Data strategy consulting that ends in a foundation AI can actually use.
Your team built the AI prototype — and it stalled, because the data underneath it isn’t ready. We centralize, clean, and structure your SAP and legacy data so AI agents can do their job, then hand you a governed foundation, not a deck.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
Most teams call for data strategy services after the AI prototype stalls.
From a project review with a mid-market industrial manufacturer: a five-person internal dev team, SAP on-prem, Google Cloud chosen, and a customer-facing AI prototype they built themselves — on a free-tier model — that stalled when it met the data.
The prototype shines in the demo, then meets your data
Built on Gemini because it was free, the prototype answers beautifully — until it runs against real SAP extracts. Duplicates, dead records, fields nobody defined. The model was never the problem.
“Where we are the weakest is the data strategy”
Their words, not ours. A five-person dev team can pull SAP extracts into Google Cloud and build the app on top — but the treatment of the data through the application, the architecture, the administration of the systems: that is a second full-time job nobody is staffed for.
SAP has its limits
In the buyer’s words: “SAP has its limits… a lot of compromises done in the past years.” Years of workarounds, custom fields, and duplicate customer records are fine for humans who know the quirks — an AI agent takes every one of them literally.
Nobody scoped the clean-up
The real phase-zero job — “clean up the data and filter it properly” — sat between the extracts and the app with no owner. It is unglamorous, it is large, and the AI roadmap does not move until it is done.
Five developers can’t build the product and the platform
Your team is already stretched across the extracts, the app, testing, and architecture. Ask them to also design a governed data platform and both ship late — or the data work quietly never happens.
Agents can’t use data nobody governs
No golden record, no shared definitions, no access rules. Every AI agent you point at the estate inherits the mess — and the prototype stays a prototype.
An AI pilot that never ships still bills you every month.
The prototype looked free until you count the team-months spent circling the same data problems. Your numbers will differ — the first engagement puts figures on yours before anything gets built.
From anonymized calls with a mid-market industrial manufacturer — SAP on-prem, Google Cloud chosen, customers ordering by the truckload
AI data strategy is a build job: centralize, clean, structure.
Phase zero, in the buyer’s words, is that you have to have the data before you can do anything. Three steps, fixed order — the same foundation every later AI agent stands on.
- 01
Map the estate the agents will read
We inventory every source your AI will touch — SAP on-prem, the extracts already landing in Google Cloud, spreadsheets, shared drives — and define in plain language what good looks like for each: which records matter, which fields mean what, which duplicates die.
- 02
Centralize, clean, and structure it
The data lands in one governed place in your cloud — deduplicated, filtered properly, golden records defined, quality checks running. This is the clean-up nobody scoped, done as a build job: AI agents do the grunt work, engineers review the rules.
- 03
Hand over a governed foundation
Ownership, access rules, documentation, and quality monitoring ship with the data — plus a drawn line for where you keep human control. Then your prototype, and every agent after it, finally has something clean to stand on.
Legacy data migration that ends in a foundation AI agents can use — not a deck.
A data strategy consultancy maps your estate and hands over a roadmap — the cleaning, structuring, and governing becomes someone else’s problem, usually yours. We are an AI operating partner: the team that maps your data is the team that centralizes, cleans, and structures it inside your cloud, and we stay until your people run it.
One boundary, plainly: we ready the data, we do not rebuild your applications. If the software itself is the problem, that is our software modernization work — a different engagement. And because we build AI agents for a living, we know exactly what they need from data: this foundation is designed for the agents that come next, not just the reports you run today.
Where we are the weakest at the moment is the data strategy.
Led by Peter Enestrom.
ERP data migration, SAP, Google Cloud, and other questions.
Straight answers, before you book anything.
Most buyers mean something plainer than the phrase sounds: get the company’s data into one place, clean it, define it, and govern it — so the business, and the AI agents it is about to run, can trust what they read. Data strategy consulting done as a build job ends with a working, governed data foundation in your cloud, not a slide deck about one.
Because the model was never the hard part. Phase zero is the data: if the estate underneath is duplicated, undefined, and ungoverned, the prototype demos well and fails on real records. That is the phase-two seam we see constantly — a self-built prototype on a free-tier model, then a stall. The fix is not a better model; it is a data foundation the model can actually use.
Yes — we move SAP data into Google Cloud (BigQuery and Cloud Storage, with Vertex AI where the agents will run) without touching how SAP runs day to day. The real work is SAP data quality: deduplicating records, defining fields, and cleansing years of workarounds before the data lands, because moving messy data just relocates the mess. SAP keeps running; your AI agents finally get data they can trust.
Almost never. ERP data migration and clean-up can run while SAP stays exactly where it is — the foundation gets built alongside, and the ERP question becomes a later decision made on clean data, not a desperate first move. If the application layer itself needs rebuilding, that is a separate engagement — software modernization — and a separate decision.
Wherever you decide — in the buyer’s words, “where you should step in and manage for us where we need more human control.” Governance is part of the handover: named owners, access rules, and a drawn line between the decisions AI agents take alone and the ones that route to a person, with every agent decision logged against the rule it applied.
A governed data foundation in your cloud: golden records, shared definitions, access rules, automated quality checks, and full documentation — plus the walkthrough that makes your five-person team the owners, not the tenants. Support stays available; dependency is not the model.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.