Four out of five reasons AI projects stall in production are upstream of the model. Data foundation is one of our standalone offers, and it ships inside every AI engagement we deliver, because skipping it is how AI dies.
Click any pain to see the evidence. Every claim carries a citation a CTO can verify in five minutes. If a number becomes unprovable, it comes off this page.
Customer behaviour and operational data live across 7+ systems with no shared key. Recommendation engines confidently recommend products from categories the user has never browsed. CRMs send re-engagement emails to users who churned three months ago. The model is not the problem; the join is.
Salesforce State of the Connected Customer (2024 wave 6, n=14,300): 73% of customers expect companies to understand their unique needs; 51% say companies generally fail. Forrester finds enterprises maintain on average 87 distinct data sources for the customer record alone.
A senior AI architect will read it and reply within one business day with a structured assessment: which pains apply, what we'd lock down first, where the real risk is. No call required.
Hover any step to read the rationale. These five are non-optional. They are why our delivered systems hold up after handoff.
Owners, freshness, schema, gaps. We know what exists before we touch a model.
Hover the spokes to see what each unlocks. The infrastructure that makes a recommendation engine work also makes BI dashboards reliable, real-time decisioning possible, and the next model you build cheaper.
Data foundation works two ways. It is a standalone engagement when a client needs the foundation in shape before any model work makes sense, and it ships inside every AI delivery as the precondition for everything we build on top. Either way, we treat it as a first-class line of work, not a hidden tax inside a model project.
If your data layer is in worse shape than your AI roadmap admits, the feasibility call will say so. Sometimes the right next step is data foundation work first, AI second.