Ontoxiom · Enterprise Ontology Platform
AI thatunderstands your business / connects your systems / respects your boundaries / runs your workflows
Qihuan puts an enterprise ontology at the core: data from ERP, MES, WMS, CRM and documents becomes a business world AI can understand, query and act on, with every action inside permissions and audit.
Connects to the systems and data you already have. Nothing to replace.
- ERP
- MES
- WMS
- CRM
- SRM
- TMS
- OA
- PDF / scans
- Excel
- CSV
- REST API
- Webhook
- MCP
- Databases
Why an ontology
LLMs understand language. Not your company.
Enterprise data lives in dozens of systems as tables and fields. Pointed at a database, AI can't read the business or hold a boundary. An ontology reorganises it all into objects, relations and actions: the layer AI needs to reach production.
Without an ontology
- AI sees tables, columns and endpoints
- Every use case rebuilds its own context
- Permissions live in prompts; nothing is audited
- Demos are fast; production never arrives
With an ontology
- AI sees customers, orders and suppliers
- Model once; every use case shares the semantics
- Every read and write goes through a policy kernel
- Your first use case is already production
The Ontoxiom platform
Four engines. One path from data to action.
Data Pipeline keeps facts flowing in, the Ontology Building Engine defines your business world, the Data Engine makes it queryable and executable, and Harness lets people and agents work on top of it.
Turn every source into living facts in your ontology.
- Many systems, one set of business objects
- Freshness configurable per integration
- Every fact traceable to its source
- No need to replace existing systems
Typical value ranges
Measure AI by production results.
0–0 wks
Typical time to the first use case in production
0–0%
Reference range for reduced repetitive work
0%
Of AI reads, writes and actions in the audit trail
0
Existing business systems you need to replace
Reference ranges from typical deployments. Actual results depend on systems, data quality, business complexity and scope, and are not a guarantee.
The Four Laws
Essence before relation. Boundary before capability.
The Four Laws are our first principles for industrial AI, and the starting point for every layer of the platform.
FDE
AI adoption doesn't start in a meeting room.
Forward deployed engineers work on site with your business team to model, connect and launch, taking the first high-value use case into production.
Discover
Interview business and IT, map systems and data, shortlist AI use cases and form a value hypothesis.
Pilot
FDEs on site build the first ontology, connect key data and run one high-value use case in production.
Scale
Roll out Harness and governance policies, extend to cross-department use cases and set up enterprise-wide adoption.
Evolve
Capture skills and experience, keep improving as models advance, and let AI capability compound.
Open access
Bring the agent you like. Connect it to one enterprise ontology.
You don't have to use our app. Through MCP, REST and Python / Node SDKs, WorkBuddy, Claude, ChatGPT or your own agents can use the ontology directly, with exactly the same permissions and audit.
Use cases
Running in real operations.
Supply chain exception response
Continuously detect late orders, stock deviations and supplier issues, trace affected orders and customers through relations, and open tasks or replenishment approvals within permissions.
- Detect→
- Trace→
- Suggest→
- Approve
Cross-system business questions
“Which customers dropped more than 20% in sales this month while carrying excess stock?” Answered once across ERP, CRM and WMS, filtered by permissions and cited.
- Understand→
- Join→
- Filter→
- Cite
Intelligent trade documents
Extract fields from PDFs and spreadsheets, check cross-document consistency and classification history, and route differences to human review.
- Extract→
- Compare→
- Flag→
- Review
FAQ
Frequently asked questions
An LLM Wiki has a model compile sources into markdown pages it maintains itself; agents read those pages to answer questions. It's great for capturing personal or team know-how.
But a wiki is a model-written summary. It can be stale, it can't count or enumerate precisely, it has no field-level permissions and it can't execute business actions. Agents in Harness work on typed objects continuously synced from your systems, so answers are exact down to the individual order. Every read and write passes the Governor, and actions go through checks, approvals and a full audit trail.
They can coexist: the wiki holds the “how”, the ontology holds the “what” and the “do”.
Turn your business into a world AI can understand.
Bring one concrete business problem. We'll tell you honestly whether it makes a good first use case.