Commercial Kitchen Drawing Generation Automation

The Problem

Gaylord Industries faced a significant bottleneck in their pre-sales and engineering process. Generating detailed commercial kitchen layouts for proposals was a manual, time-consuming task performed by skilled drafters. As demand grew, the sheer volume of drawing requests led to delays, inconsistent standards across the team, and increased pressure on engineering resources.

• High volume of repetitive drafting tasks consuming valuable engineering time.
• Inconsistencies in drawing standards depending on which drafter worked on the project.
• Lack of visibility for sales teams on the status of their drawing requests.

Constraints & Challenges

• Complex Geometry: Commercial kitchens vary wildly in shape and size; the automation needed to handle non-standard room layouts intelligently.
• Accuracy is Critical: These drawings are used for manufacturing and installation; errors in dimensions could lead to costly rework.
• Integration: The solution needed to fit into their existing workflow without requiring engineers to learn entirely new software ecosystems.
• Legacy Data: Leveraging existing block libraries and standards was essential to maintain continuity.

Our Approach

We treated this as a systems engineering problem, not just a scripting task. We decoupled the logic (what needs to be drawn) from the execution (the drawing process itself).

• Structured Inputs: implemented a JSON-based input format to capture requirements from the sales/specification team, eliminating ambiguous hand-written notes.
• Automated Logic Core: Built a rule engine that calculates placement, clearances, and component selection based on the input parameters.
• AutoCAD Automation: Developed robust scripts to drive AutoCAD to generate the geometry, layer management, and annotations automatically without human intervention for the initial draft.
• Tracking Portal: Built a web-based dashboard where sales reps could request drawings and track their status in real-time.

What Was Automated

Impact

Before vs After