One version of the truth — and models that survive Monday.
The pipelines, tests and ownership that make every number agree, and the plumbing that keeps a model working after the launch post goes up.
Is this you?
“Three teams have three definitions of revenue, and half our dashboards disagree with the other half.”
One definition per number, with an owner’s name on it and the path it came from in plain view.
“Our AI work keeps dying somewhere between the notebook and production.”
A path from training to serving your team can walk again, with the same numbers on both sides.
“Attribution is held together with hope and three spreadsheets.”
A clean trail of events, and segments that flow back into the tools your team runs campaigns in.
“I can’t say which systems hold a given customer’s data, and deletion takes two weeks.”
A catalogue of where personal data lives, and deletion and export that run themselves.
What changes
Two dashboards, two answers, and a meeting to decide which is real.
One number, one owner, and the path it came from is there to look at.
The model worked in training and quietly broke in production.
The same data feeds both sides, and a mismatch is caught before a customer sees it.
Nobody can tell you whether a row is trustworthy.
Every dataset has tests, a freshness check and a name attached to it.
What you get
Pipelines that are tested
Raw to trusted, with tests, freshness checks and a named owner on every dataset.
One definition per number
Revenue means one thing, queryable from every tool, and a change to it goes through review.
The path from training to live
Reproducible training, the same data on both sides, and a version you can roll back to.
A model that goes live carefully
It runs alongside first, then to a slice, then to everyone — and back out just as quickly.
Drift caught early
What goes in and what comes out are watched, so failure is a ticket on Monday rather than a bad quarter.
Personal data under control
A catalogue of where it lives, who can see it, and pipelines that delete and export on request.
How it goes
Audit and metric map
We map sources, datasets and the numbers your business actually uses, and score where they disagree.
A warehouse you trust
Tested pipelines with owners and freshness promises. The first business-critical number becomes single-source.
Features and models
Shared features, reproducible training, and the first model in production behind a flag with drift monitoring on.
Handover and rhythm
Your team owns the foundation with a rhythm that holds. We stay on for capability, not for keeping the lights on.
A trusted warehouse runs ₹1–2.5Cr over 12–22 weeks. Add ₹50–80L for shared features and the first model in production. Stop, ship or extend at the end of every phase.
Questions we get on the first call
Got any questions?
Ask — a founder reads every message.
Do we need all of this before we get anything?
No. The first trusted number lands in the second phase, and it is usually the one your leadership argues about most.
What does it cost?
A trusted warehouse is typically ₹1–2.5Cr over 12–22 weeks depending on how many sources come in. Shared features and the first model in production add ₹50–80L.
Which warehouse should we use?
Whichever you are already on, unless it is actively fighting you. We pick the one your team can own, not the one we happen to like.
Will you replace our data team?
No. We pair with them and hand the foundation over. Most engagements end with us doing platform work while your team owns the analysis.
How do you handle personal data?
Catalogued at the column level, access set per region or per tenant, deletion and export automated, and the path from source to dashboard visible for every column that holds it.
Can you do the AI work on top of it?
Yes — that is the agentic and decision work, and this is what makes it trustworthy. The two are usually paired.
For your engineering teamarchitecture · stack · how we run it
Have an intelligent system to build?
Tell us about the messy bit — the legacy system, the model that won’t behave, the workflow no one wants to own. We’ll come back with a discovery plan inside two business days.
