How Leus Capital became the 3rd most-recommended UA-financing provider on AI search

Picture a mobile game studio in London or Istanbul. The game is working, the cohorts are healthy, and the only problem is that the cash needed to scale user acquisition is locked in Apple’s payout, two months away. What did a founder do about this in the old world? Google it, skim a few agency pages, call an investor. In 2026 they do something different: they open ChatGPT and ask. “What are the best UA financing partners for mobile games?”

The answer arrives in seconds and names four companies: Braavo. Capchase. Pipe. Clearco. Clean, persuasive, with sources linked. The founder books their first call from that list.

Leus Capital did exactly this job - non-dilutive UA financing advanced against app-store receivables. But it wasn’t in the answer. And that wasn’t a feeling; it was a measured fact: in May 2026, Leus appeared in barely 3% of the category’s AI answers - 3.0% in Türkiye, 2.3% in the UK. Among financing providers: 14th place.

The few seconds in which buying decisions are made were being filled, every day, almost entirely with competitors’ names.

On May 5, GeoGenie went live. Across two markets and 13 topics, 157 real, buying-intent questions - from “how do I fund UA without dilution” to “how do I bridge Apple’s payout delay” - were put to ChatGPT, Gemini, Perplexity, and Google AI Overviews every single day. Every answer was scanned: which brands are named, which sources are cited.

The picture that accumulated in the first weeks was brutal, but unusually precise. The problem was no longer a vague sense that “AI doesn’t like us” - it was a target list at the prompt level. Which question does each competitor win? Where does Leus not exist at all? Which niche is nobody strong in? This is the first pillar of GeoGenie’s methodology: find the “Jobs To Be Done” prompts - surface the real questions buyers ask AI, and map the territory with measurement instead of assumption.

By mid-June the curve stirred, and the clearest way to tell what happened next is through the program’s most successful example. On the flagship topic - “Mobile App User Acquisition Financing” (UK), answered by a guide written for exactly that question - visibility climbed from 2.8% in May to 9.0% in June, and in the first two weeks of July hit 32.6%. When a founder asks that question now, one answer in three says Leus.

This was the proof that the chain works: write the narrow answer to the right question, open the gates to bots - and the engines start citing you.

Ask that founder’s question today - “what are the best UA financing partners for mobile games?” - and one time in three, the answer now names Leus Capital, with leus.capital’s own guide shown as the source. The company that wasn’t in the answer ten weeks ago is now the answer’s source.

No single tactic did this. Three pillars worked in sequence and together: the jobs buyers actually ask AI about were found by measurement; each job got one narrow, focused page; and those pages were made consumable by bots. Every link of the chain is measured on its own: 32.6% visibility in answers, citation volume up 7.8x, bot traffic running at 200x the human AI traffic, and human visits starting from zero. And the position compounds - every new citation makes the next recommendation more likely, as more of the category’s discovery shifts to generative engines.