A growing self storage operator wanted its facility pages to stand out in Google. That meant search results showing each site’s hours, location, reviews and common questions directly on the results page.

Behind the scenes, that depends on structured search data on every page.

The operator’s earlier approach used AI to read each page and write that data. It was unreliable: run it twice on the same page and you’d get two different answers. With more facilities opening, the problem was only going to grow.

R6AI gave the honest answer: this job needed a dependable system, not more AI. The operator’s website already stored every facility’s details in structured fields, entered by the team as part of normal page updates.

So R6 designed a tool that reads those fields directly and publishes the search data on every facility page. Four types of search data cover business details, reviews, storage information and common questions.

Because the data comes from the page itself, it updates whenever the team edits a page. Nothing drifts, and there’s nothing to re-run.

Every new facility the operator opens gets the same treatment with no extra work. It scales with the business instead of creating more of it.

Not every problem needs AI. Sometimes the most useful AI advice is knowing when a simpler, more reliable tool will do the job better.

We’d rather say so than sell something that only works in a demo. That’s the honest no in practice.

A self storage operator wanted AI working inside the business, not a generic demo. Its management had seen what the tools could do. What they wanted was proof that AI could take real work off their own team, on their own systems.

The team covered customer care, finance, marketing and facilities, each with different needs and very different comfort levels with technology.

Before building anything, R6AI ran a one-on-one call with every attendee. One call changed a whole stream: what the business assumed a role needed and what the person actually needed were completely different. We then set AI up on the operator’s own data, so the workshop would run on their numbers, not sample ones.

The team worked through four streams: AI foundations, customer care, finance and data, and marketing and operations. Everything was demonstrated live on the operator’s own data, and each person worked along on their own laptop.

The highlight for most of the room was watching AI do the work, not describe it. Research, analysis and real tasks were completed in front of them, using the business’s own information.

The team left with a working automation, a roadmap of what to build next, and clear rules for what goes into AI and what doesn’t. Each person had at least one thing to try at their desk that week.

The feedback was honest. The most common request was more hands-on time and smaller groups by skill level. Both now shape how R6 runs every workshop.

The clearest signal came after the day. One attendee said the moment it clicked wasn’t in the room at all. It was back at their desk, doing it on their own.

That’s the point of an R6AI workshop. We don’t leave a team with a demo to admire. We leave them able to keep going once we’ve gone.