How AI Automated Repetitive Isometric Verification with 99% Validation Accuracy
A global EPC organization used contextual engineering intelligence and agentic AI to automate isometric drawing validation against P&IDs, Line Designation Tables and piping specifications.
Result at a Glance
Project Overview
The Challenge
Isometric drawings had to be manually validated against multiple engineering documents before issue.
Individual isometrics passed through 2 to 15 revision cycles, requiring teams to repeat the same verification activities whenever a drawing or its source information changed.
Key Challenges
- Repetitive manual validation of isometric drawings
- Inconsistencies between drawings and reference information
- Repeated checks across 2 to 15 revision cycles
- Significant time spent cross-referencing documents
- Limited automation within the quality-control workflow
- Difficulty maintaining traceability to source data
Before Automation
P&ID + LDT + PMS + ISO → Manual Cross-Checking → Engineering Review → Revision → Repeated Verification
Every revision created another cycle of manual comparison, review and validation.
The Solution
Drishya AI digitized the relevant engineering documents and built a computable contextual model connecting piping systems, equipment tags, routing logic and specifications.
Agentic AI validation was embedded within the isometric quality-check workflow, enabling automated checks whenever engineering information changed.
The Solution Included
- Digitization of engineering source documents
- Contextual modelling of connected engineering information
- Automated cross-document comparison
- AI-enabled isometric validation
- Pass, fail and query classifications
- Event-driven quality checks
- Traceability between isometrics and source data
How It Works
The system connects relevant engineering information within a computable model. Agentic AI
validates the isometric drawing and classifies each check as passed, failed or requiring engineering review.
| Before | After |
|---|---|
| Manual cross-checking | Automated validation |
| Disconnected documents | Contextual engineering model |
| Repeated revision checks | Event-driven quality checks |
| Variable review consistency | Standardized validation |
| Manual discrepancy identification | AI-generated results |
| Limited traceability | Connected source-data references |
Business Impact
99% Validation Accuracy
The automated quality checks achieved 99% validation accuracy while reducing repetitive engineering work and improving verification consistency.
The Results
- Achieved 99% validation accuracy in automated checks
- Reduced manual engineering effort
- Reduced verification time across revision cycles
- Improved the consistency of isometric quality checks
- Strengthened traceability to underlying engineering data
- Enabled automated checks following engineering changes
- Created a scalable foundation for AI-enabled engineering assurance
Key Takeaway
Contextual engineering intelligence and agentic AI automated repetitive isometric quality checks while achieving 99% validation accuracy and improving engineering traceability.
