Seebo, a company designing manufacturing tools that predict and prevent industrial disruptions, has raised $ 9 million. A spokesperson says the funding will be used to further develop Seebo’s AI technology and expand its roster of clients.
Due to inefficiencies in the production process, large manufacturers suffer tens to hundreds of thousands of dollars in losses each year. Companies lose 20% to 30% in revenue due to inefficiencies alone, according to IDC. Seebo aims to help solve this with predictive algorithms that recommend remediation steps.
Seebo’s platform integrates manufacturing processes with AI and machine learning. Leveraging an enterprise-tailored approach to feature engineering, it translates data from these processes into visual insights delivered to operators and shift managers, quality control and maintenance engineers, and management.
It’s a pivot from Seebo’s business model four years ago, which offered companies an end-to-end platform to design, validate, and launch smart devices using a set of drag-and-drop tools. Although Seebo achieved a measure of success in industries ranging from health and wellness to fashion, it recently broadened its focus to address a greater range of industrial use cases.
Seebo adopts a “digital twin” approach to simulation — an approach that has gained currency in other domains. For instance, London-based SenSat helps clients in construction, mining, energy, and other industries create models of locations for projects they’re working on, translating the real world into a version that can be understood by machines. GE offers technology that allows companies to model digital twins of actual machines and closely track performance. Oracle has services that rely on virtual representations of objects, equipment, and work environments. And Microsoft itself provides Azure Digital Twins and Project Bonsai, both of which model the relationships and interactions between people, places, and devices in simulated environments.
But Seebo claims its customers — which include Barilla, Nestle, Mondelez, PepsiCo, Allnex, and Volkswagen — can create digital twin prototypes in record time, often within two weeks or less. Seebo also says its solutions take a more holistic view than most, aggregating data from the production line (including from automated quality inspection systems) and applying process-based AI to predict and help prevent issues that drive scrap and rework.
Seebo accounts for production flows and raw materials, in addition to the products actually being manufactured. It creates a virtual map of production lines to contextualize predictive alerts, events, and historical data. Employing predictive simulation allows process engineers to simulate how a production process will behave in different scenarios (and whether process inefficiencies will be avoided).
Seebo says that for one manufacturing customer, it was able to trace broken wafers to high oven temperatures and abnormal jumps in conveyor belt speed. Based on this, the company’s operational team used Seebo’s products to create quality alerts to avoid blockages and maintain quality standards.
“Manufacturers today realize that in order to prevent losses and continuously master complex production processes, they need a technological solution that truly understands the unique complexity of their production lines and is both easy for production teams to use and scalable across various manufacturing lines,” said Seebo CEO and cofounder Lior Akavia in a statement.
Ofek Ventures led the $ 9 million investment in Seebo, with participation from Vertex Ventures and existing investors Viola Ventures and TPY Capital. The round brings the company’s total raised to $ 31 million.