Data & AI architecture

A shared view of the business

The same customer, product, supplier or transaction can appear in several systems with different definitions. We develop shared models of the business, from core entities and relationships to detailed analytical structures. That provides a basis for consistent reporting, integration and sensible technology decisions.

Business systems and enterprise architecture

Most organisations have a mixture of established systems, newer cloud applications and manual processes. We look at business systems strategy, enterprise architecture and information flows together: where to simplify, where to integrate and where replacement is justified. The roadmap connects those choices to business priorities and delivery capacity.

Modern data platforms

We design how information is ingested, stored, transformed, curated and made available for reporting, analytics and AI. Platform options, data engineering, integration, security, development environments and operational support all need to work together. We consider the existing estate as well as the opportunities offered by modern cloud services.

Business meaning and semantic models

For AI, we extend metadata with explicit business definitions, relationships and semantic models, so that systems can interpret information in its business context. This includes the meaning of measures and entities, the rules behind them and their connection to trusted sources. Good semantics build on sound modelling and metadata management.

AI applications and integration

We design how AI applications use business information and interact with systems. This includes retrieval-augmented generation (RAG) to ground responses in relevant documents and data, and Model Context Protocol (MCP) connections to expose selected data and tools to AI applications. Architecture covers access controls, integration, evaluation, monitoring and the software needed to make the service work.

Platforms and tools

Our architecture work spans Microsoft Azure, including Fabric, Amazon Web Services (AWS) and Google Cloud (GCP), alongside Databricks and Snowflake. We advise on the role, selection and integration of data management tools such as Collibra, Microsoft Purview, Profisee and Ataccama. The right combination depends on the business, the existing landscape and the capabilities needed.

From design to working software

We combine architecture with practical understanding of SQL, data engineering, APIs and application development. That helps us work through detailed models, interfaces, development and integration choices with delivery teams, and identify where specialist implementation expertise is needed.