Inside Didero's Physical Supply Chain Solutions

Global supply chains continue to depend on fragmented communication systems that scatter information across emails, PDFs, spreadsheets and supplier messages. A New York startup called Didero has developed an AI platform designed to convert these dispersed data points into automated procurement workflows, targeting the operational complexity that manufacturers face when managing direct materials sourcing.

The company drew inspiration from Denis Diderot's 1751 Encyclopédie project, which organised the practical knowledge of tradespeople and craftsmen into a structured reference system. Didero applies a comparable method to procurement data, using AI agents to extract contract terms, pricing details and delivery schedules from natural language communications.

The platform addresses direct procurement, which covers the raw materials and components required for manufacturing operations, rather than indirect spending on internal supplies. This distinction matters because direct materials procurement directly impacts production schedules, inventory levels and ultimately the ability to fulfil customer orders on time.

Managing direct materials procurement

Direct procurement remains one of the more labour-intensive functions in global supply chains. Buyers and suppliers exchange purchase orders, shipping documents and order confirmations through multiple communication channels, while Enterprise Resource Planning (ERP) systems provide the underlying records.

The challenge intensifies when manufacturers work with hundreds or thousands of suppliers across different regions, each with their own communication preferences and documentation formats. Purchase orders might arrive via email, supplier portals or Electronic Data Interchange (EDI) systems, while amendments and exceptions get communicated through phone calls or instant messages. This fragmentation creates data silos that prevent procurement teams from maintaining real-time visibility across their supplier base.

According to Didero, much of the work required to manage these transactions occurs outside ERP platforms. The company has built a network of AI agents that operates on top of existing ERP environments such as SAP, Oracle and Microsoft Dynamics, removing the need for infrastructure replacement.

The system reads incoming communications and extracts variables including minimum order quantities, pricing and delivery terms. Purchase orders can be issued, acknowledged and reconciled without manual intervention for routine transactions.

Didero escalates activity to procurement teams through an "Exception Queue" when tolerance thresholds are breached. These thresholds could include invoice price discrepancies, lead-time extensions or supply disruptions, and the platform presents analysed alternatives when flagging issues.

The Exception Queue functions as a control mechanism that allows procurement professionals to define their risk tolerance and decision-making boundaries. Teams can set specific parameters around acceptable price variations, delivery window changes or quality specification adjustments. When suppliers propose changes that fall outside these parameters, the system automatically flags the transaction, provides context about the deviation and presents options based on alternative suppliers, inventory levels or production schedule flexibility. This approach maintains human oversight for strategic decisions while automating the routine execution work.

Orchestrating supply chain automation

The proliferation of AI tools across enterprise functions has created an orchestration challenge in supply chains. Many organisations deploy separate AI systems for different activities, which could lead to disconnected automation and new inefficiencies.

Didero's platform connects sourcing activity, supplier communications, accounts payable workflows and production planning into a single operational framework. The system is designed to understand supply chain terminology and automate processes that traditionally required human intervention.

These processes include running Request for Quotation events, chasing supplier acknowledgements, processing invoices and updating supplier performance data. Sustainable packaging manufacturer Footprint uses the platform to manage supplier communications and procurement workflows across its supply network.

For manufacturers contending with supply chain volatility, the ability to automate routine execution while retaining human oversight could reduce operational burden. As Tim Spencer, Chief Executive Officer of Didero, says: "Global trade runs on natural language communication. The goal is to go from 'I need a good' to payment without having to lift a finger."

Funding and team background

Didero raised US$30m in a Series A round co-led by Chemistry and Headline, with participation from Microsoft's venture fund M12. Existing investors including First Round Capital, Construct Capital, BoxGroup and AI Grant also participated.

According to Didero, the platform is deployed with more than 30 manufacturing and distribution customers. The funding will support product development, engineering expansion and customer success initiatives, while extending autonomous capabilities into international payments and logistics orchestration.

Tim Spencer, Chief Executive Officer of Didero, conceived the company after managing thousands of suppliers while building ecommerce aggregator Markai during the pandemic. The experience exposed limitations in manual procurement processes and highlighted how much time procurement teams spent on administrative tasks rather than strategic supplier relationship management.

He is joined by Lorenz Pallhuber, a former McKinsey procurement specialist who spent years advising manufacturing clients on supply chain optimisation and procurement transformation programmes. Tom Petit, former technical co-founder of Landis, brings machine learning infrastructure and enterprise software development experience, having built scalable AI systems that process large volumes of unstructured data. The founding team combines operational procurement expertise with technical capability in machine learning and enterprise software architecture.

Autonomous execution in supply chains

Procurement technology has focused on visibility, reporting and workflow management for decades. The current generation of AI tools has improved information access, but many function as assistants that support human decision-making rather than execute tasks independently.

Didero is pursuing autonomous execution, enabling software agents to carry out routine procurement activities and involve humans only when exceptions occur. This approach could redefine how procurement work is performed in manufacturing supply chains.

Whether autonomous procurement becomes the dominant operating model remains uncertain. Manufacturers face supply chain complexity, labour shortages, geopolitical uncertainty and pressure to improve efficiency, and technologies capable of executing routine procurement work autonomously are moving from experimentation to practical application.