A strong specialist skill is useless if the wrong system gets invoked. Long-running AI work breaks in boring, expensive ways.

That is not a prompting problem. It is a routing and authority problem.

How I can help you:

I build project routers for teams using multiple AI workflows, agents, or specialist assistants so that they work with each other as a team.

I design source-of-truth resolutions so your agents retrieve the current rules instead of improvising or relying on assumptions or stale chat data.

I create reusable skill registries and specialist handoff logic hardened by determined and dedicated flow states reducing the need for model reliance on its own internal and displaced memory context.

I separate project states with individualized canonical states bound by rules living under essential model instructions and routed properly so unrelated workflows do not contaminate one another, but exist with awareness of the role each project plays, enabling a persistently growing ecosystem to develop organically.

I add governance wrappers, escalation hooks, and recovery mirrors around existing AI stacks so that trust is designed at the root, and variance is eliminated with rule based source archiving and implementation.

Trust means closing your eyes at night and sleeping soundly because your agents got your back. And I can put it all together for you, precision results sourced and undisputed because I got tired of doing things the hard way.

Phillip, agree that routing is where it breaks. In our builds the worst bugs came from an agent pulling an old rule from chat history instead of the current source. Pinning one source of truth per project fixed more than any prompt change did.

A system I designed for local service businesses that consistently lose out on Google reviews. Full build in progress

Automated Review Requests for Local Service Businesses The Problem:

Google reviews are one of the strongest signals for local search ranking, this is well established across HVAC companies, cleaning services, salons, contractors, and landscaping businesses. Every owner in this space knows reviews matter. Very few of them ask for one consistently.

The reason isn't a lack of care, it's a lack of a system. Asking every customer, right after every job, every single time, is a small task with no natural trigger behind it. It depends entirely on someone remembering in the moment, usually right when the business is busiest and least likely to remember. The result is a steady stream of satisfied customers who were never actually asked, and a review count that grows far slower than it should. The Solution👍

I designed a system that removes the dependency on memory entirely, so a review request goes out every time, without anyone having to think about it.

The moment a job is marked complete in the CRM, the system picks it up automatically. It waits a short period first, so the request doesn't feel rushed or robotic, the customer gets a little time to settle in before being asked. Then it sends a review request by SMS or email.

From there, the system tracks what happens next. If the customer clicks through and leaves a review, that's logged as a win and the process ends there, no further messages, no risk of annoying someone who already did what was asked. If there's no response after a few days, the system sends exactly one follow-up, never more than that, since a second or third nudge tends to do more harm than good. Whatever the outcome, sent, clicked, or reviewed, it's recorded in a tracking sheet, so the business owner always has a clear, accurate picture of where every customer stands. The result is simple: every completed job gets a consistent, well-timed ask, with no manual list to maintain and no customer slipping through the cracks.

What's Next

The full workflow build, including the architecture and the tools behind it, is going up in the next few days.

That email trigger right after a job finishes should cut out the manual reminder step and keep reviews coming in.