Ontology Design & Modeling
We design the object model, the parts, orders, patients, and signatures, that becomes the operational graph your teams work from.
Most companies don't have a data problem. They have a modeling problem. The parts sit in the ERP, the work orders sit in a scheduling tool, the quality holds sit in somebody's spreadsheet, and nobody can answer a question that crosses all three without a week of chasing people.
An ontology fixes that by naming the real things your business runs on. A part. A work order. A patient. A signature. Then the links between them, and the actions a person or an agent is allowed to take. Once that exists, every app, dashboard, and agent you build reads from the same model instead of inventing its own.
We do this with your operators in the room, not from a requirements doc. The model is wrong the first time. It gets less wrong every two weeks.
How those objects connect, and what a person or an agent can actually do to them. That model is your ontology: the map every decision ties back to.
Object modeling
We name the real things: parts, orders, patients, claims, sites, signatures. Not tables and not tickets. The nouns your operators already use on the floor, defined once so six systems stop disagreeing about them.
Link design
Which order consumed which lot. Which inspection covers which asset. Which invoice belongs to which facility. The links are where the answers live, and they are usually the part nobody ever wrote down.
Action design
An action is a governed write. Release the hold, approve the invoice, reassign the crew. Each one carries who is allowed to run it, what it validates before it commits, and what it leaves behind in the record.
Permissions and lineage
Row and column level access on the same model your apps and agents read from. Every value traces back to the system it came from, so when someone challenges a number you can show them where it started.
Modeling what already exists
You already have a data model. It's just spread across six systems and a lot of tribal knowledge. We map what's really there before we change anything, including the parts that only exist in someone's head.
Simulation and what-if
Once the graph is real you can branch it. Change a lead time, pull a supplier, move a crew, and see what it does downstream before you do it for real.
Narrow first. Checkpoint often. Nothing you can't walk away from.
Discovery
A short sprint with your operators. We map the use case, the objects, and the data you actually have, not the data the diagram says you have.
First build
We pick the thinnest slice a real person can use on a real day, and ship that. Production-ready on the first release, not a prototype we promise to harden later.
Every two weeks
A checkpoint and a decision. You see working software, you tell us what is wrong, we adjust. You are never locked into the next phase.
Handover
Documentation, runbooks, and your engineers in the repo while we build. If we disappear, the thing keeps running.
What you're handed
A published object model, objects and links and actions, versioned in your environment
Governed datasets behind every object, with lineage back to source
An action catalog with validation rules and permissions per role
Working sessions documented, so the model has a why attached to it
A migration path from the systems you are keeping
Same discipline, different floor.
- ManufacturingParts, vendors, orders, and production runs modeled as one graph instead of four systems that don't talk.
- HealthcareFacilities, contracts, invoices, and clinical events linked so finance and operations argue from the same record.
- Supply ChainShipments, lots, carriers, and exceptions connected end to end, so a delay has a traceable cause.
- GovernmentFilings, signatures, batches, and reviewers modeled to survive an audit or a legal challenge.
- Financial ServicesCounterparties, positions, and documents tied together with lineage on every field.
Shipped, running, and measured.
Trailer Manufacturing ERP
FactoryOS
Ontology-driven inventory and production workflow connecting vendors, purchasing, and build batches on Foundry & AWS.
157K+
Parts Governed
21K+
Manufacturing Orders
8K+
Units Annually
1,800+
Vendors Integrated
Life Sciences Data Platform
Clinical Trial Analytics
Ontology-backed analytics platform unifying clinical trial data across sites, enabling real-time enrollment tracking, adverse event monitoring, and protocol compliance at scale.
12
Trial Sites
45K+
Patient Records
60%
Faster Reporting
3x
Query Resolution
Revelation, not reinvention.
We've built this before. These are deployable pieces we bring in on day one instead of billing you to write them again.
Tell us what's breaking. If we're not the right team for it, we'll say so and point you somewhere better.