I read the answer before touching the page
I help Kenyan SMEs and marketers move from search visibility to AI-answer visibility by checking what public evidence already exists, where it conflicts, and which claims need to be made clearer in English and Swahili.
A business cannot be cited clearly when its public facts arrive like scraps from five different tables.
At the corner table of a noisy Nairobi café, three business owners once gave me the same complaint in three different languages of frustration. One had good Google rankings but an old branch page still named a closed location. One had map visibility and steady WhatsApp orders, yet the directory description called the service by the wrong category. One had reviews that any human buyer would trust, including an awkward three-star review that still named the real service clearly. When a customer asked an AI tool for a shortlist, they vanished, got flattened into a vague category, or appeared with the wrong service. That café scene became my teaching example for a larger problem. I had spent years writing service pages, cleaning directory entries, reviewing search snippets, and teaching practical visibility workshops. The crack showed me that ranking was no longer the whole job.
I am from Kenya, and I work close to the ground: service pages, sector pages, directory evidence, map descriptions, review patterns, and the small public details that help a business sound real outside its own website. My habit is an answer ledger. I write down the exact phrasing an AI tool gives before a change, then again after the public evidence has been cleaned, clarified, or translated. It is slow work. That is the point. The useful signal is often one sentence hiding under a pile of marketing noise: who you serve, where you operate, what you do, what proof supports the claim, and which language the buyer is likely to use.
I treat generative engine optimisation as disciplined source-making. A Kenyan business cannot rely on English evidence alone when customers also ask in Swahili. It cannot assume a directory listing will save a thin website. It cannot expect an answer engine to respect a service boundary that is never stated plainly. My job is to read the combined trail: website, maps, reviews, directories, social proof, sector mentions, and answer patterns. Then I help turn scattered facts into citable evidence, without pretending that anyone can promise a ranking or a guaranteed mention.
Path into the practice
- 2011
Service-page writing
I began writing service pages for local Kenyan businesses, learning where a page said plenty but proved little.
- 2014–2016
Directory and snippet cleanup
I cleaned up directory listings and reviewed search snippets, closing gaps between what a business claimed and what a search result actually showed.
- 2017–2019
Visibility workshops
I taught practical digital-visibility workshops for small teams that could not afford vague theory, focused on plain, provable claims.
- 2021
Mapping the compression
I started mapping how answer engines compress business descriptions, tracking where a business gets flattened into a generic category.
- 2023–2024
The answer ledger
I began keeping a handwritten answer ledger, comparing the exact phrasing an AI tool gives before and after public evidence changes.
Bring the messy evidence before you write another page.
I will read what answer engines already say, then show which public facts need repair.
Send the case