A

Atomic

AI-powered supply chain planning platform

Overview

Atomic builds an AI-powered SaaS platform for supply chain planning that helps companies with physical products, with a focus on CPG, food and beverage, and apparel. Its end-to-end planning engine models supply and demand at unit and transaction levels, automates forecasting and inventory management, and runs planning scenarios while integrating with ERP and WMS to use real-time data. The platform uses agentic AI and simulation to generate automated recommendations and alerts, including demand and supply planning plus financial impact analysis with scenario versioning. Its goal is to reduce inventory costs, prevent stockouts, shorten planning time, and improve working capital by providing fast onboarding and data-driven, scenario-based planning.

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About Atomic

Simplify's Rating
Why Atomic is rated
B-
Rated B on Competitive Edge
Rated B on Growth Potential
Rated C on Differentiation

Industries

Data & AnalyticsIndustrial & ManufacturingEnterprise SoftwareAI & Machine Learning

Company Size

11-50

Company Stage

Series A

Total Funding

$15.5M

Headquarters

New York City, United States

Founded

2023

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What believers are saying

  • Atomic closed a $12.5 million Series A on September 29, 2026.
  • TechCrunch says Atomic's ARR quintupled since January 2026, signaling accelerating demand.
  • Good Chop cut inventory from nine weeks to four while more than doubling revenue.

What critics are saying

  • DoorDash and Good Chop anchor Atomic's credibility; any stalled rollout damages enterprise trust fast.
  • Oracle, SAP, and Kinaxis already own planning budgets, squeezing Atomic's expansion into existing accounts.
  • Atomic's autonomy promise creates existential liability if one wrong purchasing rule causes service failures.

What makes Atomic unique

  • Former Tesla planners built Atomic around decision speed, not dashboards, after Model 3.
  • Atomic automates 90% of DashMart purchasing across hundreds of sites, with zero-disruption migration.
  • Nucleus turns planning rules into executable purchasing, then reuses them across every order.

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Funding

Total Funding

$15.5M

Below

Industry Average

Funded Over

2 Rounds

Series A funding typically happens when a startup has a product and some customers, and now needs funding to scale. This money is usually used to grow the team, expand marketing, and improve the product. Venture capital firms are frequently the main investors here.
Series A Funding Comparison
Below Average

Industry standards

$15M
$8.2M
$12.5M
$15M
$30M

Benefits

  • Remote Work Options

Company News

Madrona
Sep 29th, 2026
A Reasoning Flywheel for the physical economy: why we invested in Atomic.

A Reasoning Flywheel for the physical economy: why we invested in Atomic. For most of the past three decades, enterprise software has been in the business of telling people what happened. Systems of record captured the transactions, analytics tools turned them into reports, and machine learning eventually got quite good at predicting what would come next. The decision itself, though, has stayed where it always was, with people working through spreadsheets and hard-won experience, and what they learn from one decision seldom carries into the next. What is new with AI is that software can finally take part in that work. It can reason through a decision alongside the people who own it, act on it and learn from how it turns out, so that each decision makes the next one better. We call that compounding loop the Reasoning Flywheel, and we believe the companies that get one turning inside their operations will steadily pull away from those that don't. Few corners of the economy need that flywheel more than the companies that make and move physical goods. Forecasting tools have improved steadily for years, yet planning teams at most consumer brands and retailers still run their most consequential decisions through legacy software, sprawling spreadsheets and the heroics of a few people who know where everything lives. The forecast was never really the bottleneck. The hard part is translating a team's judgment about suppliers, margins and service levels into the hundreds of purchasing and production decisions the business makes every day. Legacy planning software stops at a recommendation and leaves that last step to people, which means the judgment never compounds. It gets reapplied by hand, order by order, and it walks out the door when someone leaves. Atomic is building what it calls an AI control system for physical goods companies, and we think of it as a Reasoning Flywheel for the physical economy. It gives planning teams a model of their business at the product level and recommendations that show their reasoning. When a team improves the logic behind a decision, Atomic carries that improvement into every order the business places, and as trust builds, AI agents take on more of the work. That is what executable judgment looks like, and it is why we are doubling down. Today Atomic announced a $12.5 million Series A co-led by Klass Capital and Madrona. We backed the company at the seed, and are excited to double down on their incredible mission. The founders are the reason we were confident so early. Michael Rossiter, Neal Suidan and Jeff Goodrich led sales and operations planning at Tesla through the Model 3 ramp, when the business needed to scale 20x. Tesla had to take a mass-market car from launch to volume faster than traditional supply chain software was designed to support. To get through it, the team built a 50-person planning engineering organization that treated planning as a system to be engineered rather than a report to be reviewed. Jon McNeill, then Tesla's president, worked alongside them through that period, and when they set out to build something of their own, DVx incubated it. We rarely see founder-market fit this literal. Most companies contending with volatile demand and tight supply will never be able to justify the kind of team Michael, Neal and Jeff built at Tesla, and until recently no software could stand in for one. AI changes that equation. Models can now reason across the messy, product-level data a planning team lives in, planners can work with AI to write new decision logic in a fraction of the time it used to take, and agents can handle work like S&OP preparation, inventory questions and supply-risk checks. Atomic turns what the founders learned on one of the hardest versions of this problem into a product a physical goods company can deploy in about 30 days, using the data it already has and running alongside its existing ERP. The early results show what happens once that loop kicks in. At DoorDash's DashMart business, Atomic now automates 90% of purchasing across hundreds of sites, following a migration from a legacy vendor that took about three months. More telling is what the DashMart team did next. Using Atomic's AI, they built new logic for how primary and backup suppliers should be used in roughly an hour, and Atomic applied it across daily purchases, shifting more volume to primary suppliers and improving gross margins. That hour is the flywheel in miniature: a team's judgment captured once, carried into every order, and transparent enough that the people responsible for the outcome can trust it. With this round, Atomic will extend its platform from planning and decision support into a full control system, one that connects a company's business objectives to the decisions it makes every day, and will support larger enterprise deployments. Volatile demand, supply shocks and thinner margins are turning planning into a competitive discipline for every company that makes or moves physical goods, and the companies that come out ahead will be the ones whose best judgment reaches every decision. Michael, Neal and Jeff are building that capability for everyone who could never build it themselves, and we're rolling up our sleeves alongside them.

PR Newswire
Sep 29th, 2026
Atomic raises $12.5M to build the AI control system for physical goods companies.

Atomic raises $12.5M to build the AI control system for physical goods companies. Sep 29, 2026, 06:00 ET Founded by former Tesla supply chain leaders, Atomic already automates 90% of purchasing for DoorDash's DashMart business and helped Good Chop cut inventory in half while more than doubling revenue. BOSTON, Sept. 29, 2026 /PRNewswire/ - Atomic, the AI-native supply chain planning platform, today announced a $12.5 million Series A led by Klass Capital and Madrona. Adrian Schauer of Klass Capital and Matt McIlwain of Madrona join Atomic's board. Atomic will use the funding to extend its platform from planning and decision support into a control system that connects a company's business objectives to its daily operating decisions, with AI agents extending the capability of human teammates. When demand shifts or supply tightens, planning teams have to understand what changed, compare responses and adjust what gets bought, built or moved. Atomic connects that work in one operating model. Teams can improve the logic behind a decision and carry that improvement into the orders the business places every day. The company was founded by Michael Rossiter, Neal Suidan, and Jeff Goodrich, who together led Sales and Operations Planning and planning at Tesla through the Model 3 ramp-up and built a 50-person planning engineering organization. The company was incubated at DVx Ventures, the venture firm founded by former Tesla President Jon McNeill, who worked alongside the team at Tesla and now serves on Atomic's board. "Most companies can't justify building the kind of planning engineering team we had at Tesla," said Rossiter, Atomic's co-founder and CEO. "They're still running critical parts of their business through legacy software, hundreds of spreadsheets and individual heroics. Atomic gives them that capability as a product." Companies can deploy Atomic's sales and operations planning layer in about 30 days using the data they already have. From there, Atomic can expand into detailed planning and execution, running alongside a company's existing ERP rather than requiring the business to rebuild its processes around new software. For DoorDash's DashMart business - the company's convenience and essentials store - Atomic now automates 90% of purchasing across hundreds of sites. DashMart migrated to Atomic in about three months with feature parity and zero business disruption. Its team recently used Atomic's AI to build new purchasing logic for how primary and backup suppliers should be used in roughly an hour. Atomic then applied that logic across daily purchases, helping shift more volume toward primary suppliers and improve gross margins. "Atomic has helped us plan and manage inventory more proactively, so we can maintain high service levels for consumers even in times of disruption," said Nick Palefsky, Director of New Business Verticals at DoorDash. "Turning a forecast into the right purchase at the right site takes operating judgment we've built over years. What to carry where, how to substitute when a supplier misses. Atomic is how we apply that judgment across our network every day." Additionally, Good Chop, HelloFresh's meat box subscription business, used Atomic to reduce inventory on hand from eight or nine weeks to four while more than doubling revenue and expanding its distribution network and product range. "Atomic helped us keep inventory under control as we expanded the business. It's the core engine we use to plan supply and place purchase orders," said Seb Tron, president of Good Chop. Atomic's AI agent platform, Nucleus, is also in daily use by customers for tasks including S&OP preparation, inventory questions and supply-risk checks. The new funding will be used to expand the platform and support larger enterprise deployments. Adrian Schauer of Klass Capital and Matt McIlwain of Madrona will join Atomic's board alongside Jon McNeill, co-founder and CEO of DVx Ventures and the former President of Tesla. DVx incubated Atomic and has backed the company since its earliest days. "We've never heard customers talk about a software vendor the way they talk about Atomic. They trust it with daily operating decisions and are asking for more," said Daniel Klass, founder and managing partner of Klass Capital. "We backed Atomic at the seed because the founders had lived this problem at Tesla. A year and a half later, their platform runs daily purchasing across DoorDash's DashMart network. Most enterprise AI stops at a recommendation. Atomic acts on the plan and shows its work. We're pleased to co-lead this round and join the board," said Matt McIlwain, managing director of Madrona. "Michael, Neal and Jeff helped build the system that allowed Tesla to operate at a scale and speed that traditional supply chain software simply wasn't designed for," said McNeill. "What gets us excited about Atomic was the opportunity to take what they learned solving one of the hardest supply chain problems in the world and turn it into something any company can use. AI now makes it possible to give companies not just better forecasts, but a system that can actually make and execute decisions." Atomic's customers include DoorDash's DashMart, GoodChop (HelloFresh), Starface World, OOFOS, LMNT and Vincero. About Atomic Atomic is building the AI control system for physical goods companies. Founded by former Tesla planning leaders, Atomic gives planning teams a model of their business at the product level, recommendations that show their reasoning, and AI agents that take on more of the work as trust builds. Atomic is backed by DVx Ventures, Klass Capital, Madrona, Alumni Ventures, and Sandberg Bernthal Venture Partners. Learn more at atomic.supply. SOURCE Atomic

Blasting News
Sep 29th, 2026
Ex-Tesla managers launch Atomic with $12.5M for AI that automates supply chain decisions

Ex-Tesla managers Michael Rossiter and Neal Suidan have launched Atomic, a supply chain AI platform that has raised $12.5 million in Series A funding. Led by Klass Capital and Madrona Venture Group, this brings the startup's total funding beyond $15 million. The founders managed supply chain planning at Tesla during the Model 3 production ramp-up in 2018, where traditional spreadsheet methods proved inadequate. Atomic's platform simulates scenarios and automates supply chain decisions, addressing a market estimated at $20 billion. Clients include DoorDash and HelloFresh. Jeff Goodrich, former Tesla planning director, has joined as CTO and third cofounder. The platform enables companies to reduce inventory costs whilst freeing planners from repetitive tasks to focus on managing exceptions and validating rules.

Egerin
Sep 29th, 2026
Exclusive: Ex-Tesla team raises $12.5M to put supply chains on autopilot | TechCrunch.

Exclusive: Ex-Tesla team raises $12.5M to put supply chains on autopilot | TechCrunch. Supply chain startup Atomic came out of stealth last year with founders who promised to use their experience at Tesla to streamline inventory and boost customers' bottom lines. At its core, Atomic decides how much inventory a company should have, and where, by simulating scenarios, then recommending, or even automatically choosing, a response. The approach traces back to a crisis. Atomic's founders built an early version of this system at Tesla during the 2018 Model 3 production ramp, when the automaker's own spreadsheets couldn't keep up with how quickly planning needed to change. But in the year-plus since Atomic co-founders Michael Rossiter and Neal Suidan started talking publicly about their work, that shift has become real for customers - including big tech companies like DoorDash and HelloFresh. It has paid off financially, too. Atomic's annual recurring revenue has quintupled since the beginning of this year, according to Jon McNeill, a former Tesla president and the founder of DVx Ventures, where Atomic was incubated. That growth just helped Boston-based Atomic lock down a $12.5 million Series A funding round, bringing its total funding to just north of $15 million to date. The new round was led by growth equity firm Klass Capital and Seattle VC stalwart Madrona Venture Group. Atomic has also brought on longtime Tesla planning director Jeff Goodrich as its CTO and third co-founder. "If you think about running a supply chain, running an operating model, it's like an infinite search space for optimization that you're trying to figure out all the decisions you could make at any given time - and then it changes all the time too," Rossiter, who is Atomic's CEO, said in an exclusive interview with TechCrunch. "AI can play the role of finding all of the best paths through that forest." "The company has transitioned from pilot customers into real customers, and by real customers, it's DoorDash-scale customers," said McNeill, who's also on Atomic's board, in an interview. "The product has evolved from being an optimization platform that gives recommendations to a platform that not only gives recommendations but it makes decisions, so it's fully autonomous in that sense, and so DoorDash is running, I think, 90% of its purchasing across hundreds of sites." For food-focused customers like DoorDash, for instance, Atomic's software helps cut down on waste and spoilage. And each new industry Atomic works in presents different challenges, Rossiter said. "The cool thing about Atomic is it's a general model of how you think about supply chains and operating models, and so no matter what that system looks like, our AI can can adapt to it and tailor fit it," he said. "We're working with CPG [consumer packaged goods], we're going deep with mobility and manufacturing clients right now as well, and working that space, kind of going back to our Tesla roots." Making the agentic software useful and adaptable for multiple industries was a big part of what drew investor interest for the Series A, Rossiter said. But it was also Atomic's ability to deploy quickly with new customers. "We gave them this challenge from a board level of compressing their onboarding time and really making it a non-event for a customer to turn this on," McNeill said. Suidan, who is also Atomic's chief product officer, ran point on that challenge, McNeill said. He was able to push Atomic's agentic AI to the point it was able to figure out the "decision rules" that a customer's staff might have, even if they hadn't been written down anywhere. "Then customers were saying, 'Okay, then you might as well make the decision and free my time up,'" McNeill said. "And where executives, I think, kind of got it was - decision speed is an advantage in any business. And this was one of our first principles at Tesla. When I got to know Elon, he said the thing that will separate us from all of our competitors is decision speed, because decision speed compounds. Like, I make a decision today, I build on that decision tomorrow, et cetera, and it takes one of our competitors, like Ford or Toyota, 30 days to make the first decision." Rossiter said he's excited to help pull customers' supply chain efforts out of spreadsheets and into advanced software that can help make planning decisions. It's an evolution he's seen CFOs lead for their own organizations, but he said it's rarer to find someone doing the same for operations. "Finance data always gets the priority. Operating data doesn't always get that," he said. When you purchase through links in its articles, Egerin Inc. may earn a small commission. This doesn't affect its editorial independence. Sean O'Kane is a reporter who has spent a decade covering the rapidly-evolving business and technology of the transportation industry, including Tesla and the many startups chasing Elon Musk. Most recently, he was a reporter at Bloomberg News where he helped break stories about some of the most notorious EV SPAC flops. He previously worked at The Verge, where he also covered consumer technology, hosted many short- and long-form videos, performed product and editorial photography, and once nearly passed out in a Red Bull Air Race plane. You can contact or verify outreach from Sean by emailing [email protected] or via encrypted message at okane.01 on Signal.

FinSMEs
Apr 16th, 2025
Atomic Raises $3M in Seed Funding

Atomic, a NYC-based provider of an AI-powered supply-demand management software, raised $3M in Seed funding

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