Enterprise ontology · Digital twin · Agentic AI · Monitoring
An ontology is the big-data knowledge model that lets AI understand your company the way a person does. Building one used to mean consultants and forward-deployed engineers drawing the domain model by hand for months. HyperEZ moved that step into a machine learning pipeline. Connect your source systems, the model is drafted with evidence attached, and our field analysis engineers lock it down against the real domain.
Built by HyperEZ · Powered by the Regnax LLM engine · Air-gapped deployment available
You end up with a running system, not a consulting deliverable. Live in steel and logistics operations since 2023, and selected as the AI transformation partner for ROK Air Force logistics systems.
References
HyperEZ ontology and digital twin work runs where nothing is allowed to stop: steel plants, logistics centers, airports. Defense is on that list too.
Official partner (CPN) 
Official technology partner 
ROK MND · Air Force Selected AI transformation partner, logistics systems Individual engagements stay unpublished under customer security policy. Ask us and we will put together the references closest to your domain.
What is different
Most of what an ontology project costs is labor spent drawing the domain model by hand from a blank page. Interviews, spreadsheets, months of arguing about entities and relations. At HyperEZ a machine learning construction pipeline and field analysis engineers split that stretch between them.
Connect your sources and Skein profiles the data, then produces candidate types, entities, and relations with evidence attached. The blank-page design workshop disappears and review is all that is left. This is where most of the 60% comes from.
Relations that follow deterministically from your records are handled by a rule engine. No token cost and no hallucination on that path. The LLM is reserved for places that genuinely need judgment.
What you get is screens and APIs, not a design document. The second build that normally turns the report into software, and the handover risk that comes with it, both disappear.
The hours that went into blank-page modeling go into validation, site-specific rules, and handover. People still make the calls, but the drawing is gone, so months on site drop and cost and timeline fall together.
| Stage | Traditional ontology build | Skein |
|---|---|---|
| Domain modeling | Consultant interviews and workshops, months | Pipeline drafts it, days |
| Validation | Repeated workshops, agreement by document | Yes or no per candidate in the approval inbox |
| Deliverable | Design documents, implementation is a separate project | A published ontology you can query immediately |
| Deployed engineers | On site for the whole program | Only for validation and rollout |
| Change | Redesign and a new contract | Incremental publishing on immutable versions |
The savings figure is our own estimate for equivalent scope and deliverables. We will size it against your actual scope on request.
The platform
Company operations split into four layers: what you know (Nexus), where it happens (Twin), what to do about it (Action), how it looks right now (Sense). All four stand on the same knowledge model, so answers, execution, and monitoring never disagree.
FDE service
We do not hand over software and leave. HyperEZ engineers open up your source systems, harden the auto-generated model against the real domain, and hand it to a team that can run it alone. Because the pipeline does the modeling, that engagement is short, which is why it is affordable.
We start by finding out what data actually exists across ERP, MES, PLC, and legacy systems. Where there is no modern interface, we build the connector.
We validate the pipeline draft with the people who do the work. Because nobody starts from a blank page, meetings end in confirmation rather than negotiation.
Agents and workflows get wired into the real process, and we train your team until they can change it without us. The deliverable is a screen in use, not a document.
For separated networks we bring our own LLM platform inside. It runs with no external API calls, and our ontology-based multi-LLM ensemble is patent pending.
How it works
Plug in your systems over MCP, or start with files, REST, or read-only SQL. Records land immutably with a fingerprint and a timestamp.
The pipeline profiles your data and produces candidate types, entities, and relations. Each candidate carries the records it came from.
Review only what matters in the approval inbox. It shows exactly what publishing will change, and confirmed versions are immutable.
Get cited answers, and let agents execute work through governed writes. Preview, approve, verify, audit. None of it is skippable.
Trust
Every assertion links back to its original record, with time and confidence. No unsourced answers.
High-impact and irreversible actions always require a human decision, with before and after preview.
Who, when, what, why, on which version. Tenant isolation, tool allowlists, and an instant kill switch.
Connections
Skein speaks MCP, files, REST, and read-only SQL, so it meets your systems where they are. It also exposes itself as an MCP server, so your own agents and tools can talk back.
Curated, tested MCP connectors for common tools. Self-serve setup in minutes.
Already run an MCP server for an internal system? Register it and scope its permissions.
Legacy ERP with no modern interface? HyperEZ builds and operates a custom connector for it.
AI engine
HyperEZ builds Regnax in house. It routes each task to the model that suits it and handles text, images, and audio on one path. Cloud by default, with a self-contained LLM platform for separated networks.
FAQ
Anything else, send it over. We answer within one business day.
The modeling stage drops from months to days. The pipeline produces candidate types, entities, and relations from your source data, so your team only approves. Total duration depends on how many source systems you have and how complex the domain is, so tell us the scope and we will size it.
It is our own estimate against the effort a traditional build takes for equivalent scope and deliverables. Most of the saving comes from the design labor that used to go into drawing the domain model by hand from a blank page. We will size it against your actual scope on request.
Both work on top of an ontology. The split is who draws the knowledge model. Foundry sends engineers to design it; Skein has a pipeline draft it and people approve. We can also bring the whole stack, LLM included, inside a separated network.
Yes. We bring the HyperEZ LLM platform inside, so it runs with no external API calls. Our ontology-based multi-LLM ensemble is patent pending.
MCP, file upload, REST, and read-only SQL. For legacy systems with no modern interface, such as older ERP, MES, CRM, or SCM, HyperEZ builds the connector.
Every answer carries per-sentence citations, and clicking one lands on the record it came from. Relations that follow from rules never touch an LLM. Anything that changes data becomes a proposal that a human has to approve before it runs.
A list of the source systems to connect, access to them, and one person who knows the domain. Our FDE engineers handle the rest on site.
Tell us the scope and what you run today, and we will size the build in time and cost.
