As manufacturers navigate rising production demands, supply chain complexity and increasingly fragmented procurement systems, London based AI company Magentic is betting that software agents can take on much of the operational workload. The company has raised $18 million in Series A funding, just over a year after launching, to expand its AI digital workers across procurement and supply chain operations.
Major funding round
The Series A round was led by Felicis, with existing investors Sequoia Capital and The Westly Group also participating. Magentic said the new capital will accelerate development of its AI agents, broaden the range of procurement and supply chain workflows covered by the platform, and support research into AI systems capable of solving complex industrial optimisation problems.
Magentic was founded by Robin Van Aeken and Odhran O’Donoghue, with experience spanning McKinsey and OpenAI. The company launched in July 2025 and is based in London and New York. Its platform is designed specifically for global manufacturers and procurement teams dealing with large volumes of data, suppliers, contracts and purchasing decisions.
Digital workers for procurement
Rather than functioning as another software dashboard, Magentic’s digital workers, known as Mages, operate within the systems companies already use. The company describes them as advanced multi agent systems capable of carrying out procurement and supply chain work from start to finish.
The agents can work across enterprise resource planning systems, spreadsheets, contracts, invoices and other business information. Magentic’s platform transforms fragmented procurement information into structured data that its agents can use to identify issues and take action.
The technology can be applied to both direct and indirect spending. Direct procurement covers materials, components and other inputs that contribute to manufactured products, while indirect procurement includes operational purchases that support the wider business.
Automating complex decisions
Magentic’s agents are designed to move beyond simple rule based automation. They can analyse purchasing activity, compare transactions with supplier agreements, identify pricing discrepancies, track contract obligations and help teams recover value that might otherwise be missed.
Its use cases include invoice reconciliation, contract analysis, discount tracking, supplier data validation and spend compliance. The platform can also identify purchases made outside approved contracts and help route them toward the correct supplier or pricing arrangement.
The company says its AI workers can take on complete tasks rather than simply flagging problems for employees. This includes reviewing spend, preparing supplier requests, identifying discrepancies and assembling evidence for recovery cases.
Designed for complex industrial environments
Manufacturing companies often operate across a mixture of legacy enterprise software, spreadsheets, supplier databases and other systems. Magentic is designed to sit on top of these existing tools rather than requiring companies to replace their technology infrastructure.
Security is another focus as manufacturers increasingly consider allowing AI agents to interact with sensitive enterprise systems. Magentic says it is SOC 2 Type II and ISO 27001 certified, GDPR compliant and aligned with the EU AI Act. Its security controls include zero data retention arrangements with major AI providers and isolated deployments across different data regions.
Building an AI workforce
CEO and co founder Robin Van Aeken said manufacturers are facing a major capital investment cycle driven partly by demand for AI infrastructure, while also dealing with trade disruption and geopolitical uncertainty.
CTO and co founder Odhran O’Donoghue said industrial AI requires systems capable of working with far larger amounts of context than conventional AI applications.
The new funding gives Magentic additional resources to develop what it calls an AI workforce for the physical economy. The company plans to expand its digital workers across more procurement and supply chain functions while continuing research into systems that can reason across large volumes of multimodal industrial data.
For manufacturers, the goal is to shift AI from an analytical tool into an operational layer capable of continuously identifying problems, making decisions and executing work across complex procurement environments.