· 1 min read
Getting Started with Data Infrastructure
A practical guide to building a solid data foundation for your business, from tracking to prediction.
// data infrastructure & ai
Data & AI for real businesses, not big tech.
I'm Mitch, founder and data professional. I speak business and tech, and I've built or rebuilt the data infrastructure behind leading B2B and B2C companies globally. Behavioural tracking, data warehousing, ML models, predictions in your CRM. Everything it takes to get the most value out of your data.
no vendor lock-in · you own the stack · business first
mitch@prod ~ %
$ stitch analytics orders crm --on customer_id
✔ 3 sources joined, one view of every customer
$ dbt build --select customer_360 ltv churn
✔ 3 models built, 14 tests passed
$ sync-predictions --dest crm,google-ads,meta
✔ 18,204 scores pushed to where your team works
$ ▊
trusted by teams at
// latest-writing
Tech Guides
// work-with-me
You don't need an AI transformation. You need more value from the data you already have.
AI runs on data, and most businesses have plenty of it. What's missing is the stitching: analytics in one tool, orders in another, the CRM in a third, and nothing joining them up. I bring it all together in a warehouse you own, into one useful view of every customer. That's the foundation the models, automation, and AI that actually pay off are built on. Here's where it often lands:
01 · ltv-modelling
Flying blind on customer value
imagine instead → Every customer scored for lifetime value, with cohorts that show you where the money actually comes from and which acquisition channels are quietly losing it.
02 · lead-scoring
Sales chasing dead leads
imagine instead → Every lead ranked by propensity to buy, live in your CRM, so your team calls the right people first while your competitors work through their list alphabetically.
03 · churn-prediction
Churn you only see in hindsight
imagine instead → At-risk customers flagged weeks before they leave, from behavioural patterns and engagement signals, with enough time left to actually save them.
04 · conversion-modelling
Ad platforms guessing at value
imagine instead → Predicted conversion values synced back to Google and Meta, so their algorithms spend your budget the way you would: on the users who turn into revenue.
// client-feedback
What clients say
"Aisle 3 has benefited tremendously from Mitch's expertise and dedication. Since being involved with us, he's been a driving force in solving the data challenges of online shopping aggregation, delivering remarkable results even on a shoestring budget."
Thomas Vosper
CEO at Aisl3 LTD
"We found Mitch online and got him in to help drive our data analytics approach. He was super hands-on, got our team going with several quick wins and set a strong platform for us to grow our in house data analytics ops. I'd recommend Mitch to any business loking to build out their data infrastructure."
Ryan LeBlanc
CTO at Hampr
"I've had the pleasure of collaborating with Mitch on numerous projects over the past several years. He's partnering closely with us at Marketcheck to integrate our marketing activities with our on-site analytics and CRM, enabling us to optimize campaigns for high-value leads more effectively."
Alastair Campbell
Head of UK Sales Strategy at Marketcheck UK
"Mitch's expertise in the AI and data space was instrumental in helping Pagnea secure grant funding, which allowed us to expand our team and take Mitch's ideas into development."
Will Jordan-Cooley
CEO at Pangea Chat
"Very smart with a PhD in copy/paste or some other clever data tech. Always made an effort to understand our business and contributed heavily towards the creation of our AI team."
Doug Scott
CEO at RedBrain