Elevon in ForbesFour AI Teammates for a Two-Person Merch Brand
Furt Dačo is a small Shopify merch shop (socks, tees, caps) run by two people after hours. No investment, no big team. Instead of hiring for stock, product, strategy and marketing, they built four AI agents that pull their own data and report back.
Client
Furt Dačo
Industry
E-commerce
Solution
Custom AI agents (Elevon)
Deployment
Production
No investment. No big team. Two founders and four AI agents.
The Challenge
Furt Dačo started the way most side projects do: two people, a Shopify store, and a line of merch (socks, tees, caps) built in the evenings. No funding, no team to lean on.
At that size, every hour and every slow week is felt directly. There is nobody dedicated to inventory, nobody watching the product portfolio, nobody owning strategy, and nobody making sure the brand actually posts and markets. The founders were doing all of it themselves, between everything else.
Hiring four people to cover stock, product, strategy and marketing was never an option for a project this small. But ignoring those four jobs is exactly how small brands quietly stall.
Why Elevon
Four role-based agents, not four hires
Stock, product, strategy and marketing each got their own AI agent. The work that would normally need a small team is covered by agents the founders built themselves.
Agents pull their own data and report
The agents read live data from Shopify, evaluate it, and send back finished reports on a schedule. No manual exports, no spreadsheets, the analysis lands in the inbox.
No code, no investment
The whole setup was built by two people after hours, with no developers and no budget. AI started small, on a project where it was safe to learn.
“You don't fall in love with AI at a conference. You fall in love with it the moment it takes work off your hands that you didn't want to do anyway.”
The Rollout
Four agents run the back office, each with a clear job. They connect to Shopify, pull the data they need, evaluate it, and deliver a formatted report, some saved to file, some sent straight to the inbox on a schedule.
What this looks like in practice
Once a month the founders open their inbox to a finished strategy report: what to launch, which bundles to push, where stock is about to run out. The product agent flags a rising trend worth testing; the stock agent says exactly what to reorder and from whom. They review, decide, and get back to the parts they enjoy.
The suite
Sources
AI agents
Output
Illustrative reconstruction of the production suite.
Monthly strategy report
Real output format, recreated with blind sample data.
The Results
Four AI agents running in production on a real Shopify store
Stock, product, strategy and marketing covered without a single extra hire
Agents pull live Shopify data and deliver scheduled reports straight to the inbox
Reorder decisions, dead-stock alerts and new-product ideas, all prepared automatically
Founders freed to spend their limited time on the work they actually enjoy
4 agents
0 new hires
Shopify
data pulled automatically
→ inbox
reports on a schedule
The point is not a headline number. It is that a two-person brand now runs four back-office functions on autopilot, on data it never had time to analyze before.
“This is the right way to adopt AI. Don't start on the hardest processes. Start on something small, learn its limits, learn to trust it, and only then let it go deeper.”
Why It Worked
Most AI adoption stalls because it starts in the wrong place, on the biggest, hardest, highest-stakes process in the company, where the cost of getting it wrong is high and trust is low.
Furt Dačo did the opposite. It started small, on a low-stakes side project, where it was cheap to experiment and safe to learn the limits. The founders got to see exactly what the agents were good at, build confidence, and only then think about going deeper. That first skill, learning to trust AI on real work, is the one that scales.
And the agents earned that trust the honest way: by taking over the back-office work the founders didn't want to do anyway, and handing back finished answers instead of more dashboards.
Want a similar transformation in your organization?
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