Manual bottlenecks and high reliance on expert experience
Quotation calculation based on engineering drawings is a critical process that involves determining manufacturability and calculating labor hours and costs. Traditionally, this workflow relied heavily on manual reviews and the subjective experience of individual staff members. Without a systematic way to manage and compare similar historical cases, teams were burdened by time-consuming, repetitive reviews. Furthermore, the lack of standardized criteria for labor and cost calculations led to significant discrepancies between staff members, resulting in chronic processing delays and an increasing operational burden that hindered overall business efficiency.
Building an automated quotation system driven by drawing structure intelligence
To address these challenges, we implemented an AI-powered automated quotation system that parses 2D and 3D drawings alongside related documents, converting them into structured, AI-ready data. The system automatically determines manufacturability by performing deep similarity analysis with past drawings. It is designed to apply standardized logic for calculating labor hours and costs based on these historical matches, while also providing variance analysis to explain the specific reasons for cost fluctuations. This established a unified, AI-based process that spans the entire pipeline—from initial drawing upload to final assessment and quotation generation.
67% Faster reviews and 50% shorter lead times
The implementation improved the quoting process significantly, delivering measurable business value. The time required for drawing reviews and production feasibility assessments was reduced by 67%, which successfully shortened the overall lead time for delivering final quotations by 50%. Most importantly, by standardizing production criteria and cost calculation benchmarks, we minimized human variation and ensured a consistent, reliable quotation system across the organization.
