Customers can now see exactly what they are ordering before the garment is made.

Chef Works had offered embroidery personalization for over two decades. The challenge was not the capability itself but the absence of any way for customers to see the result before committing to an order. A buyer selecting an embroidered garment for their staff would specify a design, a placement position, a colorway, and any text or logo, then wait for delivery to see whether the outcome matched their expectation.

When it did not, the costs were absorbed across returns, reprints, customer service time, and the erosion of confidence in the ordering process. These were not edge cases. They were the accepted operating cost of running a personalization programme without visualization. The problem was structural, and it scaled with the business: the more personalized orders Chef Works fulfilled, the more exposure they carried from the gap between what a buyer imagined and what they received.

Chef Works' catalogue spans chef coats, aprons, headwear, pants, shirts, and footwear across men's and women's ranges. Any visualization system that required product-by-product implementation would cap the programme's potential before it reached scale. The requirement was not a configurator for some products. It was a rendering infrastructure for the full catalogue.

Purpose-built embroidery visualization tools exist. Chef Works evaluated the category. The structural limitation they found was consistent: tools designed around embroidery as a single output type handle embroidery well and nothing else. Chef Works needed a solution that could render embroidery accurately today and extend to other personalization types, other product categories, and other output channels — web, print, production file — as the programme grew. A vertical tool would have required a separate vendor for each of those extensions.

The technical requirements for embroidery visualization are also more demanding than a surface-level product comparison suggests. Designs must follow the contour of a garment, not sit flat against it. A logo placed on a chef coat reads differently depending on the fabric weight, the cut, and the position on the body. Colorway rendering has to be accurate enough that a buyer can trust the screen preview matches the finished garment — which requires simulating how each thread colour behaves against the fabric, not just displaying a flat colour swatch. Most embroidery visualization tools approximate these properties. LiquiFire OS, operating through the Wilcom API integration, addresses them at the level needed for production-quality output.

Chef Works engaged LiquidPixels Professional Services to lead the integration, working directly with their team to set up the embroidery designs and colorway definitions that allow accurate visualization across each product type. For an organisation whose personalization programme spans hundreds of product types, the implementation capability of the partner was as important as the technology itself.

The integration began with a custom prototype that mapped the full technical requirements before any production work commenced. This phase defined the specifications for connecting LiquiFire OS to the Chef Works ecommerce platform and established the scope of the initial rollout: the several hundred highest-volume products that account for the majority of personalized orders.

For each product in the initial phase, LiquidPixels Professional Services worked with the Chef Works team to define the embroidery design library, colorway definitions, and placement zones for each garment type. This required close collaboration with a Chef Works team member who had moved from a production embroidery role into a newly created ecommerce position specifically to lead the integration from the brand side. That pairing — LiquidPixels rendering expertise with deep embroidery production knowledge — was the mechanism that produced accurate design-to-garment mappings at scale.

The key architectural distinction in the implementation is the separation between screen representation and production output. When a buyer configures a personalized garment, LiquiFire OS renders a rasterized preview using simulated thread colours drawn from the colorway definition. When the order is placed, the same chain outputs a machine file — in DST or EMB format — that carries thread product IDs rather than simulated colours, so the production equipment receives an unambiguous instruction for which physical thread to select at each step. There is no manual translation between what the buyer approved online and what the embroidery machine executes.

Following the initial rollout, Chef Works structured three additional phases of advanced dynamic imaging development, with the objective of extending visualization coverage across the full catalogue.

Buyer response to the visual embroidery configurator was immediate. Customers who could select a colorway and see an accurate screen preview before committing converted at higher rates and returned products less frequently. The reduction in order errors was directly attributable to the accuracy of the preview: when buyers can see exactly what they are ordering — including the correct thread colours against the actual garment — the gap between expectation and delivery closes.

The operational improvement extended into the production workflow. Machine file output from the LiquiFire OS embroidery chain eliminated the manual re-entry step between customer configuration and production instruction. The thread product IDs embedded in each output file are the production instruction — there is no intermediary step where someone interprets the online order and re-enters it into a separate system.

Chef Works has subsequently committed to three additional phases of dynamic imaging development with LiquidPixels. The programme that began with several hundred core products is now structured to extend visualization coverage across the full Chef Works catalogue.