Solutions · Data governance
Govern the data first. Then AI is worth trusting.
One customer has three names across three systems; order status disagrees in two places. We govern data around the ontology: unify entities, declare who is authoritative for each field, check quality continuously and make every record traceable to its source.
ERP
Huadong Precision Ltd.
C-0192 · credit ¥500k
CRM
Huadong Precision
Contact: Mr Wang · 0592-…
Invoice
Huadong Precision Co.
Tax ID 9135…2X
Sound familiar?
Duplicate, inconsistent master data with several codes for one customer
Nobody can say which system is authoritative for a field
Quality problems are found only after a report is wrong
Sources and changes can't be traced, so audits are painful
How we govern
Inventory
Map data sources and key objects: customers, parts, suppliers, orders.
Set rules
Define field-level authority, validation rules and freshness targets.
Unify entities
Match the same entity across systems; uncertain matches go to human review.
Run continuously
Sync and monitor continuously; bad records go to a queue instead of disappearing.
What you get
Unified master data
Customers, parts and suppliers each have one identity in the ontology.
Field-level authority
Every field's source system and precedence is clear.
Entity review workflow
Match evidence, human decisions and history all retained.
Quality & freshness monitoring
Failed checks, delays and anomalies at a glance.
What you get
- One customer, one identity
- Every field has a known source and owner
- Bad data is stopped before it reaches reports and AI
- AI and BI run on the same trusted data
FAQ
Frequently asked questions
Yes. Data Pipeline accepts PDFs (including scanned pages via OCR), Excel, CSV and pasted data. Models extract and normalise them against a declared schema, so new layouts need no bespoke training, and results can be previewed and reviewed before they're published to the ontology.
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.