Wojas is a Polish footwear and leather goods company with a long-running retail and e-commerce presence. Its content has to compete in traditional search results and in generative answers that cite sources from across the web.
The company needed a clearer view of both. Standard SEO reporting showed only part of the picture. Generative engine optimisation, or GEO, added another set of questions: Where was Wojas cited? Which competitors appeared instead? What topics and page formats created the strongest next opportunities?
We built a production platform that brought those signals together. It monitored SEO and GEO visibility, tracked citations, compared competitors, generated reports, and turned the findings into content recommendations and strategy.
The business challenge
Search visibility no longer lives in one list of keyword positions. A brand can rank in classic search, appear in an AI-generated answer, receive a citation, or be absent while a competitor is recommended.
Each view answers a different business question:
- SEO shows how pages perform in traditional search results.
- GEO shows how the brand and its content appear in generative answers.
- Citation monitoring shows which sources an answer uses.
- Competitor analysis shows who receives visibility when the brand does not.
Reviewing these signals separately makes it difficult to decide what content to improve or create next. Wojas needed one platform that could connect monitoring to action.
One view of SEO and GEO visibility
The platform collected the available search and citation signals into one reporting flow. Teams could see Wojas visibility, review where competitors appeared, and inspect the topics behind each result. Google Search Console provides the classic search performance view, while the platform added the generative and competitive views needed for a wider decision.
This mattered because SEO and GEO are related rather than competing disciplines. Google’s guidance for AI features says that established SEO practices still apply to AI Overviews and AI Mode. A page must first be indexed and eligible for a search snippet before it can appear as a supporting link in those experiences.
The platform therefore kept classic search performance beside generative visibility. It did not treat GEO as a separate layer of tricks. The same move toward citation-level reporting now appears in Bing Webmaster Tools, whose AI Performance dashboard reports which URLs are cited and how citation activity changes over time.
An AI agent that turns signals into recommendations
Monitoring explains what happened. The AI agent helped the team decide what to do next.
It analysed the collected reports, citations, competitor presence, and content gaps. It then produced content recommendations and strategy suggestions based on those signals. A recommendation could identify a topic that competitors covered more clearly, a ranking-style page worth considering, or an existing page whose structure did not answer the search intent well enough. The recommendation still had to lead to helpful, people-first content, not a page written only to manipulate a search system.
The agent supported analysis and planning. It did not replace editorial judgment. The team kept a human in the loop to review each recommendation against brand priorities, product knowledge, and the evidence behind it before deciding what to publish.
This is a practical form of AI adoption. The agent owns a defined analytical task inside a wider content process, while people retain the decision about the brand’s public voice.
Reports that lead to a next action
The platform organised its output around decisions rather than a growing collection of charts. Reports connected four questions:
- Where does Wojas appear today?
- Which competitors appear for the same topics?
- Which citations and pages influence that visibility?
- What content or strategy action should the team consider next?
That structure made each report useful beyond measurement. It turned reporting into workflow automation and let the team move from a visibility gap to a reviewable recommendation without starting a separate analysis from scratch.
The recommendations also stayed grounded in the content and competitive evidence available to the platform. That boundary is important for any AI agent that advises a business. A confident suggestion without a visible reason is difficult to review and easy to misuse.
What changed for the business
Wojas gained a production system for a problem that had previously been split across SEO, GEO, citations, competitors, and content planning.
The platform gave the team:
- GEO visibility that increased from 2% to 23% within four months;
- one place to monitor SEO and GEO visibility;
- citation and competitor analysis connected to the relevant topics;
- reports that explained the current position;
- content recommendations and strategy suggestions tied to observed gaps.
We do not present these outputs as guaranteed ranking gains. Search systems change, and a recommendation still has to become useful content. The platform’s value is that it gives the team a repeatable way to observe the market, decide what deserves attention, and review the evidence behind each next step.
The result
The completed platform moved Wojas from separate visibility signals to one operating view. Within four months, GEO visibility increased from 2% to 23%. SEO, GEO, citations, competitor analysis, reporting, and content recommendations now support the same decision process.
The central lesson is to connect measurement to action. A visibility dashboard can show where a brand stands. A useful production system also explains what changed, why it matters, and which content decision the team should review next.