You have the data.
You’re missing the picture.
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
OOS catalog
15.3%
What your data looks like once it talks to itself. Orders, ads, returns, reviews in one place.
I solve data problems. That's the work I've been doing for years.
Insurance, AI, and large-scale data. That's the background Kardata is built on.
My day job is data. For the last few years I’ve worked in insurance, using AI to automate the manual workflows that slow teams down and to turn large amounts of data into insights the whole business can act on. I work on data at scale, in the terabytes, which means solving problems that are rarely off the shelf. Kardata is that experience, aimed at your business.
Scale
Pipelines that move billions of rows a night, and keep running the morning after.
Migration
Legacy systems moved to modern stacks while the old one keeps serving customers.
Reliability
Systems built to fail loudly, not silently. The quiet break is the expensive one.
AI on my own hardware
Models running locally. Your data never has to leave the building.
Most brands have the data. What they don't have is a data team.
So the tools pile up: store, reviews, ads, email, support. Nobody sees the full picture. Three things happen, silently, every month:
Stock runs out
Your best seller sells out and you find out when the orders stop. The signal existed three weeks earlier, in another tool.
Returns pile up
Every return costs the product, the shipping, and a customer. Which product, which channel, which reason: that answer never gets connected.
Reputation drifts
Your site shows one rating; the rest of the web shows another. You can't see the gap. Customers can.
Start with a diagnostic. Stay if it proves itself.
One project to find your leaks. If it pays for itself, we keep going, month to month. If not, you walk away with the findings either way.
The diagnostic
Two weeks. I connect the tools you already run, map the data, and hand you a list of what is leaking and what it costs. You keep the findings whether or not we continue.
The managed layer
Everything connected and maintained, a weekly read on what matters, and agents that watch your data so nothing waits six months to surface. Month to month, cancel any time.
No lock-in. The setup and the agents stay yours if you leave.
Three steps. Zero tech lift on your side.
Connect
I plug into the tools you already run. No rip-and-replace, no new vendor to learn.
Map
One layer where orders meet ads, reviews meet returns, stock meets sales.
Alert
A weekly read on what is working, what is leaking, and what to fix first.
Your AI agent is only as smart as the data under it.
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.
I researched 40 D2C brands before I wrote a single word of pitch.
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.
A short call. Your data, your numbers.
No pitch deck. I’ll show you what your data says, starting with the tools you already run.