October 9, 2026, (Inside AI) — Shanghai-based Black Lake Technologies has raised nearly RMB 1 billion ($140 million) in Series D funding to scale its industrial AI agents globally, the company announced in April. The eight-year-old startup, which began as a blockchain and data analytics experiment, now sells AI that reads CAD drawings, plans manufacturing processes, and generates quotes for small and midsize factories across China, Southeast Asia, Latin America, and Eastern Europe.
The funding round, which values the company at an undisclosed figure, will primarily accelerate deployment of its AI agents and expand its international footprint. Black Lake says it has become profitable and that annual revenue is growing by more than 60%. The company operates a regional headquarters in Singapore and serves over 100 overseas customers in more than 10 countries, including Indonesia, Vietnam, and Mexico.
Black Lake's core product is a suite of industrial AI agents that handle tasks from drawing interpretation and process planning to quoting, scheduling, and quality control. Its CAD-to-Process Agent can analyze a drawing in about a minute, with company-reported accuracy above 95%, compared to hours of manual work. The company targets a gap left by traditional enterprise software: many small factories still rely on experienced engineers and veteran workers to make critical production decisions.
"Many factories his previous startup approached had essentially no data to analyze," founder and CEO Zhou Yuxiang recently told Fortune, describing the obstacle that led him to work on a factory floor himself. There he observed lengthy production changeovers, information bottlenecks, and decisions that depended heavily on individual experience.
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That experience shaped Black Lake's early focus on capturing production data and coordinating work. By 2019, its pitch centered on making factories more flexible as consumer demand shifted toward greater product variety. The company joined Microsoft Accelerator Shanghai after its Series A, working with Azure technologies, and experimented with blockchain for traceability and image recognition for quality control.
Today, the technologies have changed, but the underlying problem remains. Industrial decisions are less forgiving than chatbot conversations. A bad sentence can be regenerated; a bad process plan can waste material; a poor quotation can erase margins; and a scheduling mistake can delay an order.
Data quality remains a constraint. In a 2026 survey conducted with IndustryWeek, Boston-based Augury found poor data quality to be the leading barrier to AI maturity, cited by 47% of manufacturing leaders. Augury, which grew from machine-health and predictive-maintenance software, is extending its foundation with agents for reliability, maintenance, and operations. Other competitors include Tulip, which centers on configurable frontline applications, and Siemens, which is integrating AI across a broader engineering, automation, and industrial-software portfolio.
Black Lake comes from production-management software. Its AI products are built around factory workflows and production constraints, while its commercial strategy has increasingly targeted China's small and midsize manufacturers alongside larger factories and supply chains. The company has also extended beyond product design to distribution. Zhou has recently recruited food-delivery riders to help sell its AI products to factories, reflecting a push to reach smaller manufacturers that traditional enterprise software sales teams may not easily cover.
A recent example of its international expansion comes from Puebla, Mexico, where a Black Lake digital system was introduced at an automotive plant in 2026. Production practices vary; factories may already rely on mature enterprise resource planning (ERP), manufacturing execution system (MES), and automation vendors; data rules and integration requirements also differ by market. The experience accumulated in China's fragmented supplier ecosystem may be useful abroad, but it cannot simply be assumed to transfer intact.
Black Lake's path from 2018 to 2026 illustrates how industrial AI is moving closer to real production workflows. Eight years ago, the company focused on capturing and organizing factory data. Today, it explores how AI can use that data to support parts of industrial decision-making. The technologies have changed, from blockchain to AI agents, but many of the underlying questions remain. Manufacturers still need to turn experience and operational knowledge into information that software can use, determine where automated decisions can be trusted, and adapt those systems to different production environments.
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For Black Lake, the next phase may play out both at home and abroad, as the company explores how widely its industrial AI model can be applied across different factories, production environments, and markets.
