What is DrawingHub?
Helping asset owners regain trust in your engineering drawings.
DrawingHub is a productised service that de-duplicates, extracts, validates, and improves engineering drawing metadata, so your drawings are findable, and ready to support better operational decisions.
Impact
The impact we have is to help you establish a trusted relationship with your engineering drawings. Engineering drawings are probably not a critical control in your business. But your isolation system relies on them. Your safety system also needs trusted engineering drawings to do its job. So does your asset planning and execution. People and future intelligent systems (your AI) rely on this information to do their job well.
How it fits
DrawingHub operates as a plug-in process inside your existing environment, improving drawing data quality across the lifecycle, from ingestion through to ongoing management in the DMS. It is designed to integrate with existing document management systems and consulting-led delivery models.
DrawingHub in detail
The problem
Asset-intensive organisations depend on findable drawing data to plan work, execute, maintain their assets, and stay compliant. Systems like Accruent Meridian, RedEye, ProjectWise, and TeamBinder manage storage and access well, but they depend on the quality of the underlying data, and that data is often the weak point or delivered with bespoke tools:
Inconsistent or missing drawing titles
Unclear or incorrect revision information
Poor metadata quality across large drawing sets
In mining, energy, utilities, and manufacturing, poor drawing data feeds straight into safety, maintenance planning, and compliance risk.
What DrawingHub does
A controlled, multi-stage workflow that improves drawing data quality at scale:
- 01
Ingestion. Drawings are securely ingested from the source, then registered, tracked, and prepared for processing.
- 02
Automated extraction and validation. AI extracts key fields (drawing title, drawing number, revision and history) and applies validation rules. High-confidence results progress automatically.
- 03
Tiered human-in-the-loop validation. Where confidence thresholds are not met, items are escalated to structured human review built for consistency, accuracy, and traceability.
- 04
Validation tooling. Bulk validation across large datasets, field-level review and correction, and a full audit trail of every change and decision.
- 05
Output and integration. Validated metadata is exported back into your document management systems, enhancing the data they already rely on.
The result is a more consistent, reliable, and usable drawing dataset, with no changes required to your core workflows.
Security and data handling
Security is treated as a foundational principle, not an add-on.
Your data stays yours. All customer data remains the property of the customer.
Never used for training. Customer data is not used to train models.
Minimal exposure. Full engineering drawings are never sent to public AI models. AI processing is applied in a controlled way that minimises data exposure.
Isolated. Customer data is logically isolated, with no cross-customer sharing.
Data residency. Stored and processed in the AWS Asia Pacific (Sydney) region, Australia.
Controls. Encryption in transit and at rest, plus audit logging of system activity.
Outcomes
- Improve confidence in engineering drawing data
- Reduce risk from incorrect or incomplete information
- Enhance audit readiness through traceable data handling
- Standardise metadata across large drawing sets
- Scale data quality improvement efficiently
