From SQL to shipped AI features.
Moonlabs is the operator-led AI Academy in Derby. We run three live companies — Homemove, home.co.uk and homedata.co.uk — and we teach twelve students per cohort to ship a real AI product, sell it to a real customer, and raise on it. Three pillars: Coding, Commercials, Investment. Twelve weeks. £6,000.
Moonlabs is what we are. Two operators — James Freestone and Louis O’Connell-Bristow — who run Homemove, home.co.uk and homedata.co.uk. The whole homedata.co.uk business is the data layer underneath two consumer products — we live in warehouses, dbt, metric definitions and observability every working day.
The Academy is what we do. A twelve-week, in-person, twelve-student cohort in Derby. You build a real AI product. You sign a paid pilot on it. You write a deck and a financial model. You leave with a deployed system, a paying customer reference and a live investor pipeline. Coding, Commercials, Investment — the three pillars taught in equal weight every week.
Why this page exists. A serious data analyst in 2026 knows the data better than anyone else in the company — and has the shortest distance to AI engineering of anyone in the building. The dashboards, the SQL, the metric definitions, the joins are most of the work of an AI feature already done. You leave the Academy as the analyst who is already the AI engineer — an upgrade most teams quietly promote into a senior band, and a wedge that lets some graduates leave to found data-AI products of their own.
Coding · the data-AI stack, built
Agents that query your warehouse, retrieval over your business knowledge, structured outputs on dashboards, evals over the AI features themselves, observability that actually explains what changed when. A deployed data-AI feature by week twelve — the bit your team has been waiting for someone to actually ship.
Commercials · the analyst-to-AI-engineer pay jump
AI-engineers-who-also-do-data are the most-asked-for shape at AI-native startups right now and command Staff-Eng compensation. Pricing your own data-AI offer, the discovery call, a one-page pilot agreement, the first paying customer. A paid pilot by week six.
Investment · raising on data-AI products
Hex raised $52m, Glean at $4.6bn, Sourcegraph, Cube.dev funded by Bain Capital, Motherduck by a16z, dbt Labs at $4.2bn — data-AI is one of the densest funded categories in vertical SaaS. Cap table, ten-slide deck, financial model, term sheets. A live investor pipeline by demo day.
Common questions.
I am mainly an SQL and dashboard analyst. Is this realistic?
Yes — arguably the strongest starting point. Your existing data fluency is most of what you need; the AI engineering layer is what we add in twelve weeks.
I am a data scientist with some Python. Will I be bored?
No — we will push you further. Data scientists tend to reach the highest ceiling in the Academy because the underlying maths intuitions are already there; the engineering and commercial pillars are what get bolted on.
Will I learn fine-tuning and traditional ML?
Lightly. Almost no useful AI engineering in 2026 trains models from scratch — the work is engineering on top of frontier models. If deep classical ML is what you want, an academic masters at a serious university is the better artefact.
Can my employer fund this?
Often yes — the Apprenticeship Levy and L&D budgets can apply. Many data teams have explicit AI upskilling allocations. Full funding routes here.
How does this compare to the Academy course for product managers?
Same Academy, different default project shape. Analysts gravitate to features over data systems; PMs gravitate to features over user workflows. PM page.
More Academy entry points.
The Academy is one course with many doors. Each of these pages is a different entry point into the same twelve weeks.
Build it. Sell it. Raise on it. In twelve weeks.
Tell us where your data work sits today and what AI feature you would ship on top of it first. James and Louis read every application personally and reply inside the week.
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