AI implementation for ecommerce
AI in your store, built by the person who runs it (me).
I'm one person running 14+ brands, on systems that already do real work. Tell me what's eating your week and I'll tell you the first thing worth building.
- Live agents, not vaporware—there's a working one right here now.
- A product-finder that answers real customer questions, already live on a client's storefront.
- No agent or workflow makes a move until you sign off or approve.
What 'AI doing the work' actually looks like
Builds run from a $2,500 roadmap sprint to bespoke systems. Every number is public.
See every number ›Live agent right here, right now
Don't take my word for it—ask one.
This is one of the small agents I build, pointed at my own services. Describe the messy part and watch it work, then scroll up and tell me yours.
Or tap an example
A live demo of the kind of thing I build. It only recommends real strivefortone services, and it won't invent one.
FAQ before you send it over
A few answers before you tell me what's broken.
What does AI implementation for an ecommerce brand actually involve?
One scoped build, pointed at something already costing you—leaking ad spend, a report no one trusts, 4,000 products no one wants to write. Not a strategy deck. A working system with a clear job and a human check on anything that touches money.
How is this different from an AI agency?
You get the operator, not an intern. I build the thing, wire it to your data, and I'm the one who answers when it does something weird on a random Tuesday. I run these systems across 14+ brands of my own—this isn't a roadmap and a junior.
What does an AI build cost?
A roadmap sprint starts at $2,500. A core build runs $5,000–$9,000 depending on data and integrations. Multi-system or custom builds start at $12,000, scoped up front. Once it ships, the Intelligence Retainer is $3,000/month.
Do I need my data sorted first?
Usually that is the first build. The Data Backbone connects and verifies Shopify, GA4, the ad platforms and your CRM—because every clever layer above messy data just guesses faster.
What's the smallest way to start?
A roadmap sprint, or a single AI Automation sprint on one bottleneck. Find the problem worth solving, build one thing that solves it, then decide what's next.