QA Automating

AI-Driven Document QA for a UK Construction Firm

Implement an AI-driven Document QA solution that automates revision checks, verifies naming conventions, and ensures metadata alignment across EDMS platforms.
This solution reduces rework by up to 90% and cuts manual verification time by 50%.

Pain Points:

Pain Points:

•Frequent manual checks lead to wasted time and human errors.

•Discrepancies in revision sequences and naming conventions cause confusion.

•Mismatched title blocks vs. EDMS metadata create compliance risks.

•Delayed issue detection triggers rework and cost overruns.

•Inconsistent QA procedures hamper efficient collaboration and approvals.


How We’re Solving It:


1-Automated Document Validation

Our AI engine interfaces with major EDMS platforms (e.g., Asite, Autodesk Construction Cloud) to scan incoming or updated files. It confirms naming accuracy, checks revision consistency, and verifies the correct “purpose of issue.”
Any deviations are flagged for immediate review.

2-Metadata Synchronization

The system inspects each title block for proper naming, dates, and references (e.g., client details, project names). For CAD deliverables, it can also verify layering conventions and compliance with specific design standards

3-Smart Title Block Checks

The system inspects each title block for proper naming, dates, and references (e.g., client details, project names). For CAD deliverables, it can also verify layering conventions and compliance with specific design standards

4-Revision Workflow Optimization

An AI-based checker confirms that revision sequences follow the designated order—such as ensuring P03 logically follows P02.
If there’s a mismatch, the relevant team member is alerted immediately, eliminating guesswork and delay.

5-Cross-Platform Compatibility

We not only validate standard documents but also integrate with Tekla and Revit to automate best-practice connection settings. This yields faster approvals and reduces the chance of errors in structural details

6-Predictive Reporting and Dashboards

After each batch of uploads, the system compiles QA metrics, such as the number of flagged documents and most common errors. Project leads can access these insights through a user-friendly dashboard, which encourages continuous improvements

Technologies Used:

Technologies Used:

Machine Learning for Document Intelligence: Automates name checking, revision validation, and metadata alignment.

Natural Language Processing: Interprets textual elements in title blocks to ensure standard formatting and phrasing.

EDMS Connectors: Secure APIs for Asite, Autodesk Construction Cloud, and other platforms, enabling real-time updates.

CAD Integration: Dedicated adapters for Tekla and Revit that verify structural connections and cross-check design elements.

Dynamic Dashboards: BI tools that visualize error rates, time savings, and other QA performance metrics.


Conclusion:

Conclusion:

By automating routine QA tasks, our solution can potentially shorten assurance processes by up to 50% and reduce errors by as much as 90%. We also estimate that such efficiencies could save the client tens of thousands of dollars each year on rework and manual verification.

The system’s capacity to handle multiple file formats keeps CAD and document workflows aligned, decreasing compliance risks and preventing common data inconsistencies. Ultimately, this targeted AI approach transforms day-to-day operations, delivering agility, cost savings, and a foundation for ongoing digital innovation within construction projects.


For more information on the technologies used, please follow the link

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