I get your data AI-ready and build the workflows and agents that handle the repetitive analysis — so your team stops copy-pasting numbers into decks and starts acting on them.
Four ways to bring AI into your analytics. Data readiness usually comes first — AI on messy data just produces faster mistakes.
Automate the repetitive analysis, reporting and monitoring your team currently does by hand — with humans kept in the loop where judgement matters.
Get your data clean, structured and documented enough that AI outputs are trustworthy rather than confidently wrong.
Custom agents that answer product and growth questions on demand, grounded in your own data and metric definitions.
Ongoing help evolving your AI-powered analytics as your product, data and the tooling landscape all keep moving.
The recurring reporting and monitoring that ate your team's time runs itself, reliably, without anyone remembering to do it.
Grounded in governed definitions and real data, with evaluation in place — so outputs are checkable, not just plausible.
The analytical throughput of a bigger data team, from the team you already have.
I go deep on your product, market and goals before touching a single event.
We set clear targets and a tight scope so we're aiming at the same thing.
I become part of the team — in your tools and chat, doing the hands-on work.
Regular insight and iteration, with everything documented so it stays yours.
No — and that approach is exactly what produces confident nonsense. Reliable AI analytics needs governed metric definitions, clean structure and evaluation. That groundwork is most of the work; the model is the easy part.
It's the right time for the readiness audit, not for agents. AI on messy data just generates wrong answers faster. We fix the foundation first, then automate on top of it.
No. It removes the repetitive work — recurring reports, monitoring, first-pass analysis — so the people you have spend their time on judgement and decisions instead of assembly.
Agents are grounded in your governed metric layer rather than free-associating over raw tables, and I build evaluation and guardrails in from the start so accuracy is measured, not assumed.
Whatever fits your stack and constraints — commonly Claude or OpenAI models, orchestrated with n8n, Zapier or custom code, sitting on your existing warehouse and analytics tools.
Book a 20-minute intro call. Tell me where your team loses time and I'll tell you honestly what AI can and can't fix.
Book an intro call →