Trepo is the operating system for the kitchen. Households use it to know what they have and cook with it — and every time they do, it records what a real home actually eats.
Households get their kitchen back. The market gets a signal it has never had.
Nobody can see their own pantry from the store. Delivery apps go dark at the door, recipe apps assume you own nothing, and a list never depletes on its own. The fridge is a black box the second it closes.
One retailer sees one basket. Nobody can say what else came home with it, whether it got bought again, how long the gap was, or whether it was planned or impulse. Everything else is a model of what probably happened.
Both problems are the same missing dataset. The household cannot see its own kitchen, and neither can anyone selling into it.
Trepo tracks what the kitchen has, tells them what they can cook with it tonight, and moves what is running out onto the shopping list. Check in with a photo of the receipt, the fridge or the shelf — or just say it.
Four reasons to open it. Every one of them writes data.
Household-level consumption on their category, across every banner their buyer shops — not a panel, not a model, and not one retailer's slice. Repeat versus trial, the replenishment window, and the whole basket around their product.
Which home runs out on Thursday, not "households that probably buy soda."
Nobody is asked to track anything. The value the household came for is what produces the asset the market pays for.
A purchase says a unit left a shelf. Consumption says it was used, and is about to be needed again. It cannot be bought at any price — it exists only if a household chooses to tell you, week after week.
Three a day, every week, for months. We know the Thursday before they run out.
They did not run out and they did not stop eating yogurt. They switched brands — and we can see to what.
Club, discounter, corner shop. The whole basket, which no single retailer can assemble.
Nobody fills in a profile. The pattern falls out of the timestamps — and every log makes the next recommendation better, which makes the next log more likely.
Brands pay for what no panel can give them. Already in market — our first partner one-pager went out on real portfolio data. This round funds the ad products on top: sampling into homes that just ran out of the competitor, follow-along on whether it stuck, and closed-loop tracking to in-home consumption.
Once the graph knows the cadence, the list becomes an order and we take a share of a basket the household was always going to buy. A retailer only learns you need it when you turn up and buy it. We know before that, so we are the one who asks.
Data revenue funds household growth. Household growth makes the data more valuable and the replenishment volume bigger. Neither line caps the other, and every household added improves both at once.
13,371 signups in roughly six weeks with no paid acquisition engine behind it. 32,042 recipes came in straight off TikTok and Instagram — the product is being handed around inside the feed it already lives in.
The device sits where the food is, so logging costs a second and happens at the moment it is true. It turns occasional logging into continuous measurement, and it is the hardest part of this to copy.
Direct to CPG brand and category teams with a portfolio one-pager built on their own products. Zach has sat on both sides of this trade, which is why the first conversations are already live.
Signups are cheap. Logged food is not — every household on the graph is one that chose to do work for us, repeatedly.
Their data is a by-product of a transaction they own. Nobody logs their pantry into a checkout.
The list, the recipes and the inventory are the same product. That is the consumer pitch, and it is why the logging happens at all.
Builds the thing, hardware and software both. The Halo device, the firmware, the OTA pipeline, the ingestion backend and the Kitchen Graph itself are his. Previously Neuro and Tesla.
Exact titles, dates and the two or three shipped accomplishments Matt wants named. Not inventing a bio for a diligence document.
Owns revenue, and has stood on both sides of the trade we are building. Retail media and advertising at Viant; CPG sales and marketing leadership at USCAPE — the buyer of this data, and the seller of the products it measures.
Exact titles, dates and accomplishments. The framing above is the argument; the credentials need to be Zach's words.
Growth is the line that is real today. Revenue starts on the data side, where a partner conversation is already live. The replenishment line turns on once the graph is dense enough to predict cadence per category. At 261 dollars a household a year across both lines, 5M households is roughly $1.3B — 3.7% of US homes.
A 24-month projection needs monthly burn and cash on hand. Give me both and the model gets built as a supporting document, alongside the cap table and hiring plan. The deck slide stays this thin on purpose.
Data revenue booked to date, pipeline value, and the assumed month the replenishment take-rate switches on.
5M is the recommendation. Matt and Zach to confirm before this is shown.
We are raising $X.XM on a $XXM cap SAFE to spend on three things: household density so cadence becomes predictable per category, the ad products that convert live CPG interest into contracted revenue, and the replenishment bridge that turns a list into an order.