Modern data platform foundations on Azure
Decisions your AI systems can act on, backed by a governed semantic layer over open table formats. We build the foundation and the operating model around it, not the models themselves.
AI projects stall on foundations, not on models
Gartner found that 63% of organisations either lack the data management practices AI requires or cannot confirm they have them, and expects 60% of AI projects to be abandoned through 2026 for want of AI-ready data. Without a shared contract between source systems and consumption, every team rebuilds its own definitions.
We build the layer that stops the rebuilding. A semantic model becomes the shared contract, governance sits inside the data pipelines instead of alongside them, and open table formats keep your data readable outside the platform that produced it. The decision is not which AI model to adopt but what your data platform can contractually answer.
A platform built in layers, each one holding the next
AI does not fail at the model. It fails underneath, where the data is fragmented, ungoverned or impossible to trust. This is the stack we build, bottom up.
Sources
ERP, CRM, IoT, files and third-party APIs, structured and unstructured
Ingestion & engineering
Reusable pipelines, batch and streaming, with tested transformations
Lakehouse foundation
Fabric, Databricks or Snowflake, chosen on your context, not on ours
Governance & quality
Ownership, lineage, access models and validation gates
AI-ready consumption
Data shaped for analytics, ML and agents, and trusted by both
One governed foundation instead of a platform per department. The same data serves analytics, machine learning and agents, with the same ownership and the same quality gates.
Vendor-agnostic: Fabric, Databricks or Snowflake, decided by your context and not by our shelf.
What blocks a platform from serving AI, and what we change
Five structural problems, straight from the offering.
Data locked in one platform. Proprietary tables tie your data to a single vendor and its roadmap.
Metrics that disagree. Every team defines revenue, margin or churn slightly differently.
Governance that blocks. Controls arrive as a queue and teams route around them.
Pipelines replicating everything. Data is copied between systems, multiplying cost and inconsistency.
Dashboards instead of decisions. Reporting answers what happened, never what would happen if.
What the engagement includes
The lakehouse foundation, the semantic contract layer above it and the operating model that keeps both alive after handover. Not for organisations still choosing a cloud provider, and not a fit where no single owner signs off business definitions.
Platform architecture
Lakehouse topology, storage strategy and the interoperability model based on open formats.
Semantic layer
Business definitions, hierarchies and metrics as a shared contract for analytics, ML and agents.
Governance and security
Ownership, lineage, access control and quality gates embedded in the platform.
Industrialized ML and forecasting
Forecasting and ML workloads running on the same governed foundation, not beside it.
What you take away
- Target platform architecture
- Semantic layer design
- Governance framework
- Industrialized ML setup
Assess the foundation first
Two weeks, fixed scope. You leave with a platform audit, the blockers to your AI roadmap and a prioritised build sequence.
Book the readiness assessment