Hi, I'm Gautam, co-founder of Predflow. This is our third pivot.

Before this, we built a customer intelligence platform for D2C brands. Segmentation, retention, predictive audiences. It didn't work. But every single brand we talked to had the same problem, and it had nothing to do with segmentation.

Their ads data was broken.

A brand would show me their Meta dashboard claiming 4.2x ROAS. Then they'd open Shopify and revenue was flat. Google was claiming credit for the same conversions. Three dashboards, three stories, but there was zero clarity.

I watched performance marketers spend hours every week trying to figure out why ROAS dropped, with no real answer. I watched founders ask "where did the money go?" and nobody could point to a number they trusted. I watched teams kill their best prospecting campaigns because last-click attribution made branded search look like the hero.

That's the problem we set out to solve. But once we started digging, we found a deeper issue nobody talks about.

The data itself is messy before any tool even touches it.

Here's what I mean. Every time someone clicks an ad and lands on your store, a little tag travels with them saying where they came from. These tags are typed in manually by whoever set up the campaign. Your marketing team types "Instagram." Your agency types "IG." Your affiliate partner types "NSD" because that's their internal shorthand. Six months later, you have dozens of tags that mean the same thing but look completely different to any software reading them.

Now when you ask "how much revenue came from affiliates?", every analytics tool gives you the wrong answer. Not because the tool is bad. Because it doesn't know that "NSD" is your affiliate partner. That knowledge only exists in someone's head.

We built a layer that fixes this. We call it a semantic layer. Think of it as a translation dictionary between what your raw data says and what it actually means in your business. You tell the system once: "NSD" means "Non-Stop Deals" and that's an affiliate. "bik" and "bitespeed" are both the same retention tool. From that point on, every report, every AI answer, every dashboard uses the clean version. You set it up once, it applies everywhere.

On top of that clean data layer, we built three things:

Attribution that actually reconciles. We connect to your ad accounts, your store through our own web pixel, and your analytics. Your attributed revenue can never exceed your actual revenue. When Meta says 25 orders and Shopify says 15, we show you the real number and why.
An AI agent that understands your business. Not a dashboard you stare at. An agent you ask questions. "Why did ROAS drop yesterday?" "How did innerwear CAC move month over month?" "Which campaigns should I kill?" It answers with real numbers because the data underneath is clean. A brand's head of marketing asked ours for CAC broken down by innerwear vs outerwear over 12 months. The agent returned the answer instantly because the semantic layer already knew which of their 200+ SKUs belonged to each category.
Creative intelligence. It scores your ads, catches fatigue before it tanks your ROAS, and tells you which hooks are working and which aren't. You can try this part for free right now at https://app.predflow.ai/ad_comparator_app, no signup needed.

We built this for performance marketers, D2C brand operators, and agencies managing ad spend across Meta and Google. We have 8 paying customers onboarded, from brands spending $5K/month to ones managing over $75K/month. The feedback that keeps coming back: "This is the first time my numbers actually made sense."

You can try it free at predflow.ai or install directly on Shopify at apps.shopify.com/predflow.

Would genuinely love your feedback. All of it helps.