Every AI plan rests on one assumption: “our data can carry this.” Most can't, yet.
Strategies fail quietly, months later, when the model meets the data. Fields that don't exist, systems that don't talk, quality nobody measured. By then the budget is committed and the credibility is spent.
We do the diligence up front, hands on the actual systems rather than a questionnaire. Engineers query your data, test your pipelines, and interview the people who own them. You get the unvarnished read and a costed plan to close what's missing.
FOR LEADERS WHO
want the gaps priced before the program is funded.
FOR TEAMS WITH
a roadmap that assumes data nobody has verified.
FOR PROGRAMS THAT
stalled once already on data surprises.
01 What you get
01
The data reality map.
What data you actually have, where it lives, who owns it, and how good it is, mapped against the use cases you intend to fund.
02
The infrastructure read.
Whether your platform can train, serve, and govern models in production, and where it will buckle first.
03
The gap plan.
Every gap priced: what it costs to close, what it blocks until then, and which fixes unlock the most value soonest.
04
The team assessment.
The skills your organization has, the ones the plan requires, and an honest read on hire versus train versus partner.
02 How it runs
Four phases, hands on the real systems.
Diligence done by the engineers who would build on it. No questionnaires.
PHASE 01
Inventory.
We map the estate with the people who run it: sources, systems, owners, and the paths data actually takes.
PHASE 02
Interrogate.
Engineers query the data and exercise the pipelines directly, testing quality, access, and lineage against the intended use cases.
PHASE 03
Price the gap.
Each gap gets a cost, a fix, and a list of what it blocks. No hand-waving about “data cleanup.”
PHASE 04
The verdict.
A readiness read you can plan against: what to build now, what to fix first, and what to defer until the foundations hold.
03 Questions we answer
Can our data carry the use cases we're about to fund?
What's usable today, without new plumbing?
Which gaps block which initiatives?
What does closing each gap actually cost?
Can our platform serve models in production?
Do our teams have the skills to own this?
04 Continue the arc