Whalesbook
Partnering Strategy and Funding Round
Founded just last year, Doss is evolving its strategy. The company is now focusing on partnering with AI-native ERP providers rather than competing directly. CEO Wiley Jones explained that Doss aims to complement ERP rivals like Rillet and Campfire by offering specialized, AI-driven inventory and procurement tools that integrate seamlessly, rather than competing head-on.
The $55 million Series B funding round was co-led by Madrona and Premji Invest, with participation from Intuit. Existing investors, including Theory Ventures and General Catalyst, also joined, signaling strong confidence in Doss's specialized approach.
Filling the Inventory Gap in ERPs
Enterprise Resource Planning (ERP) systems are vital for businesses, connecting departments from finance to HR. Yet, many modern AI-based ERPs, while strong in core financial tasks, often miss the crucial need for detailed inventory management.
Doss provides an AI-native layer to ensure real-time physical goods data precisely aligns with accounting ledgers. Jones noted that AI ERP companies often manage accounts payable and receivable but lack integrated procurement and inventory workflows. Doss's platform fills this gap, building supply chain visibility tied to financial and accounting integration.
Target Market and Competitive Landscape
Doss primarily targets mid-market consumer brands with annual revenues between $20 million and $250 million. Verve Coffee Roasters is cited as a key customer. Doss positions itself against traditional ERP giants like NetSuite, which is improving its AI features. However, the startup believes its specialized, integrated approach offers a compelling alternative.
Jones acknowledged that selling two separate systems—one for accounting and another for inventory management—poses a challenge. Yet, he argues that the difficulty of implementing older ERP systems makes customers more open to adopting multiple new, AI-powered solutions. He suggests the future lies in system architectures that are highly adaptable and usable by automated agents.