AI visibility begins where professional evidence becomes readable.
I teach small independent accounting studios how generative AI systems may read, compress, and repeat their public business information. The course follows ordinary studio evidence: websites, chamber records, directories, reviews, service pages, and the phrases clients use when asking about invoices, payroll, compliance, or company setup. I do not treat AI answers as magic. I inspect what can be checked, mark what can only be inferred, and build habits a local practice can repeat.
Livia SannaroWhat the course covers
This free mini-course contains 15 lectures with short self-check tests. It is made for accounting studio owners, partners, and staff who already know how their practice works, but want to understand how AI systems may describe it when business owners ask practical questions. I look at language models in plain terms, then move into hallucination, source trails, directory contradictions, multilingual naming, reviews, credentials, recommendation behavior, correction paths, and monthly monitoring. There is no enrolment pressure, certificate wall, or promise of command over model outputs. The material is meant to be read, tested, and repeated with your own public evidence.
- 15 lectures
- 5 tracks
- €0 tuition
Four ways an AI answer reshapes a small accounting studio — it names the practice, narrows the service, borrows nearby evidence, or leaves the firm unmentioned.
The lecture index holds the full course notes in one place. You can read them in order as a guided path, or return to one issue when a studio description looks wrong, vague, or borrowed from a nearby source. Each lecture ends with a small test for private checking.
Make your studio evidence easier for machines to read.
Start with one query, one answer, one source trail, and one correction path.