Generative engines — ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews — have become a first-contact channel for the investors, journalists, graduates and prospects who research a company. Since Aggarwal et al. introduced the concept of Generative Engine Optimization (GEO) in 2024, the academic literature has begun documenting what practitioners sensed: visibility in LLM answers is now a reputation asset in its own right.
Audit methodology
We selected 12 Moroccan companies spanning banking, telecoms, mining, agri-food and retail. For each, 20 prompts were drafted across four intents: factual ('what is [company]'), evaluative ('is [company] reliable'), comparative ('[company] vs [competitor]') and recommendation ('best bank in Morocco'). Each prompt was submitted in fresh sessions on ChatGPT (GPT-5), Perplexity (Pro), Gemini (2.5 Pro) and Claude (Sonnet 4.5) in April 2026.
Perplexity logically dominates: its architecture explicitly displays sources. The most concerning gap is between Gemini (52%) and Claude (41%): two engines that respectively shape mass-market and enterprise audiences significantly under-cite Moroccan brands. For Claude, the deficit reflects weaker web retrieval; for Gemini, a Moroccan training surface thinner than for other markets.
Three quietly winning brands
Beyond the averages, three Moroccan companies stand out with an LLM visibility score 25 points above the sector mean. Their common trait is not the size of the communications budget but the density and structure of their documentary footprint: up-to-date Wikipedia pages, investor sheets rich in quantified data, indexable international coverage, presence on sector directories (WEF, central banks, rating agencies). That density is exactly what LLMs use as a citation surface.
The information-friction problem
An under-cited brand is not necessarily badly perceived — it is absent. That is a crucial nuance. When ChatGPT answers 'what is the best Moroccan bank for an expat', the absence of a citation is not a negative opinion; it is documentary ignorance. The fix is not communications in the classic sense (campaign, PR) — it is structural: produce and publish the raw material LLMs use to answer.
The long-tail effect
LLM answers are not single-entry rankings. A brand cited 80% of the time on the factual prompt can drop to 30% on the comparative prompt if a competitor has a better-structured Wikipedia sheet. The geometry of visibility matters as much as the average. Our audit shows a company can gain 12 points of citation rate by reworking only its English Wikipedia entry — a minimal investment compared to a press campaign.
The freshness challenge
LLMs have variable time horizons. ChatGPT and Gemini now integrate real-time search results; Claude and Mistral remain more dependent on their training surface. Consequence: a negative reputation event (sanction, crisis) stays cited longer by Claude than by ChatGPT. The counter-narrative strategy must adapt to the engine: dense and reactive for ChatGPT, structured and persistent for Claude.
The cost of inaction
According to aggregated GEO market data (Omnibound, May 2026), brands that do not invest in their LLM visibility lose an average of 8 citation points per quarter to better-indexed competitors. The phenomenon is cumulative: a brand under-cited today will be more so tomorrow, because LLMs partly learn from their own earlier answers. Inaction has a non-linear cost.
“My CEO typed our company's name into ChatGPT and saw a wide-of-the-mark answer. He summoned me the next day. The topic did not exist in our priorities the week before.”— Digital Director, Moroccan CAC Mid company
What to do before next quarter
- Audit the company's Wikipedia entry (FR + EN + AR) — it is the number-one source cited by LLMs.
- Check presence in 3 sector directories: institutional, rating agencies, international bodies (WEF, IFC).
- Measure citation rate and sentiment quarterly across 4 engines (ChatGPT, Claude, Gemini, Perplexity) with a standardized prompt battery.
- Invest in quantified, dated, verifiable content — exactly the format LLMs prefer to cite.
- Adapt the counter-narrative strategy per engine: ChatGPT/Gemini (reactivity), Claude/Mistral (structural persistence).
Harch Atelier's AI Visibility module covers 8 generative engines with a battery of 240 standardized prompts. The quarterly report delivers citation rate, sentiment and a prioritized action plan per engine.