Make the Dot is emerging as one of the most practical AI wins in fashion, with LF Distribution Holding Inc., a Li & Fung company, using the platform to cut design timelines dramatically while boosting buyer engagement across global retail partners. The collaboration shows how targeted, workflow-native AI can help brands move from idea to production faster, with fewer samples and more commercial-ready concepts.
How Make the Dot Is Changing Design
Make the Dot is an AI-powered collaborative fashion design platform built specifically for fashion teams rather than generic image generation. Its tools help designers move from moodboards and concept sketches to ultra-realistic, production-ready visuals on a single shared digital canvas.
By integrating research, ideation, prototyping, and line sheet creation, Make the Dot reduces the need for early physical samples and shortens time-to-market for brands and retailers worldwide. The platform also supports real-time collaboration and supplier integration, making it easier for teams across countries and time zones to align on final product direction.
Li & Fung’s Results: From Days to Hours
Since adopting Make the Dot, Li & Fung’s design team has reported striking productivity gains. According to the company, research time dropped from one day to just one hour, and line sheet creation was reduced from seven days to one, freeing designers to spend more time refining ideas instead of fighting workflows.
These speed gains have also translated into stronger commercial performance. Li & Fung notes that buyer engagement has increased thanks to higher-quality AI visuals that are closer to final product execution and easier for retail partners to evaluate and buy into.
AI That Supports, Not Replaces, Designers
Inside Li & Fung, the team behind this rollout sits within an AI-native innovation group focused on embedding intelligent tools into every stage of the product lifecycle. Mel Limoncelli, SVP, Head of Licensed Brands – Apparel at Li & Fung, said, “As an AI-native team within LF, we’re pushing creative boundaries by embedding AI into every stage of our process… Make the Dot has become one of the tools that help drive success for us today.”
Far from replacing jobs, the implementation is described as a growth driver. With repetitive tasks compressed from days to hours, design teams at Li & Fung have been able to sharpen their creative concepts, launch new brands, and pursue fresh initiatives that would have been hard to prioritize under older, slower workflows.
Built for Fashion, Not Generic AI
Emilie Ho, CEO and Co-founder of Make the Dot, said, “We created Make the Dot with fashion designers at the center of our product vision… Our goal is to deliver tools that are optimized for design teams, allowing them to go from idea to production faster.” Unlike general-purpose AI tools, the platform is tuned to fashion-specific needs, from fabric behavior and fit considerations to merchandising-ready visuals.
This industry focus helps brands and sourcing partners cut waste by reducing unnecessary samples and tightening decisions earlier in the calendar. It also supports more sustainable creation, as teams can test and refine multiple directions digitally before committing to physical developments.
What This Means for Fashion Supply Chains
For Li & Fung, which connects major retailers and brands to over 15,000 suppliers across more than 40 markets, AI-native tools like Make the Dot are part of a larger digital supply chain transformation strategy. The aim is to help partners optimize inventory, reduce costs, and improve service while responding faster to shifting consumer demand worldwide.
As Mel Limoncelli said, “You can’t just plug in an AI tool and expect transformation. You need to align the right technology with your clients’ needs and the team’s workflows. With Make the Dot, we’re seeing real, measurable results.” For fashion brands and retailers, this case study signals a clear direction: the next edge in speed and growth will come from specialized AI that fits seamlessly into how design teams already work.
