Proven Case

BlueprintLegacyAI Case Study: Manufacturing Companies

PROBLEM

Customer Challenges & Requirements

Industries

Fasteners, hardware components, fluid power components, paper manufacturing equipment, and related manufacturing sectors

Customer Requirement

Quickly search historical design drawings and quotation data to accelerate quotation and process planning.

Challenges Before Implementation

Design drawings were scattered across different locations and formats, including PDF, DWG, and image files, making them difficult to search and reuse. Quotation and pre-production preparation also relied heavily on experienced engineers.

SOLUTION

NexusAI Solution

By implementing BlueprintLegacyAI, the customer transformed scattered drawing data into intelligent, searchable engineering knowledge—accelerating both quotation and production preparation.

Build a Unified Engineering Drawing Database

Integrate original drawings, processed drawings, and manufacturing process information into a structured and comparable design dataset.

AI-Powered Similar Drawing Search

Use AI modeling to quickly identify similar designs and related manufacturing information, providing process and quotation recommendations.

Integrate with ERP / MES for Faster Deployment

Connect drawing search and comparison results with existing manufacturing systems to shorten the transition from engineering analysis to production.

RESULT

Results & Business Impact

80%+ Reduction in Drawing Search Time

Reduce drawing search and comparison time from 15 minutes to under 3 minutes, while increasing reuse of existing designs.

25%–50% Improvement in Quotation Efficiency

Automatically generate quotation structures and reference process information, reducing manual effort in preliminary quotation preparation.

Estimated 3%–8% Increase in Order Margins

Reduce design rework and unnecessary engineering effort, helping improve estimated order margins.

50% Faster Production Preparation

Automate drawing feature analysis and process matching to streamline the path from order intake to production launch.

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