Work you can
actually
verify.
Two clients, two completely different categories, the same principle: become visible where buying decisions start today — inside AI answers. Here is what we actually did.
SERVICE: GEO + AEO + TECHNICAL SEO
MARKET: DACH
STATUS: ONGOING RETAINER
Looly: putting a young fashion brand inside the AI answer.
Starting point. Looly Women is a direct-to-consumer womenswear brand. Like almost every young e-commerce brand, it simply did not exist for AI engines: no entity signals, no schema markup, product pages without extractable answers. When shoppers asked ChatGPT or Perplexity for recommendations in the category, only large marketplaces and established brands were named.
What we did. A full AI visibility audit as the baseline. Entity building: Organization and Product schema with stable @ids, consistent brand phrasing across every channel, sameAs links connecting all profiles. Category pages received TL;DR answer blocks that answer the audience's real buying questions ("What do I wear with…", "Which fit for…"). Plus robots.txt opened for AI crawlers, an llms.txt, and a monthly prompt panel across ChatGPT, Perplexity, Gemini and Google AI Overviews.
Where it stands. The brand is anchored as an entity in the relevant knowledge graphs and is now named in the panel's first category prompts. Branded search and direct traffic are trending measurably upward; the panel keeps running monthly and drives the content roadmap. The most important effect: Looly now competes inside AI answers in a league classical SEO alone would never have carried a young brand into.
NIGHTSAVER
SERVICE: GEO + AEO + AGENTIC SEARCH
MARKET: DACH
STATUS: ONGOING RETAINER
Nightsaver: becoming the answer to "what's on tonight?"
Starting point. Nightsaver aggregates nightlife offers and deals — a category made for AI search: "best bars in {city}", "student party tonight", "clubs with free entry today". Exactly these questions are migrating from Google to ChatGPT and Perplexity. Nightsaver appeared in none of those answers; magazine listicles and outdated blog posts were cited instead.
What we did. Question-first architecture for local intents: dedicated, extractable answer blocks per city and occasion instead of generic landing pages. Event and Offer schema so dates, venues and deals are machine-readable — the precondition for being treated as a bookable source by AI agents (Agentic Search). Entity building through local directories and consistent company data. A weekly prompt panel for the most important city queries, because local AI answers rotate fast.
Where it stands. Nightsaver is increasingly named in the tracked local prompts as a source for current nightlife offers — with a direct link. The structured event data makes it one of the few platforms in the category an AI agent can actually query rather than merely mention. The panel runs weekly; every new city is built out with the same playbook.
No fantasy percentages. Verifiable work.
We don't publish invented uplift numbers. Every statement above can be evidenced in a client conversation — with prompt panels, screenshots and monthly reports. Reference calls with both clients are available on request.
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