DATA & AI STRATEGY
Your data team is good. AI makes them better and faster
We help data teams adopt AI tools that cut the grunt work, raise the quality of what they ship, and free business users from waiting in the queue.
THE CHALLENGE
Most data teams spend more time maintaining than building
Writing boilerplate SQL, updating YAML configs, chasing missing test coverage, answering ad-hoc questions that never quite make it into a dashboard. The list of low-value work is long. And it crowds out the high-value work your team is actually good at.
AI tools can take a significant chunk of that load off. But knowing which tools to adopt, how to integrate them into your existing stack and processes, that's where most teams get stuck. The AI tooling landscape moves fast. What didn't exist six months ago is now production-ready. We track it so you don't have to, and make sure your team adopts what's actually worth using right now.
WHAT THIS LOOKS LIKE IN PRACTICE
This is what we typically work on together.
AI-assisted development. Your team spends less time writing boilerplate and more time on the work that actually requires their expertise. We help you set up and fine tune AI coding tools (GitHub Copilot, Claude Code, or Microsoft Copilot), in a way that fits your stack and your workflows. Writing SQL, generating dbt models and YAML configs, restructuring semantic layers in your BI tool, developing python based ETL scripts, reviewing pull requests. We cut through the "which tool should I use?" noise and get you to a working setup that your team will actually use.
Automated pipeline monitoring and fixes. When a pipeline breaks, AI analyses the failure, traces it to the source, and drafts a fix for your team to review and merge. Less time chasing errors, faster recovery, more reliable data delivery to the business.
Self-service for business users. Set up AI agents connected to your data environment so business users can answer their own questions. "Why is this metric down this week?", "Where do I find the data for this?", "What's the definition of this KPI?" Your analysts stop being a helpdesk and start doing the work only they can do.
Documentation and testing that keeps up. Column descriptions, model lineage, test coverage — the stuff that always falls behind. AI generates and maintains it as your codebase evolves, so your data platform stays legible without someone having to carve out time for it.
WHAT YOU WALK AWAY WITH
Configured, working AI setup
Tools installed and integrated in your actual environment. Not a demo, but a live setup your team uses from day one.
Team trained and confident
Your engineers and analysts know how to use the tools in their daily work and when to reach for AI and when not to.
Backlog of next improvements
A prioritised list of further AI integrations your team can tackle independently, so momentum doesn't stop when we do.