Augmentics builds an AI decision system for regional foodservice and specialty distributors. Every morning it tells your buyers and warehouse team exactly what to do with the lots that won't sell at full margin — ship it, repack it, divert it, mark it down, or donate it — with the dollar value of each option attached.
In regional perishables distribution, the difference between margin and shrink is a series of small calls made at 6am with incomplete information: which customer gets this lot, what gets marked down, what gets diverted, what to reorder. Today those calls live in spreadsheets and the heads of a few veteran buyers. When they're right, nobody notices. When they're wrong, it shows up as spoilage.
Forty-seven lots need a disposition decision before the trucks load. Each one has a shelf-life clock, a set of customers who might take it, a secondary market, and a markdown floor. Augmentics ranks them, recommends an action for each, and shows the dollar consequence — so the decision takes minutes, not the whole morning.
Augmentics reads your lot-level inventory, your incoming orders, and where it can, your cold-chain data — through read-only connectors to the systems you already run. It is not a chatbot. It is a small, governed decision system that reasons over your data and produces recommendations your team can act on.
For every active SKU-and-lot, it predicts demand over the next 1–14 days as a probability, not a guess — “a 70% chance this lot clears at full margin in three days.”
For each lot at a decision point, it weighs the options — ship first, repack, divert, mark down, donate, scrap — and ranks them by expected dollar outcome.
It closes the loop upstream: given what's already at risk in the warehouse, it advises next week's purchasing — don't reorder this, increase that, switch this supplier.
The operator clicks approve, modify, or override. Every override teaches the system where your team knows something it doesn't.
We work with founder-led and family-owned regional distributors in foodservice, specialty produce, and protein — the operators who chose to stay independent, and who'd rather have a sharp technical partner than be rolled up into someone else's playbook.
This is where most AI deployments fail — and where being precise from day one is the whole game. The system arrives with no authority. It proves itself on small, bounded decisions, and the envelope only widens as the results hold up. You stay in control the entire time.
The agent runs in parallel. Your buyers make every call. Every disagreement between the agent and your team is logged — that's the most valuable thing it learns.
Low-value lots with plenty of shelf life and high confidence can execute without review. Everything bigger, riskier, or customer-sensitive still goes to a human.
Routine decisions run on their own. You decide how far that goes. The system tracks its own accuracy and dollar impact the whole way, so the value is never a matter of faith.
Twenty years building and scaling supply chains, most of it on the hardest problem in perishables: matching capacity and inventory to demand that won't sit still. At Coupang — South Korea's largest online retailer — Matt built the company's first capacity-planning function from the ground up, forecasting demand for its dry and cold-chain networks through hypergrowth, and led the build-out that grew inventory from 10 million to 96 million units across twelve new cold-chain sites. He went on to run end-to-end supply chain for Coupang's launch into Taiwan, cutting out-of-stock rates from 36% to 12% while standing up a local team.
Earlier, at Amazon, he managed inventory for the fiercely seasonal US Toys business — forecasting, buying, and cutting purchase orders against demand that spiked and collapsed faster than any system could track — then built the multi-year capacity plan for Amazon's launch into Mexico. He started where the best operators do: on the floor, coming up over six years at UPS.
Matt has built and led supply chain teams across South Korea, Taiwan, China, Mexico, and now Spain, repeatedly delivering in markets where he didn't share the local language. For a company whose customers span Iberia, Northern Europe, and the US, that range isn't a footnote — it's the job.
Twenty years building the systems that decide what to buy, what to hold, and what to let go. At Amazon, Tim spent eight years on the Global Inventory Platform, managing the engineering teams behind its automated buying algorithms and — most relevant here — its Optimal Inventory Health and Removals Planning systems, which recommended what inventory to pull and how to dispose of it across Amazon's worldwide network. The disposition problem Augmentics solves for perishables is one he has already solved at global scale.
He went on to lead supply chain engineering as a Senior Director at Coupang — owning forecasting, procurement, and inventory automation across engineering, data science, and operations for both fresh and full-line retail — and then as VP of Engineering at HelloFresh, where he ran the supply chain technology organization and, latterly, the platform governing how GenAI is deployed safely across the business.
Tim builds the part of Augmentics that turns a promising agent into a trustworthy one: the evaluation harness that scores every recommendation against its real-world outcome, so the system's accuracy is proven decision by decision rather than taken on faith. He holds a Ph.D. in computer science from Notre Dame.
We work with a small number of distributors at a time. A first conversation is exactly that — two operators who understand your business, asking what your hardest disposition decisions actually look like.