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.
That history is fragmented by design. Every retailer sells its own slice, cut its own way — Kroger's data does not line up with Albertsons', and neither one sees the same household at Costco or Trader Joe's. Brands stitch mismatched store-by-store reports together and still cannot say whether the product got used, got bought again, or sat in a cupboard for a year.
Consumption is the missing dataset. A household cannot see inside its own kitchen — and neither can anyone selling into it.
One system in place of five: the inventory you keep in your head, the list in the notes app, the recipe app that assumes you own nothing, the leftovers you forget, the trip you make twice. Trepo knows what the kitchen has, says what can be cooked with it tonight, and moves what is running out onto the list.
Log it however it happens — a photo of the receipt, the fridge or the shelf, your voice, or a tap on Halo, the device that sits where the food is so logging costs a second and happens while it is still true.
Five jobs, one product. Every one of them writes data.
Household-level consumption on their category, across every banner their buyer shops — repeat versus trial, the replenishment window, the whole basket around their product, and the brand a household switched to when they left.
Then the ad products only this data makes possible: sampling into homes that just ran out of a competitor, couponing timed to the run-out rather than the calendar, basket tracking that closes the loop back to in-home use, and audience profiles built from what a home eats, not what it clicked.
Which home runs out on Thursday, not "households that probably buy soda."
Nobody is asked to track anything. Households log because logging is how the product works for them — the behaviour we sell is the same behaviour they came 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. It is also the input layer agentic shopping does not have: an agent that cannot see the kitchen is guessing.
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.
Costco on Sunday, Trader Joe's on Wednesday, the corner shop on Friday. One basket no single retailer can assemble.
Nobody fills in a profile. The household builds itself out of timestamps — what they eat, how fast, and what they will accept instead. That is exactly what an agent needs in order to shop on their behalf, and every log sharpens it.
The products CPG already buys from Kroger, Instacart and NIQ — sold against a signal none of them have. Sampling into homes that just ran out of the competitor. Couponing timed to the run-out, not the calendar. Basket tracking and closed-loop measurement to in-home use. Audience profiles built from real consumption. Already in market — our first partner one-pager went out on real portfolio data.
Once the graph knows the cadence, the list becomes an order. Trepo is the input layer the agent is missing: what a household eats, when it runs out, and what it will accept instead. We take a share of a basket they were always going to buy — and we get to ask first, because a retailer only finds out you need it when you turn up.
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.
Who buys this over time: consumers, through the app and Halo · CPG brands and their retail media budgets · retailers and delivery platforms · and the food-automation and home-robotics companies that will need in-home consumption to train on and cannot collect it themselves.
All of it organic — word of mouth and the feed, with no acquisition engine behind it. Signups are cheap; logged food is not. On the sell side we go founder-led, direct to CPG brand and category teams with a one-pager built on their own portfolio.
Instacart, DoorDash, Amazon and the assistants can place the order. None of them can see the kitchen, so the agent is guessing at its own input — it knows what you bought from them, not what you have.
NIQ, Circana and retailer clean rooms sample purchases: weeks late, one banner at a time. Their data is a by-product of a transaction they own, and nobody logs a pantry into a checkout.
Recipe apps without inventory, or a $3,000 fridge with a camera in it. Ours is a free app plus a cheap device that sits where the food is — which is why the logging actually happens.
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.
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 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 it gets built as a supporting document alongside the cap table and hiring plan. Also needed: 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.
The app is live and in daily use. Halo is going out to beta households now, and the web build that opens up Android is in progress.
13,371 users and 281,726 items logged — dense enough to profile a household, not yet dense enough to predict cadence in every category. That gap is what this round closes.
The first CPG partner conversations are live, opened with a portfolio one-pager built entirely on our own household consumption data.
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.