One Definition, Every System, Including the Next One
An ontology, a knowledge graph, and a semantic layer that hold one agreed set of meanings for your products, customers, and orders, across the systems you already run. Dashboards, applications, and agents all work from the same definitions.
Explore our other solutionsThe Challenge
Definitions are the part of your architecture nobody owns, which is why every project renegotiates it.
Fields that sound identical
"Available" means unreserved in one system, on the shelf in another, and orderable in a third.
Mappings rebuilt every time
Each new connection starts the mapping from scratch, and the definitions drift a little further apart.
Models that guess
An AI reading your data has no way to know that these two records are the same product in different regions.
Knowledge held in people
The business logic that makes your data usable is undocumented, and it leaves when they do.
Undocumented meaning was manageable while a person sat between the data and the decision, filling the gaps from memory. That person is now the bottleneck, and everything waiting behind them is a decision the business could have made. Writing the meanings down is what lets the queue move without them.
The Solution
Built on the platforms you already run: one agreed definition for each thing your business tracks, the connections between them, a single place every system gets its answers, and structure pulled from the content you already have.
Ontology Design
From Hidden Knowledge to a Written Model
We define your core entities and the relationships between them, working with the people who own each one. Products, customers, orders, inventory, pricing, promotions, entitlements. For every definition we name the system of record and the conditions under which it changes.
Your domain is where this gets hard. SKU variants, fitment, contract pricing, subscription state, and B2B account hierarchies are conditional rules, and generic schemas record them as fixed attributes. Modeling them properly is what lets a system answer a question nobody has to interpret afterward.
Entity modeling
Core objects defined once, with the attributes and states that matter to the business.
System of record
For every definition, the system the rest of the business follows when they disagree.
Domain depth
Modeled around how your industry works, down to the cases that break generic schemas.
Knowledge Graph
Let a Query Cross the Whole Business
A question that spans several systems needs custom work to connect each one. A knowledge graph already holds those connections, so a question like, "Which customers bought a product affected by this recall, in a region where we owe them a replacement?" can be answered with a single query.
The graph also gives AI agents a map to reason with. Instead of finding records that look similar to the question, an agent follows the connections to the exact record it needs.
One query, many systems
Questions that span systems, answered by following the relationships between them.
Cross-system reach
Relationships that move across platforms, without moving data off its source.
Grounding for retrieval
A structure that agents and search can follow, so they get complex questions right more often.
The Semantic Layer
One Vocabulary, Applied to Every Question
The semantic layer is where the ontology becomes operational. A query arrives, the layer knows which system holds the answer and which definition applies, and returns a consistent result. Definitions are versioned, so when one changes you can see which systems and reports depend on it before the change ships.
This is what keeps the next tool you buy working from your definitions. Dashboards, applications, models, and agents all resolve against one vocabulary, so they agree by design and nobody reconciles afterward.
Query resolution
One place that knows where an answer lives and which definition governs it.
Consistency by construction
Reports, applications, and agents agree because they resolve against the same layer.
Changes that carry through
When a definition moves, the layer knows what downstream needs to know.
Enrichment & Processing
Pull Structure Out of What You Already Have
A large share of what your business knows sits in specifications, manuals, support threads, and product copy. We build content processing that handles structured records and unstructured content in one pipeline, extracting entities, attributes, and relationships as it goes.
Metadata, tagging, summaries, and embeddings are created at ingest, so content arrives usable and stays that way as more of it lands. When the ontology changes, the pipeline reprocesses what is already indexed, so the model and the content never drift apart.
Entity extraction
Products, parts, and attributes pulled out of documents and copy automatically.
Enrichment at ingest
Tagging, summaries, and embeddings created as content arrives.
One pipeline
Structured and unstructured content handled together, governed the same way.
Start From the Questions That Cross Systems
Talk through one question your business asks constantly, and the entities it has to touch to answer it.
Outcomes
Written-down meaning is the piece every project after it inherits. Each new system, report, or agent starts from definitions that already exist, instead of paying again to work out what the data means.
Fewer handoffs to get an answer
Questions that needed someone who knew the definitions, answered without waiting for them.
Smaller, faster integrations
New systems connect to one vocabulary and skip the mapping exercise with every neighbor.
One number, one meaning
Two teams pulling the same figure get the same answer, and the meeting moves to the decision.
Knowledge that outlasts the people
Business logic written into the architecture, where it survives the team that knew it.
How Orium Helps
Our engineers work inside your team, and the ontology, the graph, and the semantic layer are yours, documented so your people can evolve them without us.
Domain Modeling
Facilitated sessions with the people who own each definition, ending in a documented ontology.
- Core entity and relationship modeling
- System of record assignment per definition
- Commerce-specific modeling for variants, which parts fit which products, and account hierarchies
- Contract pricing, entitlement, and subscription state modeling
- Conflict resolution where two teams hold different definitions
Graph Engineering
Build the knowledge graph and the pipelines that keep it current as systems change.
- Knowledge graph design and implementation
- Relationship ingestion from source systems
- Event-driven updates through your existing streaming layer
- Traversal patterns for retrieval and agent grounding
- Performance design for cross-system queries
Semantic Layer Build
Stand up the query and resolution layer over your existing platforms.
- Semantic layer implementation over your existing platforms
- Query routing and definition resolution
- Change propagation to downstream systems
- Integration with BI, applications, and agent surfaces
- Versioned definitions with dependency tracking downstream
Governance & Stewardship
The model stays current as the business changes, with or without us in the room.
- Definition ownership and stewardship model
- Change control and versioning for the ontology
- Documentation your team maintains without us
- Enablement sessions for modelers and engineers
- Ongoing evolution support as the business changes
Define It Once. Every System Inherits It.
Talk with our team about the definitions your systems disagree on, and how much of your roadmap is waiting on settling them.
More Solutions
AI & Data Readiness
Learn moreFind out where your systems disagree and which of your AI plans the data can support today.
Search & Grounded Answers
Learn moreOne place for people, assistants, and agents to get answers, with sources cited and access controlled at the source.
Governed Data Products
Learn moreTurn contested extracts into owned assets with a contract, a named owner, a refresh schedule, and traceable lineage.
