Across every major assistant, we read what the models truly know about you, where they are guessing, and where a competitor is named instead. Then a senior team makes the fixes for you, and proves the change. Built for African brands the models barely know yet.
Most buyers read a single AI reply and act on it, instead of scanning a page of links.
Brands score well when named, yet rarely appear when a buyer describes the category.
Each assistant answers differently, so we read across every one, not a favoured few.
Most tools hand you a score and leave. Two things make our reading different.
We understand how buyers across Africa actually search: the languages they use, the local phrasing, and the established names you are measured against. Tools built for other markets miss all of it.
Seventeen years inside Google, and years teaching these systems at the Swiss Cyber Institute. We read why an engine answers the way it does, so we fix the cause rather than the symptom.
How every engine describes and recommends you, across the questions your buyers actually ask.
The exact answers each engine returned, so nothing rests on our word alone.
What the results mean, read by a senior analyst, not a raw score for you to decode.
Where your own pages help the engines that read them, and where they hold you back.
Every fix ordered by how much it will move your visibility, highest value first.
We make the changes that move visibility, then run the audit again to prove they worked.
A list of what is wrong is the easy half. The work that changes what AI says about you is the hard half, and it is the half we own.
The changes that move your visibility, made for you, not handed back as homework for your team.
The work goes onto your site and into the places the engines read, so nothing sits waiting on you.
We run the same questions again afterwards and show you the change in plain numbers.
The models learned the world from a web that is thin on African businesses. So a simple score misses what matters most here: whether AI really knows you or is guessing, and whose version of you it picked up. We read for five things a dashboard cannot.
How much of what AI says about you stands on real, checkable facts, and how much it is quietly filling in.
The same question gets a different answer from a local buyer and from one overseas. We show you both, because the gap can cost you.
When a buyer asks if you are reliable, safe or real, we check whether AI can find the proof to back you up, or whether it hedges.
Which places the models lean on for your kind of business, and whether those places carry a clear, current picture of you.
Whether AI carries you into the neighbouring markets you want to grow into, or quietly drops you at the border.
One audit reads across every assistant and AI answer box your buyers reach for, because your visibility looks different in each.
We build the set of prompts your buyers actually ask, by category, by comparison and by name, in the markets and languages that matter to you.
We build a fact sheet from your whole site, because companies say different things on different pages, and the engines notice.
Each prompt runs across every assistant and AI answer box we cover, and is captured word for word, so the findings can be reproduced rather than taken on trust.
We read where you surface, where a competitor is named instead, and why, turning raw answers into a clear diagnosis.
You get a ranked plan, and we do the work that moves visibility rather than leaving you a list of tasks.
We repeat the questions a few weeks later, so you see the movement in plain numbers rather than a promise.
We are building the clearest picture of how AI sees African business.
We are a team of technologists who understand the African tech economy from the inside: where its data is rich, where it is thin, and where AI quietly gets it wrong. That understanding is the whole point, because the gaps here are not the gaps the global tools were built for.
Our background runs deep in these systems: years inside Google, and years spent teaching machine learning and AI. We do not resell someone else's tool. We built our own to question the models and grade what they say, and a senior analyst reads every result.
We work from Zürich and Nairobi, and our focus is Africa, so the reading is grounded in how your buyers really search here, not borrowed from another market.
See a sample audit, then exactly where your own brand stands across the engines your buyers now ask first.
Request a demo