We have always focused on getting quality work out the door fast. Over the last few months we have quietly added AI powered automation to our daily operations. The goal is to remove the repetitive manual tasks that slow us down so the team can focus on the important work. Here is a straightforward look at how we set it up and what it is actually doing.

The Setup

We built a custom AI assistant that runs in the background, connected to the three main tools our team already uses every day. When a new request comes in — whether a ticket or an email — it handles the intake, routes it to the right person, and updates the systems automatically. No more copying information between platforms by hand.

The assistant connects to:

  • Task management — for tracking jobs and team load
  • Storefront platform — for customer shops
  • ERP system — for production and fulfillment

It has read and write access so it can move data between them without anyone rekeying anything. The goal was to create a reliable bridge between the systems we already rely on.

The Dashboard

We built a simple internal dashboard. One screen shows everything:

  • A live ticket queue — assign work, set deadlines, update status without switching tabs
  • Emails become tracked tickets automatically — the sender gets an instant confirmation
  • A four-week timeline showing everyone's scheduled work stacked against their capacity
  • Basic analytics tracking cycle times by job type to spot bottlenecks early

What Runs on Autopilot

The AI only handles clear, repetitive tasks:

  • Logging tickets
  • Sending status updates
  • Updating products or storefronts
  • Checking job status
  • Generating reports

Anything that needs human judgment, direct customer contact, or carries real business impact still requires a person. Every automated action gets logged so it can be reviewed or reversed if needed.

We built trust slowly — testing small pieces first and expanding only after proving they worked.

The Stack

It's running on OpenClaw powered by Claude. The dashboard is basic Python running on our own servers. No customer data ever leaves our environment except through the same integrations we have always used.

Early Results

Very positive. We have automated approximately 75% of the total workload. We are still early in the process but the foundation is solid.

Next steps:

  • Prepress ops via Apogee
  • Production visibility
  • Proactive customer updates
  • Smarter workload balancing

If you are running a similar operation and want to talk about what we have learned, reach out. Happy to share.