Predflow AI
AI agents that run your back-office workflows
282 followers
AI agents that run your back-office workflows
282 followers
Predflow builds and runs AI agents for repetitive operational work such as accounts payable and reconciliation. We deploy the agent inside your systems and run it every day under an SLA, staying accountable for the output your team receives.
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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.
Gautam here, co-founder of Predflow. Since this launch things have changed a bit, so worth being straight about it.
We built this as an AI agent for ad performance. We learned a lot, but the real pull kept coming from somewhere else. Under the ads and the dashboards, what the brands kept describing was the operational grind. Reading invoices, matching them, filing them into the ERP, chasing vendors, reconciling numbers that never line up. The same steps every single day, done by people who had better things to do.
So we followed it. Predflow builds and runs AI agents for repetitive operational work, the high-frequency back-office stuff that sits between the systems a company runs and gets done by hand today. The agent reads the document, checks it, flags anything odd for a person, and files it where it belongs. We run it inside your own systems every day, and we stay accountable for the output under an SLA.
If you run ops or finance somewhere and one workflow is quietly eating your team, i would love to hear which one. Happy to answer anything here :)
Most dashboards just show you numbers and leave you guessing. We're about to start running Meta and Google ads for our launch and the idea of getting actionable recommendations on creative and budget allocation from day one instead of burning through budget while we figure it out is really appealing. How much ad spend data does Predflow need before the recommendations become reliable? Like does it work for early campaigns with small budgets or does it need a certain volume first?
Hi @ben_gend well it depends on which part you're asking about.
AI Creative scoring works from Day 1. Upload your creatives before you spend a dollar and Predflow will tell you what's wrong with the hook, the CTA, the awareness stage fit, etc. That's actually where most launch budgets get burned, and they are just fixable creative problems nobody caught.
For budget allocation and other recommendations, you need a few weeks of data.
The other thing that helps early-stage brands specifically: because your UTM data is clean from the start, you avoid the mess that most brands spend months trying to untangle later. And believe me its quite a task! Setting up the semantic layer before you launch means your Month 1 data is actually usable. Most brands come to us after the data is already broken.
Product looks good, any chance you tackle LinkedIn ads at some point?
Yes @jean_bonnenfant_tinct ! Its in our pipeline.
Congrats on shipping! Does the AI agent also suggest new audience segments or just optimize existing campaigns?
Hi @ermakovich_sergey ! Currently our agents optimize whats already running (existing campaigns). Budget allocation, creative fatigue, channel-level ROAS, root cause when something drops.
Audience segment suggestions are on the roadmap but we haven't shipped that yet.
What we do have is cohort-level analysis: new vs repeat customers by source, channel, product type, etc.
This ends up surfacing a lot of the same insights indirectly. "Your Meta campaigns are acquiring first-time buyers at 3x the CAC of Google" is a budget insight but it's also telling you something about who each channel is reaching.
Demo looks flashy but when I sign up and try to connect my Meta and Shopify, it does nothing. What to do?
Looks promising guys, congrats on the launch
Thank you @alara_akcasiz !!
Really like this shift from dashboards to actual decision-making support– understanding why something is happening and what to do next is where AI can bring the most value for marketers.
Connecting creatives, budget, and attribution into one feedback loop sounds especially powerful.
We also launched on Product Hunt today — building Ogoron, an AI system that automatically generates and maintains test coverage as products evolve. Different domain, but very similar idea of turning complex signals into clear, actionable outcomes :)
Good luck with the launch