Data & AI

What we do

Data work here starts with what an organisation already records, not with a model. Once that data is reliable and governed, applied AI becomes a question of whether a model measurably improves a specific decision, which is something that can be tested rather than assumed.

• Data Platforms

  Pipelines, storage and modelling that turn scattered operational data into something consistent and queryable.

• Applied AI / ML

  Models aimed at a defined business problem, with the evaluation needed to show whether they actually help.

• Governance & Accuracy Tooling

  Lineage, validation and review steps so an output can be traced back to its inputs and corrected.

• Analytics

  Reporting and metrics built on the same governed data the rest of the business already relies on.

Technology we work with

The tools this practice reaches for most often. We pick per project rather than per preference, so this is a starting point for a conversation, not a fixed stack.

• Python
• FastAPI
• PostgreSQL
• AWS
• Git

The other practices

Most programs need more than one of these. They are run by one accountable team rather than handed between separate vendors.