“My people burn hours moving numbers by hand.”
Data Automation
Two directions on one foundation. Good Data In automates the systems your team already works in so clean data gets produced at the source. Good Data Out acts on what the analytics engine observes, drafted and waiting on your yes.
Who it's for
The work is real. The typing shouldn't be.
Someone rebuilds the same spreadsheet every Monday. Someone retypes numbers from one system into another and hopes they got the columns right. Someone chases three people for the fields that should have been filled in when the work happened.
That is expensive twice over. It burns hours you are paying for, and it produces exactly the kind of hand-copied data that makes every report downstream a little less believable.
Two directions
Good data in. Action on the way out.
Good Data In
Clean data, produced at the source
Automation inside the systems your team already works in, so the right information gets captured while the work is happening instead of being reconstructed later. Fewer blank fields, fewer copy-paste steps, fewer arguments about which spreadsheet was current.
The flagship: the attribution engine
Every lead, meeting, and deal stamped with where it really came from, modeled in your own warehouse rather than trapped inside one tool's reporting. If you are spending meaningfully on marketing and still guessing which spend buys customers worth keeping, this is the answer to that question.
Good Data Out
Something noticed it, and teed up the response
Action taken on what the analytics engine observes. A smart observer catches the change, surfaces it to the person who can do something about it, and prepares the response so the work is already drafted when they open it.
Always pending your yes
Nothing sends itself. The follow-up, the flag, the reorder note, the board pack: they arrive built and waiting for a person to approve them. You keep the decision and lose the preparation time.
How it works
Automation on top of a foundation, not instead of one.
Find the hours
We start by watching where the manual work actually is. The costly steps are rarely the ones people complain about loudest.
Fix the capture, not the symptom
Where data is being retyped or reconstructed, we change how it gets produced in the first place, inside the tools your team already uses.
Model it in your warehouse
The result lands in the governed layer rather than in a single tool's private reporting, so the numbers agree with everything else you look at.
Wire the response
Once the data is trustworthy, the observer can act on it: notice the change, tell the right person, and draft what comes next for their approval.
What it costs
Priced by the workflow, not by the hour.
Data Automation is scoped per workflow: we agree what gets automated and what it replaces before the work starts, at a fixed price. Most engagements start with one painful workflow rather than all of them, so you can judge the result before deciding how far to take it.
Stop paying people to move numbers.
Start with the free roadmap, or book a call and walk us through the workflow that eats the most time.