A patient AI teacher for your city's businesses

Your budget question is narrower than a software licence usually makes it. The product is sold as lifetime tiers rather than a monthly seat, and the figures sit in the publisher's own table, so you check them there rather than in this guide. What you are paying for is a teacher that researches a described task, returns options with live prices, then walks the method with you.

For a business working one city, the scope is a defined job rather than a platform. You describe what you are stuck on, such as finding nearby companies that fit your service, and the assistant builds a brief you can read before committing effort. Four modes can be switched mid-session, so the same session can move from research into a walkthrough without restarting.

Sessions are recorded with replay, which matters when the person who ran the session is not the owner. Finished runs are saved as lessons, so a method you work out once stays available to the team. None of that spreads your budget across enterprise seats you will not use, which is the objection most local owners raise first.

Two lifetime tiers are published, Learner and Certified, and the differences between them are set out on the publisher's page rather than assumed here. Both give you the same core: you describe a task, the assistant researches it, and you receive options with live prices before any teaching begins. Read the tier descriptions against the work you actually plan to run.

The practical way to choose is to list the tasks you would hand over in the first month. A local service business usually starts with one: reaching nearby companies that could buy. That task needs the brief, the four modes and the replay, because you will want to check how the brief was assembled before you act on it. Match that list to what each tier states.

Tier names can suggest more than they deliver, so treat the published description as the source. If a tier lists a capability you cannot map to a task on your list, it is not a reason to pay more. If it lists something you would use weekly, such as saving runs as lessons for staff, that is the point to weigh.

Software priced for enterprises is the usual objection, and the shape of this product answers part of it. There are no per-seat monthly charges described in the brief, only lifetime tiers, so a small team is not penalised for its size. Whether the published figures suit you is a judgement you make against your own budget, not one this page can make for you.

A second doubt is whether an AI teacher can handle a local market at all. The product researches the task you describe, so the quality of the brief depends on how specifically you describe your city, your service and the companies you want. A vague request returns a vague brief. A tight one gives you something you can check line by line.

The honest position on completion is that it is not promised. Some sessions will teach a method and leave the final work to you, which is reasonable for a teaching tool but worth knowing before you pay. Plan for a person to review the brief and finish the steps rather than expecting a finished list to appear.

Moving from one lifetime tier to the other is a decision about repetition, not about ambition. If you expect to run the same kind of task every week, such as refreshing a shortlist of nearby companies, the higher tier earns its place only if its published description covers what that routine needs. Compare the descriptions directly before you decide.

The cheaper path is to start on the lower tier, run real tasks for a month and note where you hit a wall. Record what you could not do, whether that was a mode you needed or a lesson you wanted to keep. A concrete list of gaps is a better basis for upgrading than a feature table read once.

Upgrading after you understand your own routine also protects the lifetime commitment. You will know which tasks you repeat and which you tried once and dropped. If your work stays varied and single-city, staying on the entry tier is a defensible outcome, not a failure to scale.