Revolutionise Garment Costing with AI

GSDQuest is a powerful AI-driven tool designed to transform how garment costing is done. It analyses product images to automatically identify design construction elements and uses the proprietary QED Library to instantly generate a standardised Bill of Labour – removing the need for manual input.

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Even with internal libraries, teams spend hours analysing designs, referencing standards, and building labour estimates.

GSDQuest automates this entire workflow – adding speed, accuracy and scalability early in the product development process as a part of GSDCost.

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What inputs are needed (e.g., product images)?

Multiple product images is uploaded by the user

· How does it use AI and the QED Library?

It uses AI to detect visible and hidden features in the image and maps them to construction methods from the QED Library to generate the Bill of Labour

· What is the process flow—from image to Bill of Labour?

Upload images → AI identifies features → use construction methods from the QED Library → Instant generation of standardised Bill of Labour

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✓ Automatic feature recognition from images

✓ QED Library integration

✓ Instant generation of standardised Bill of Labour

✓ Hidden feature detection

✓ Scalability across categories

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· What does the manual process involve?

Interpreting complex design features, selecting construction methods, verifying time standards and using multiple spreadsheets or systems

· How much time and effort does GSDQuest save?

Costing time is reduced from hours to seconds

· What accuracy or standardisation improvements does it bring?

Consistent use of GSDCost codes across teams and suppliers; standardised Bill of Labour generation

✓ Time savings (e.g., hours → seconds)

✓ Improved margins and profitability

✓ Consistency across teams 

✓ Faster product development cycles

✓ Reduced human error and better data quality

Fashion Brands and Manufacturers

GSDQuest follows a four-step process. First, the user uploads one or more product images flat lays, tech packs, or style photos into GSDCost. Second, computer vision AI scans the images to detect both visible and hidden garment construction features, including seams, stitching types, closures, pockets, and trims. Third, the detected features are automatically matched to validated construction methods stored in the QED Library. Fourth, a standardised Bill of Labour and accurate SMV are generated automatically, ready for costing, negotiation, or vendor submission. The entire process takes seconds, compared to the hours required by traditional manual methods. 

Traditional garment costing relies on individual engineers interpreting designs, selecting methods from memory or experience, and entering data manually, all of which introduce variability and error. GSDQuest eliminates these failure points by automating feature detection and standardising method selection through the QED Library. The result is consistent, repeatable costing output that does not vary by person, shift, or location.

No. GSDQuest is designed to enhance, not replace, existing GSDCost workflows. It automates the front-end task of method analysis and Bill of Labour creation, but the output feeds directly into GSDCost’s standard costing engine. Existing GSD-trained industrial engineering teams, costing templates, and data standards are preserved the tool simply removes the manual effort required to populate them. 

Yes. Hidden feature detection is one of GSDQuest’s differentiating capabilities. Beyond what’s immediately visible in a product photograph, the AI uses contextual pattern recognition to infer construction methods that are typical for a given style, category, or visible design detail  ensuring the Bill of Labour reflects the full manufacturing process, not just surface features.

By generating costing data in seconds rather than days, GSDQuest compresses one of the longest stages in the product development timeline. Design teams and buyers receive cost feedback almost immediately after a style is submitted, enabling faster iteration, earlier go/no-go decisions, and shorter overall development cycles all critical advantages in a fast-moving fashion market.

GSDQuest leverages Coats Digital’s QED Library a proprietary knowledge base of validated, standard garment construction methods as the third step in its four-step process. After the AI detects visible and hidden features in an uploaded image, those features are automatically matched against the QED Library’s stored construction methods rather than left to manual interpretation. This is what allows GSDQuest to output a Bill of Labour using standard GSD codes, keeping its results aligned with the same GSD methodology that underpins GSDCost.

Yes. Because GSDQuest generates a standardised Bill of Labour using GSD codes, both manufacturers and brands are working from the same language and data baseline. This removes ambiguity from cost negotiations, accelerates buyer approval processes, and builds greater trust between supply chain partners. Brands can validate vendor cost submissions more quickly; manufacturers can substantiate their quotes with objective data.

GSDQuest maintains consistency by removing manual interpretation from the costing process entirely. Because every image is analysed against the same QED Library of validated construction methods using the same AI model, two different users uploading the same garment image get the same detected features, the same matched construction methods, and the same Bill of Labour  output doesn’t vary by who runs it, unlike manual costing, which depends on each engineer’s individual experience and judgement.