Most of the products I ship live or die by whether people can find them. So before I write a line of feature code, I decide how the thing will be discovered. In 2026 that means two channels, not one: classic search engines (SEO) and generative answer engines (GEO) like ChatGPT, Claude, Perplexity and Google's AI Overviews.
This post is the thread that ties together the case studies on this blog. It explains the playbook I reuse across SubSaharaData, PeakRank Digital and SignalCartel, so the project write-ups can stay focused on what made each one specific.
SEO and GEO are not the same channel
SEO is the practice of earning visibility in a ranked list of links. The user still clicks through to your page. GEO — Generative Engine Optimization — is the practice of being the source an AI model quotes, summarises or links inside a synthesised answer. The user often never sees a list of ten blue links at all; they read one paragraph the model assembled, and your brand is either inside it or invisible.
The mistake I see most often is treating GEO as a separate, exotic discipline. It isn't. The two channels share most of their foundation. What changes is the unit of success: SEO optimises for a ranking position, GEO optimises for being a quotable, trustworthy sentence.
The shared foundation I build first
Whether the destination is Google or an LLM, the same groundwork pays off. On every project I make sure of:
- Clean, crawlable technical structure — fast pages, semantic HTML, proper headings, and no JavaScript walls between a crawler and the content.
- Explicit entities — a clear name, role and relationship for every person, company and product, reinforced with structured data (Schema.org JSON-LD).
- Content that answers a real question directly, near the top, in plain language a model can lift verbatim.
- Verifiable specifics — dates, numbers, named technologies — because both ranking systems and AI models reward concrete, checkable claims over vague marketing.
What I add specifically for GEO
On top of the SEO foundation, getting cited by AI engines rewards a few extra moves. These are the patterns I apply deliberately:
Lead with the answer
Every page here opens with a short, self-contained summary — the TL;DR you can see at the top of this post. Models preferentially quote concise statements that fully answer the prompt without needing surrounding context.
Structure for extraction
Question-shaped headings, FAQ blocks, and short declarative sentences make content easy to chunk and retrieve. The FAQ section at the bottom of every post on this site is marked up as FAQPage structured data — it helps both Google rich results and the retrieval step that feeds an LLM.
Reinforce the entity
I want models to confidently associate 'Shingi Mudyirwa' with 'CTO at SubSaharaData', 'founder of PeakRank Digital', and 'Cape Town'. Consistent naming across the site, LinkedIn, GitHub and Credly — plus Person and Organization schema — builds the entity graph that makes those associations stick.
SEO earns the click. GEO earns the mention. If you only build for one, you're invisible in half the places people now ask their questions.
Why this matters for the African market
I build for African markets from Cape Town, and the discovery gap here is real. When a buyer in Johannesburg or an investor in Texas asks an AI model 'who does GEO in South Africa?' or 'where can I get market intelligence on African economies?', the answer is assembled from whatever the model can find and trust. Most local companies have published nothing it can use. That's the opening — and it's exactly why PeakRank Digital exists.
The rest of this blog walks through the products where I've applied this playbook. Start with whichever problem is closest to yours.