The core layer: warehouse, medallion architecture, semantic layer. Everything else stands on it.
Every capability on this site, the search, the chat, the automation, the forecasting, has the same dependency. AI is only as good as its data core. Give a model clean, governed, well-modelled data and its output can be trusted. Give it silos, duplicates and three conflicting versions of last quarter, and it will produce confident answers that fall apart the first time someone checks them.
So the foundation is where we start, on every project, without exception. In practice that means a cloud data warehouse, a medallion architecture that refines data in stages from raw to business-ready, and a semantic layer that gives every tool and every model one agreed meaning for every number.
Getting there usually involves moving things: data out of legacy silos into a cloud-native environment, and often machine learning models out of ageing infrastructure into platforms built for scale and observability. We do both, and we treat the move as an upgrade rather than a lift-and-shift. If you are weighing up the destination, our guides to data lakehouses and the cloud migration roadmap are a sensible place to begin.
Data organised in governed layers, bronze to silver to gold, so everyone knows how refined the data they are using is.
Automated pipelines move data in parallel with live systems. Every dataset is validated, versioned and verified on the way across.
Existing models moved to cloud-native environments with better runtime efficiency, reproducibility and monitoring, validated to perform as well or better.
Full lineage, encryption and access control from end to end, so the foundation is audit-ready from day one.
We audit your data sources, models, formats and dependencies, then define the migration path that fits your workloads and your stack.
Schemas standardised, duplication removed, integrity validated before anything moves. Migrating a mess just gives you a faster mess.
Data and models move to the target environment in parallel with live operations, packaged and containerised where that makes the transfer safer.
Accuracy, completeness and access controls verified against compliance and quality standards. Models are benchmarked for latency, throughput and accuracy.
Observability, automated updates and MLOps practices keep the foundation reliable as workloads and use cases grow.
Structured, semi-structured and unstructured: databases, files, logs and documents. We design migration paths for cloud, hybrid, on-prem and multi-cloud environments.
Minimal to none. Most migrations run in parallel with your live systems, with validation checkpoints and checksum verification confirming that data lands complete and consistent.
Yes, from traditional ML models to fine-tuned LLMs. Every model is validated before and after the move, so performance is equivalent or better, and typical model migrations complete in 2 to 6 weeks.
Most clients have usable, structured datasets within 2 to 4 weeks of starting. The full foundation builds out from there in phases, use case by use case.
A way of organising a data platform into layers: bronze holds raw data as it arrives, silver holds it cleaned and validated, gold holds it modelled for the business. Each layer is governed, so you always know how refined the data you are using is.
The foundation is the least glamorous thing we build and the only one none of the others can survive without. Talk to us about where your data lives today, and we will give you an honest read on how far it is from AI-ready.
Tell us where your data is today and what you want AI to do. We will come back with a straight answer on what your foundation needs and where the quickest real win is.
Talk to us