AI plans rarely fail because of the AI
of retail companies name high data quality as a relevant success factor for AI projects. It remains the study's most important success factor.
of companies abandon most of their AI initiatives before production.
The bottleneck is almost never the model, it is the data foundation. We measure it, close the gaps that matter, and help your agents go live reliably.
AI advice is everywhere. Solid answers are rare.
Agentic replenishment and what it really takes
Coming soonWhy AI agents fail in production and how to see it coming
Coming soonAI in retail: use cases with proven leverage
Coming soonWhich data does AI need? The five questions before any project
Coming soonFoundation debt: why skipped levels earn interest
Coming soonThe AI readiness score: a heatmap, not a single number
See the method →Measure instead of assume. Evidence instead of gut feel.
Foundation Ascent does not rate your data landscape with a questionnaire. It rates it against verifiable evidence from your systems. Four levels, from the data foundation up to autonomy. The method is documented openly: criteria, maturity bands and scoring logic are public. Every measurement can be retraced by a third party.
A heatmap shows which data domain supports which level
More on the measurement →Reliability × autonomy: where your plan stands
See the target picture →Foundation, enablement, activation, autonomy. Each one carries the next
Understand the levels →Criteria, maturity bands, formulas: documented in public
Read the standard →Know first, then invest
The production readiness audit
One named use case. Three weeks.- How reliable your use case can become today, as an evidence-based number, not an opinion
- Which data layer caps it, and what closing that costs
- An action plan you can run without us
We close exactly that gap.
You start further up.
We tell you before you invest.
After that: vertical slice sprint (approx. 12 weeks to production) · Ascent programme
What you will rightly ask us
“Can we not do this ourselves?”
The method is documented openly, so please use it. Three things you will still not get internally: evidence instead of self-image, neutrality towards your own IT, and comparison values from other measurements.
“Do we have to clean up data for two years first?”
No. We only close the layers that cap your specific use case. Value in weeks, not years.
“We have already built an AI agent.”
All the better. The audit then shows why it has not reached production yet, and what will get it there.
“What do you see of our data?”
Evidence instead of raw data: reports, samples and logs inside your systems. An NDA before any system contact, access minimal and time-boxed.
“Does this replace our ERP?”
No. We do not build a system and we do not replace one. We make the data foundation reliable, the one your systems and AI plans fail on today.
Retail, 100 million to 1 billion euro. Three roles, one result
You get a process that runs reliably, instead of one more pilot.
You get the evidence for which data layer caps you. Measurable at last, instead of gut feel.
You get an investment sequence and proof of value in operations.