Lakehouse delivery
Medallion architecture, Delta Lake, and the governance layer that makes the data usable by an AI workload rather than only by an analyst.
Partnerships
Production experience across
every platform we name
A Databricks Bronze partnership, and daily production work across the major cloud, data and model providers.
A platform recommendation is only worth something if it is made against your estate rather than against a preferred answer. We hold a Databricks Bronze partnership because it gives us delivery access and enablement, and we build daily on Microsoft, Snowflake, Anthropic and OpenAI. That range across platforms is why we will sometimes tell you the platform you already own is fine.
Platform partners
Where the delivery actually runs
Every platform below is one we have production experience with, not one we have read the documentation for.
- Microsoft AzureCloud, HITL pipelines, Azure OpenAI
- Azure AI FoundryModel catalogue and agent tooling
- DatabricksBronze partner · Delta Lake and ML
- Google CloudCloud platform and data services
- Anthropic ClaudeDocument reasoning
- OpenAIExtraction and analysis
- Microsoft FabricUnified analytics and OneLake
- SnowflakeData cloud analytics
- LangChainAgent orchestration
- dbtTransformation and testing
Databricks
Bronze partner in the Brickbuilder
Partner Network
Lakehouse delivery under a formal partnership, alongside Fabric and Snowflake work where those fit the estate better.
See the Databricks practiceML and model serving
Model training, registry and serving inside the same governed boundary as the data, so lineage runs end to end rather than stopping at the feature table.
Enablement, not lock-in
The partnership gives us delivery access and enablement. It does not change what we recommend when Fabric or Snowflake is the better fit for the estate.
Regulatory frameworks
The regimes every build is designed against
These are not partnerships. They are the obligations we design into architecture from the first session, because retrofitting them costs several times as much.
- FCAConsumer Duty and AI principles
- Lloyd’s Blueprint TwoMarket data and process standards
- EU AI ActHigh-risk AI classification
- UK GDPR / ICOLawful processing and transparency
- NHS DSPTPatient data security and protection
- PRA SS1/23Model risk management expectations
Vendor neutrality
What neutrality means in practice
- The recommendation follows the estate, not a house platform:
Cloud, data platform, model provider and tooling are each chosen against what you already run and what the workload actually needs, which is why the answer is sometimes to keep what you already have.
- We recommend against building where the foundation is not ready:
Where the AI-Ready Blueprint shows the data estate cannot support the use case, the roadmap sequences the data work first — even though that is the slower and less profitable recommendation for us to make.
- Every platform we name is one we run in production:
We do not list partnerships as credentials for platforms we have only evaluated. If it appears on this page, we have delivered on it and can talk about what it does badly as well as what it does well.
- The deliverable is yours to take anywhere:
Blueprints, roadmaps and architectures are written to be executed by whoever you choose, including your own team or another firm. No lock-in is designed into the deliverable.
