Auger, a supply chain software startup founded by former Amazon operations executive Dave Clark, has raised $50 million in Series B funding to expand an AI platform designed to sit above ERP, warehouse management, transportation management, and planning systems. The round was led by Eclipse, with existing investor Oak HC/FT also participating. The funding brings Auger’s total capital raised to $150 million.
The company’s argument is not that supply chain teams need another system of record. Auger sits on top of existing enterprise systems, including ERP, WMS, TMS, and demand planning tools, and unifies supply chain data into a single operating layer. It then uses AI agents and optimization models to make routine decisions, execute actions, and route exceptions to human review.
Clark told GeekWire that general-purpose AI can generate insights, but supply chain execution requires systems built around domain expertise and contextual understanding. Auger describes that operating model as an ontology, or a detailed map of how supply chains actually work.
AI Layer Above Existing Systems
Auger’s positioning is notable because it accepts the reality of fragmented enterprise landscapes. Large companies already run multiple systems for planning, transportation, warehousing, procurement, inventory, and finance. The problem is that many decisions still happen between those systems, where teams reconcile data, evaluate tradeoffs, and manually push updates back into execution tools.
In one example described by GeekWire, Auger handled a supplier delivery shortfall by identifying the inventory gap, determining which customers should be prioritized, reallocating inventory, and pushing the updated plan back into existing systems. Clark said most supply chain software generates alerts and waits for humans to act, while Auger is designed to make routine decisions autonomously and flag exceptions.
At Fanatics, Clark said roughly 85% of decisions in the process Auger manages are happening autonomously, with a goal of reaching the mid-90s soon. Auger has also said another eight to 10 companies are in contract negotiations or pilot programs.
The company is built on Azure and was named a premier supply chain partner on Microsoft Fabric in March. GeekWire reported that Microsoft sales representatives can earn commission on Auger deals, though Clark said the partnership is still early.
Supply Chain Agents in the Execution Gap
Auger’s funding comes as more AI startups target the manual coordination layer that remains after companies have invested in ERP and logistics systems. ERP Today recently covered DeepFabric’s general availability launch, which similarly positioned AI agents around supply chain execution work that spans documents, exceptions, approvals, and handoffs across systems.
SiliconANGLE reported that DeepFabric’s platform ships with more than 50 supply chain agents and targets manual, error-prone work in freight audit, proposal management, inventory, financial control, and operational assurance. The overlap is not exact, but the market signal is similar: AI vendors are increasingly positioning themselves around execution work that existing enterprise systems expose but do not fully resolve.
For Auger, the investor thesis appears tied to supply chain decision-making rather than task automation alone. Clark said that the company’s ambition is to have half of US GDP flowing through its platform by 2030, with revenue exceeding $1 billion.
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What This Means for ERP Insiders
Supply chain AI is positioning itself above the application stack. Startups are not necessarily trying to replace ERP, WMS, TMS, or planning systems; they are trying to become the decision layer that connects them. For CIOs, supply chain leaders, and enterprise architects, the next design question is who owns decision rights when AI systems can read across platforms and push actions back into them.
Execution intelligence will depend on integration discipline. Agents can only make useful supply chain decisions if they understand inventory, demand, orders, transportation constraints, customer priority, and operational tradeoffs across fragmented systems. For retailers, manufacturers, logistics providers, and consumer goods companies, the practical test is whether these platforms can maintain clean data flows, traceable decisions, and reliable exception routing.
Human oversight will define how far autonomous supply chain decisions can go. Routine allocation, replenishment, routing, and exception triage may be strong candidates for automation, but high-impact tradeoffs still require accountability and business judgment. For ERP and supply chain teams, the challenge is to decide which decisions can be automated, which require approval, and how every action will be audited after execution.
