LLMs, AI/ML and MLOps with AWS, Google Cloud, Azure, enterprise Databricks and Snowflake. Six engineering services, each with a reference delivery architecture, representative use cases and agreed measures.
Discuss an engineering priorityEach service opens a complete Expertise page with the business context, delivery architecture, use cases, animated scenario and the baselines we agree before starting.
Build assistants and agents that use your business knowledge and work within your access rules.
Use predictive models to support decisions in finance, operations and customer teams.
Give models and AI applications a controlled release process and an operating owner.
Design and modernise cloud data platforms around your workloads, security needs and budget.
Bring data engineering, analytics and machine learning together on your enterprise Databricks platform.
Modernise your data warehouse and deliver governed analytics and AI on Snowflake.
The platforms differ. The way we frame, control and hand over the work does not.
We agree the job, the data available and the measures that will show the work succeeded.
Access rules, security boundaries and operating responsibilities are part of the architecture, not an afterthought.
Every build includes evaluation, monitoring and a handover so your team can run it after go-live.
We will respond with a focused scope and the measures we would agree before starting.
