Data, AI & Analytics

Most AI projects that stall do not stall on the model. They stall because the data is scattered across systems with different definitions of a customer, no owner, and no pipeline anyone trusts. The model was the fast part.

This unit builds the foundation first: data architecture, ingestion and transformation pipelines, definitions people agree on, and lineage so a number can be traced back to its source when someone disputes it. Then analytics and reporting that answer questions rather than filling dashboards.

On top of that sits the model work. Classical machine learning where the problem is prediction on structured data. Language models where the input is documents, tickets or conversation. Evaluation before deployment, because a demo proves nothing about behaviour on your real distribution.

Deployment and monitoring are the part most often skipped. Models drift, upstream data changes shape, and a system that was accurate at launch quietly degrades. We instrument for that and define who is on the hook when accuracy falls below the threshold. Our Human-Machine Interaction & Ethics unit covers the oversight design.

Typical mandates: a data platform build or consolidation, an AI use case taken from pilot to production, a model that has degraded, or diligence on a target’s data and AI claims.