Everything into one store. Then agents that read it for you.
You run on five or six systems that never talk to each other. Shopify knows the order, the carrier knows the parcel, the mailbox knows the complaint. Nobody sees all three at once, so a problem sits there until a customer gets angry or a month closes wrong. We fix that in two moves, and the second one is where the work disappears.
Scattered systems on the left. One ordered store on the right.
Three case studies.
Three systems we built and still run: a brand we ran ourselves, a product we shipped for other people, and this agency. Each one opens into a full write-up with the architecture, the actual screens and the numbers we have. The pattern behind all three is further down the page.
AI Cockpit with 3 agents for a supplement brand
Orders, tickets and tracking in one store. The first run found 13 real problems, including one paid for and never shipped for eight weeks.
Read the case study →From lead to off-boarding: agency automation
Twelve automations covering the whole arc of a client, all reading from one warehouse. Four decisions stay human on purpose.
Read the case study →AI Operating System with agents for dropshipping
Every capability its own agent, an orchestrator routing the work, and a guardrail no approval can override. Rebuilt from a dashboard in one week.
Read the case study →Three things that happened.
Not hypotheticals. Each one is from a system on this page, and each was found by a machine on a schedule rather than by a person who happened to look.
Two moves.
First every system goes into one store on a schedule, so there is a single version of yesterday. Then agents read that store and do one job each. Rules run first because they are free and certain; the model only handles what the rules cannot settle, and it never does the arithmetic on your money.
This arrives at 07:00.
You do not open a dashboard to find out something is wrong. The findings come to the channel your team already sits in, named down to the order number and the amount, and they stay open until they are provably closed.
Morning pass done. 412 orders checked, 13 need a human.
Three times a day · every item tracked until it is closed
One working thing, then the next.
We do not sell a six-month programme. We pick the job that is costing you the most attention, build that one on your own data, and you decide from there.
Thirty minutes on what breaks most often and which systems hold the answer. You leave with our read on whether this is worth automating at all.
Your systems into one place, and the first agent running on top of it. It posts into your Slack. You watch it for a week before it is allowed to matter.
Each new agent reads the same store, so the second one costs a fraction of the first. You stop whenever the next one is not worth it.