AI & Automation

Turn a portfolio of pilots into a funded, sequenced roadmap the board can hold to account.

We score each use case for value, data readiness, control burden and cost to run, then sequence funding with stage gates and stop criteria.

AI only works where the data is complete, accessible, controlled and connected to the workflow.

We assess lineage, quality, access, integration and ownership, then build the data foundations needed for priority use cases to move safely into production.

Model-risk frameworks need to hold for machine learning, LLMs, copilots and agents.

We extend model-risk and AI governance frameworks across inventory, tiering, validation, evaluation evidence, human oversight and live-use monitoring.

The pilot worked. We build the pipelines, controls and evidence that make it a production system.

We deliver data engineering, integration, testing, monitoring, release controls and governance sign-off, then take the system through go-live, hypercare and handover.

Generative AI and agent workflows need defined scope, grounded answers, permissions, human checkpoints and measured evaluation.

We build retrieval over approved content, agents with controlled system access, workflow automation around the model, and evaluation evidence that shows output quality before and after go-live.