Fresh produce · wholesale markets, exporters, distributors
And a good price from a slow payer isn't a good price. For every lot that needs a home today, Augmentics tells you who to call first and why: who has room for it, who paid the market price last time, who actually pays. You decide, and every decision is on the record.
Talk to a founder → See who to call first
Fruit Attraction, Madrid, 6–8 October. Matt and Tim are on the floor all three days. Fifteen minutes at your stand: [email protected]
Built by operators from
Amazon Coupang HelloFresh
The call you make every morning
The day starts before six and the selling is done by two. Every lot gets sold — your best rep sees to that, from memory and the phone. The margin is in the buyer you chose and the price you took, and there is no record of the calls you didn't make. A bad month never shows up as a lot that didn't sell. It shows up as ten cents on every crate, every day, and a buyer who paid in ninety days instead of thirty.
14:00
The clock every buyer knows
Every buyer on the phone knows you have until two. Do you know what they paid you last week, and how long they took to pay it? Augmentics puts that beside every name before you pick up the phone.
How it works
Augmentics reads your lots, your buyers, and what each buyer has taken from you, paid, and how fast they paid — through read-only connections to the books you already keep. For each lot that needs a home it puts a ranked list of named buyers in front of your rep, with the reason beside each name. Then it waits for a person to say yes. Once your rep has signed, it can phone the first buyer for you: it says first that it's an AI and the call is recorded, offers only the price your rep signed, and reads the deal back. Nothing is booked until a person confirms.
Who to call first · one lot, this morning · illustration
Lot · strawberries, 500g punnet · 120 cases 3 days left · needs a home by two
1 Frutas Ejemplo Llobregat SL room 600 cases · 1.05× market · pays in 30 days
2 Distribuciones Ejemplo Maresme room 400 cases · 1.02× market · pays in 14 days
3 Frutas Ejemplo Besòs SL room 150 cases · 1.00× market · pays in 7 days
4 Ejemplo Levante SA room 500 cases · 1.08× market · pays in 90 days
— Ejemplo Export SA not ranked — no recent price paid on record
What the first call is worth on this lot computed from your books · never typed
The names are made up. On your screen every line is read from your own books — the value beside the first call is cases × the price that buyer paid you last time — and a buyer we haven't seen pay is shown as not ranked, with the reason — we don't guess. The best price on the list does not win on its own: the fourth buyer offers the most and ranks below the other three, because ninety days is the price. Your rep takes the call, changes it, or says no, and the choice goes on the record with their name and their reason.
Who it's for
We work with owner-led fruit and vegetable traders, wholesalers, exporters and distributors — the houses where a few reps carry the buyer list in their heads, and the owner feels every slow payer and every ten cents.
01
Wholesale-market traders Stalls and houses at Mercabarna, Mercamadrid and the markets like them — bought or consigned, the same merchandise every week, sold by two.
02
Exporters, co-ops and regional distributors, €20M–€120M Family- or founder-owned, with a buyer list of dozens to a few hundred accounts, and no committee between the person who feels the problem and the person who can say yes.
03
Real books, honestly messy Prices, payments and volumes live in an ERP, a spreadsheet and a rep's memory. We read what you have. We don't ask you to rebuild your systems first.
How it starts
The system arrives with no authority and never gets any: it recommends, a person decides, and every decision is on the record. What it earns over time is not the right to act — it is your trust in the list.
Step 1 · Your books
We connect, read-only
Your lots, your buyers, what each one took, what they paid and when. Where a fact is missing, the buyer shows as not ranked with the reason, and stays that way until the fact arrives.
Step 2 · Beside your reps
It ranks; your reps call
For a few weeks the list runs beside the calls your reps already make. Every time a rep picks a different buyer, that is on the record too — and it is the most useful thing we learn.
Step 3 · Which call paid
You see what each call was worth
Once the money is in, we show you, buyer by buyer, which call paid and which didn't — on your own numbers, never a promise about your business.
Five things it never does
The operators behind it
Co-founder & Chief Product Officer · Supply chain
Twenty-five 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, planning 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 — 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 on the floor: unloading trailers part-time on the midnight sort at UPS in Nashville, then five years up through hub operations and industrial engineering — the work of measuring how the work actually happens.
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 selling into Iberia, Northern Europe, and the US, that range isn't a footnote — it's the job.
Coupang cold-chain capacity Amazon inventory management Cross-cultural ops MBA, Supply Chain
Co-founder & CTO · Technology
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 its Optimal Inventory Health and Removals Planning systems, which recommended what inventory to pull and where it should go across Amazon's worldwide network. Deciding what to hold, what to move and who should take it is a problem he has already solved at global scale.
He went on to lead supply chain engineering as a Senior Director at Coupang — owning demand planning, 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 recommendation into a trustworthy one: the evaluation harness that scores every call against what actually happened, 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.
Amazon inventory health & removals Coupang SCM automation HelloFresh VP Engineering Ph.D. CS, Notre Dame
Co-founder · Technical architecture
Enterprise data and AI platforms: senior director roles in data engineering and then engineering foundations at HelloFresh; five and a half years at dunnhumby across engineering, technical account direction and its Latin America innovation lab; fifteen years at YDreams, latterly as CTO Brazil. Nuno owns the architecture that keeps every decision typed, traceable and reproducible — so what the list said, and why, can always be read back.
HelloFresh data platforms dunnhumby YDreams CTO Brazil
Start a conversation
We work with a small number of houses at a time. A first conversation is two operators asking how your mornings actually run — who calls whom, what a good week looks like, who pays late — before anyone shows you a screen.
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