Tuesday, April 22, 2025

Atomic Raise $3M Seed to Revolutionize Supply Chain Planning with Agentic AI

Atomic, an AI-powered platform reshaping the $20 billion supply chain planning market, today announced that it closed a $3 million seed round. The round included DVx Ventures, the company creation platform run by former Tesla president Jon McNeill, and Madrona Ventures, a three-decade-old venture capital firm known for backing transformative AI startups and iconic companies like Amazon, Snowflake, and Smartsheet.

Atomic was founded by Michael Rossiter and Neal Suidan, who experienced the challenges of supply chain planning first-hand as leaders in Tesla’s supply chain and demand planning team. The inspiration came during Tesla’s Model 3 launch in 2018, when they realized how dependent the team was on unwieldy spreadsheets that could not keep up with the speed of changes at the business. The necessities of scaling led them to build Tesla’s end-to-end supply chain orchestration system from scratch. With Atomic, the team set out to solve the same challenges of supply planning for all companies that sell physical goods with an approach that can be rapidly deployed and generalized to any type of supply chain.

“Our vision is to ensure every physical good is actively managed,” said Neal Suidan, Founder and CEO, Atomic. “Planners are the unsung heroes of consumer brands, holding together supply chains through spreadsheets and sheer force of will. But they deserve better tools. We built Atomic to be the inventory planning system we always wished we had. With the support of DVx and Madrona, we’re scaling quickly to bring this technology to more brands, helping them move beyond spreadsheets and into the future of AI-powered inventory management.

Also Read: RELEX Solutions Named a Leader in the 2025 Gartner® Magic Quadrant™ for Supply Chain Planning

I’m Sick of this Sheet!

Trillions of dollars are tied up in slow-moving and poorly planned inventories in the U.S. alone. The vast majority of inventory decisions — even at the largest companies — are still made using spreadsheets, which are error-prone and slow to update, and difficult to scale. This leads to missed sales, bloated inventory levels, and eroded confidence in supply chain operations.

Existing supply chain planning software often forces lengthy, high-cost implementations and rigid systems that drive planners back to spreadsheets.

“What Atomic is doing reminds me of Tesla’s approach to breaking down barriers – taking something slow and inefficient and replacing it with speed and intelligence,” said Jon McNeill, former Tesla President and Founder of DVx Ventures. “By leveraging agentic AI, they are building this incredible, agile system that can be set up in under an hour and is truly a game-changer for consumer brands.”

“We reduced our working capital by millions. It changed our strategy to fund the business this year” said that company’s President.

“Atomic is the one SaaS tool that is open all day, every day on my laptop,” said the SVP of Operations at another Atomic client, where Atomic helped to improve in-stock rates by over 10 percentage points. “I can’t do my job well without it.”

AI-Powered Supply Chain Control

Unlike most planning software that requires costly integrations and lengthy implementations, Atomic is using agentic AI to flip the script. Its configurable framework form fits to client data sources without requiring system integrators or manual configurations. Within an hour, Atomic clients can be initially onboarded and begin fine-tuning forecasts, simulate disruptions, and make adjustments through an intuitive, no-code interface.

“We’ve personally grappled with the pain of supply planning as operators,” said Michael Rossiter, Founder of Atomic and Principal at DVx Ventures. “Planners shouldn’t have to waste time wrangling spreadsheets and Atomic frees them up to make strategic decisions from day one. When you’re drowning, you don’t want swim lessons – you need help immediately. So we made onboarding speed our top priority.”

SOURCE: GlobeNewswire

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