Your model is a commodity. Your foundation is the moat.
The model layer is a commodity you rent. The data foundation is the asset you own, or the leak you inherit.
Every competitor rents the same models. The durable advantage is the layer underneath, and most AI strategies skip it. We build it:
A data architecture designed for models and agents, not just dashboards: first-party, semantic, real-time where it counts.
Ownership, quality, lineage, and residency fixed on your own warehouse. No-copy, EU-resident, auditable end to end.
A use-case roadmap scored on the frontier-vs-sovereign framework, so each deployment compounds the foundation instead of leaking it.
Non-personal tasks routed to frontier models, EU-routed, by policy, not by accident.
Results land in the governed foundation, logged, lineaged, and yours to keep.
The foundation is the asset. Models come and go on top of it, at your choice, on your terms.
The AI-native consultancy market has settled into three layers, and the honest question is who ends up owning each. We build all three on a foundation you already have, and hand every one of them back to you.
Your proprietary data, secured, stored, and processed at scale on a lakehouse you own. As a Databricks partner we build on it rather than replace it: Unity Catalog governs it, and nothing has to move for the AI to run against it.
The enterprise-AI platforms give you an operating system whose ontology lives inside their product. We build the operating ontology and agentic skills as your own versioned assets, run through a harness you control, so the workflows that turn data into decisions stay yours to audit and yours to keep.
A forward-deployed engineering shop will design, build, host, and maintain it for you. We embed engineers of the same calibre, then leave the ontology, the harness, and the models in your hands. We build ourselves out of the dependency, not into it.
Databricks does the heavy lifting underneath. We add the engine and the people on top. The difference is who owns the layer that holds your judgment when we leave, and the answer is you.
The disruption is not theoretical. Named, current evidence, cited, not claimed.
The WEF/Accenture AI Playbook for Financial Services 2026 finds data analytics is the #1 AI focus at 68% adoption, ahead of GenAI and agentic AI. The advantage has moved from the model to the data foundation beneath it.
WEF / Accenture →ING is publicly rewriting its core systems because, without that groundwork, even the best AI projects become “cosmetic patches on 30-year-old logic.”
Industry reporting →Design the data architecture, governance operating model, and AI use-case roadmap that make every model useful: first-party, AI-consumable, and EU-resident by default. The layer that decides whether your AI compounds or leaks. As a partner in the Databricks Partner Program, we help teams already on the lakehouse get more from it: Unity Catalog governance and Mosaic AI put to work behind a sovereign inference layer you control.
A transparent cost model comparing frontier-lab API spend to self-hosted open-weight deployments on European infrastructure. Includes break-even analysis for the most common enterprise workloads.
Every bank now rents the same frontier models, BBVA put ChatGPT on ~120,000 desks, JPMorgan on ~200,000. After the Reset the advantage moved underneath them: to the integrated, governed, sovereign data foundation that decides whether any of it works. A field guide for banking CDOs.
In one 90-minute working session we map it across the frontier-sovereign axis, cost, risk, and strategic value. Then we tell you what we'd do.