About MISSIN Budget goes where it reports well, not where it earns.
That gap is the whole company. MISSIN is the AI marketing and attribution engine: every customer touch-point across the journey, collected and attributed to the revenue it generates, so budget goes where it actually earns. The engine is Mend.
What we think
Reach is the metric everyone gives away, and the one that means least.
Ten thousand views on a video nobody watched past the intro is not ten thousand impressions of your product. A creator with 500k views whose audience leaves at 0:40, with the placement at 2:10, has sold nothing — and reach ranks them first. We would rather measure the outcome than the proxy, everywhere, including in how we price.
Attribution should be welcome, not extracted.
The strongest attribution signal is the customer telling you who sent them. Nobody does that for free, so we pay them: a discount, usage credits, a settled reward. The incentive is not a growth trick bolted onto a data product; it is the mechanism that makes the data exist.
AI should show its working.
Mend never asks a model who gets the credit. What code can prove (a redeemed code, a click id, a tracked link) is settled in code first. Where that runs out, the AI answers small questions and code makes the call, with weights that are versioned. Every verdict shows its journey, and when the evidence does not settle an order, the verdict says Possible instead of guessing.
A confident number should be arguable.
A verdict like “Lila’s Reel converted this order: her code was used at checkout and the buyer named her” is only useful if the why is attached and you can disagree with it. Every verdict carries its journey, and the merchant controls the trust gate: content only counts once approved. Attribution you cannot vouch for is attribution you will not act on.
The pattern is portable; the integrations are not the product.
Forms, discounts, campaigns, partners and attribution are platform-neutral ideas that work wherever customers convert. We started with Shopify and Stripe, then Klaviyo, because that is where our first users are, and the deep work there — a checkout extension, a web pixel — exists to make an agnostic pattern effortless on one platform, never to define the product's limits.
Where this came from
I am a solo developer with a data engineering background, and before this I ran a fashion business on Shopify.
What I remember most clearly is how expensive it was to find out anything useful. Every tool that could tell me where my customers came from was priced for a company several sizes larger than mine, and the ones I could afford told me about traffic rather than about people. I was spending money on creators and channels and guessing, every month, which of them had worked.
The idea that became MISSIN was not an attribution model. It was simpler than that: my customers already knew who had sent them, and nobody had ever thought to ask them — or to make it worth their while to answer. So I asked, and I paid for the answer with something they wanted anyway. The attribution came out of the conversation rather than out of a pixel.
Thirteen months of development later, that question has a platform around it: the forms that ask it, the incentives that get it answered, the discounts and partner posts it joins to, and Mend, the engine that turns the whole chain into a verdict you can open and argue with. It is still one developer. That is why the pricing is what it is, and why every limit on it is published.
Who it is for
- E-commerce brands who spend on influencers, affiliates and paid channels and cannot say which of them earns.
- Growth and performance marketers who need attribution beyond UTM and reporting beyond spreadsheets.
- Agencies and multi-store operators — the workspace model is built to hold many shops.
MISSIN LIMITED is a UK company. If you want to know what we are building next, the changelog is the honest version — and if you want to ask us something, write to us.