Cohort: 1 

Location: Shanghai, China 

Application Title: AI-driven coordination platform for flexible manufacturing 

Team Members: Yuxiang Zhou (executive sponsor), Yongqiang Ren, Xiaokang Pei, Yihang Cai, Qiuru Xiang

The challenge 

Manufacturing capacity is often poorly matched to demand. Large factories tend to focus on high-volume orders with narrow margins. Smaller factories frequently have idle capacity but lack access to customers or the ability to handle product design and onboarding requirements. 

At the same time, demand has shifted. Emerging brands and online creators increasingly want small production runs that can move quickly from concept to market. Traditional manufacturing systems are not designed for this. Product development relies on manual CAD work, experienced engineers and costly moulding processes. Manufacturing Execution Systems (MES) are expensive to deploy and slow to adapt, particularly for small and medium-sized factories that still operate with limited digitisation. 

As a result, production cycles stretch over many months, capacity remains underused and factories struggle to respond to fast-moving demand. 

The solution 

A coordination layer that links demand, design and production 

Black Lake Technologies developed an AI-driven platform that connects demand sources with distributed manufacturing capacity. 

The platform acts as a coordination layer rather than a replacement factory system. It links order intake, product design and production scheduling within a single workflow. Incoming orders are matched to suitable production lines based on capacity and constraints. This matching process, which previously required manual configuration, is completed in seconds. 

The platform supports both large and small factories. It enables smaller producers to access new demand while allowing larger manufacturers to handle short production runs without reconfiguring existing systems. 

How it works 

From order to production with minimal manual setup 

When a new order is received, the system maps product requirements to factory capabilities. It can process structured data as well as CAD files. Where designs do not yet exist, AI tools generate manufacturable 3D models from text prompts, applying constraints that avoid features that are difficult to produce. 

Production tasks are coordinated through a lightweight, smartphone-based interface rather than traditional MES software. Role-specific AI agents guide scheduling and workflow execution, reducing reliance on manual planning. This allows factories to adjust quickly as orders change. 

The system is deployed across a hybrid architecture that supports rapid onboarding and local execution, making it suitable for factories with limited IT resources. 

How it differs from conventional approaches 

Reducing dependence on rigid MES systems 

Traditional MES platforms are costly and difficult to customise. Changes often require months of configuration work, which limits responsiveness. 

Black Lake’s approach replaces fixed workflows with adaptable coordination. Instead of encoding every process upfront, the platform uses AI agents to handle design generation and production planning dynamically. Custom workflows can be deployed in days rather than months. 

This allows factories to handle smaller orders and faster cycles without investing in new infrastructure. 

Impact 

• Utilisation increased from 65% to 83% 
• Production cycles reduced from 6–12 months to 1–3 months 
• Minimum order size reduced from 10,000 units to 100 units 
• 18% improvement in capacity utilisation across deployed sites 
• Platform supports 330,000 daily active users and processes millions of production-related requests each day 

Why it matters 

Making manufacturing capacity more responsive 

Manufacturing systems built for scale often struggle with flexibility. Black Lake Technologies shows how AI-driven coordination can help bridge this gap by linking demand, design and production in real time. 

By lowering the cost of onboarding and reducing cycle times, the platform helps factories use existing capacity more effectively while supporting new business models based on smaller production runs and faster delivery.