Why it holds up
Built for people, synced with reality.
Intuitive for Everyone
What makes Knowledge Graphs special is their inclusive design. The visual, network-based structure makes sense to both technical and non-technical users. Anyone that can understand a relationship diagram or a family tree, can easily work with a Knowledge Graph.
Real-Time and Reliable
Knowledge Graphs are dynamic and self-synchronized. When new information enters your system or existing data changes:
- Relationships seamlessly adjust to reflect changes as they happen.
- Connected data syncs intelligently across the graph.
- New nodes integrate seamlessly into existing structures.
- Historical records remain intact while reflecting the latest information.
- Data quality improves through relationship validation.
- The interconnected structure exposes inconsistencies and anomalies.
Where it fits
Built into the operational data platform
A knowledge graph on its own is a map with nothing on it. In IntelliStream the graph is built in and already connected to everything else the platform holds: time-series storage that compresses industrial signals around 100x, live event streams, and lineage on every derived value.
The whole stack is open source under AGPL-3.0, built on components your team already trusts: PostgreSQL, ClickHouse, Neo4j, Apache Pulsar. Read the code, run it in your cloud, on-prem, or on our own hardware in Stavanger, Norway. Nothing proprietary to escape from later.
Common questions
Straight answers to common questions
What is an industrial knowledge graph?
A connected model of your physical assets, the processes they drive, and the business context that gives their data meaning. Sensor readings, events, and documents attach to the equipment they describe, so a question about one part of your operation can follow real relationships instead of joins across five systems.
How is a knowledge graph different from a data warehouse?
A warehouse stores tables and answers the questions it was designed for. A knowledge graph stores relationships, so it also answers the questions nobody predicted, like which reported figures depend on one failing pump. Most operations need both. The graph carries the context; the warehouse carries the history.
Do we have to model everything up front?
No. Start with one plant area or one nagging question, and grow the model as new questions arrive. New assets and relationships integrate into the existing structure without a rebuild, which is exactly what a graph is for.
Is the knowledge graph a separate product?
No. In IntelliStream it is a built-in layer of the operational data platform, connected to time-series storage, events, and lineage from day one. The whole platform is open source under AGPL-3.0, so your team can read the code before you commit to anything.
What is asset dependency mapping?
The part of the model that records what depends on what: which equipment serves which process, which processes feed which reported figures, and which teams own them. It is how the graph turns a component failure into a list of consequences instead of a guessing game.
Can we run it on our own infrastructure?
Yes. Self-host it in your cloud, on-prem, or air-gapped. Or let us run it on our own hardware in Stavanger, Norway, 40 servers with 2 PB of disk, operated by the same Norwegian engineering team that builds the platform.
Get in touch
Curious what your data looks like as a graph?
Create a playground tenant, load your own data, and see the graph for yourself. A working environment in minutes, and no sales call to get there.