What We Do
A data team,for the cost of one hire.
Hiring a head of data means a long search and a fully loaded cost north of $250,000. Then you still need engineers under them, and somebody to own the reporting. We engage as your data leadership instead, and we bring the engineering with us rather than leaving you to hire it.
The comparison
One hire, and you stilldo not have a team.
A head of data runs $250,000 to $350,000 fully loaded and takes months to find. Then you learn they cannot build the warehouse themselves, so you hire an engineer. Then somebody has to own the reporting.
You are two years and several salaries in before the function works, and the whole thing rests on one person not leaving.
We engage as that leadership instead. Two principals rather than one hire, the tooling on this site, and engineering capacity that does not scale with headcount. Same order of spend, running from the first month.
Why it lands early
Most of the stackis not a first draft.
The warehouse, the models and the reporting are a pattern we have built four times over. A new engagement starts from that shape rather than from a blank page and an argument about how a data stack ought to be laid out.
So the early weeks go to what is actually particular about your business, which is usually the operating logic nobody ever wrote down. That is the whole reason the first useful thing shows up quickly. The parts that repeat are already solved.
The AI work
Everyone is building.Nothing connects.
Finance built something that reads the close. Ops has a sheet with a formula nobody can explain. Marketing pays for a tool the rest of the company has not heard of. Each one works. None of them agree.
This is the nineties and departmental spreadsheets again, except the logic now sits inside a prompt nobody wrote down, and the person who built it is the only one who knows what it assumes. That is harder to audit than a workbook and it spreads faster.
We are not here to take the tools away. Someone in your company building their own thing is a good sign, and the companies that shut it down are the ones that stall. What is missing underneath is ordinary.
Governance
A written line about what AI may touch
Which systems an assistant can reach, what needs a person to approve it, and what never goes near production. Written down, so your teams can build without asking permission for every step and you can still answer an auditor or a customer who asks.
Coordination
One roadmap instead of five
Departments start building the moment the tools get good enough. The work is not stopping them, it is making sure separate teams are not solving the same problem in incompatible ways, and that what they build can be joined up later instead of thrown away.
Semantic model
One definition, read the same by people and machines
Definitions, driver trees and the rules for reading them live in one place and get served to your warehouse and your assistants alike. That is what DimTable holds, and it is what stops a model inventing a third version of revenue that sounds convincing.
An enterprise B2B company came to us with capable teams building with AI in three separate departments and no agreed definition of the numbers underneath any of it. We put the governance and the semantic layer in place so that work could keep going without three versions of the truth turning up in the same meeting.
The arrangement
What you actually get.
It all stays in your accounts
The warehouse, the models, the tooling and the access patterns live inside your own accounts for the whole engagement. When it ends you keep them, and there is nothing to hand back.
Running from the first month
At the point where a head of data is still being recruited, this is already producing reporting your team uses. Same order of spend, without the ramp to wait through.
Built four times over
The warehouse, models and reporting follow a shape we have built repeatedly for operators like you, so a new engagement skips the argument about how a data stack ought to be laid out.
A written line, not a moratorium
Teams keep building with AI, inside rules that say what an assistant may reach and what needs a person to approve it. Nobody asks permission for every step, and you can still answer an auditor.
What else
This one rarely shows up alone.
Comparing this against a hire?
We will lay out honestly what each one gets you over the first year.