Orders, reviews, ads, returns, support tickets. They sit in tools that don’t talk to each other. I connect them into one view, and I run it for you.
karthik@kardata.co · replies within a day
Revenue (mo)
$589K
Return rate
4.2%
Stockout risk
Best seller · 12d
Review gap
1.7★
What your data looks like once it talks to itself. Orders, ads, returns, reviews in one place.
Hands-on, end to end. The person you talk to is the person who builds it.
Pipelines that move billions of rows a night, and keep running the morning after.
Legacy systems moved to modern stacks while the old one keeps serving customers.
Systems built to fail loudly, not silently. The quiet break is the expensive one.
Models running locally. Your data never has to leave the building.
So the tools pile up: store, reviews, ads, email, support. Nobody sees the full picture. Three things happen, silently, every month:
Your best seller sells out and you find out when the orders stop. The signal existed three weeks earlier, in another tool.
Every return costs the product, the shipping, and a customer. Which product, which channel, which reason: that answer never gets connected.
Your site shows one rating; the rest of the web shows another. You can't see the gap. Customers can.
Start with a fixed-fee diagnostic. Stay if it proves itself, month to month.
Two weeks. I connect your tools and find the leaks: stock, returns, review gaps. You keep the findings either way.
The full service: one unified view, weekly insights, everything connected and maintained. Month-to-month, 30-day notice.
Lower base, shared upside. You pay more only when I prove I saved you money.
An inhouse engineer costs $150–200K/year. But the whole layer costs $78K/year. And you can cancel next month with the developed setup and autonomous agents.
I plug into the tools you already run. No rip-and-replace, no new vendor to learn.
One layer where orders meet ads, reviews meet returns, stock meets sales.
A weekly read on what is working, what is leaking, and what to fix first.
Same question, two answers. The only difference is what sits underneath.
AI, no data
Generic. No numbers. No action.
AI + your connected data
Your numbers. Your forecast. Your action.
AI is the interface. The connected data layer is the answer. An agent on fragmented data fails. An agent on connected data is an analyst that never sleeps.
Real public data, mapped across every brand. What the research surfaced:
brands analysed across US, UK and EU
had subscription friction their own dashboards never showed
carried product-safety signals visible only in scattered reviews
I read what is already public, connect it, and find what you couldn’t see. Your data will tell me far more.
No pitch deck. I’ll show you what your data says, starting with the tools you already run.
Book a call