Manual, error-prone design reviews and quality challenges
Design document reviews were performed manually, making them slow and inconsistent. In large-scale, multi-disciplinary projects, even minor missed changes between revisions often led to quality degradation, rework, and schedule delays. To improve reliability and efficiency, a scalable, automated review framework was required to standardize processes across disciplines.
AI-powered comparison and parsing for change detection
MakinaRocks implemented an AI-based pipeline that automatically identifies differences between existing and revised drawings. The agent detects geometric and dimensional inconsistencies across multiple engineering disciplines and leverages OCR and LLM-based parsing to extract and interpret structural data from diverse document types. Detected change items are presented through an intuitive interface for rapid validation, while continuous feedback enhances model performance over time. Final outputs are generated as standardized reports to ensure consistent documentation and traceability.
Speed, accuracy, and over 1,000 man-hours saved annually
The AI agent saves more than 1,000 man-hours annually, dramatically accelerating review cycles and improving accuracy. By automating repetitive and time-intensive review tasks, engineers can focus on high-impact design and decision-making activities. The result is greater productivity, higher-quality outcomes, and more reliable project delivery across engineering workflows.
