About
InTheMind AI is a GEO and AI visibility company helping B2B companies appear in AI-generated buying decisions across ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews.
Our mission is simple: help credible companies become easier for answer engines to discover, understand and represent accurately. We focus on the evidence available to an AI system—crawlable pages, consistent entities, useful explanations and trustworthy sources—rather than promising control over a model’s response.
We audit how AI systems understand a company, identify why competitors appear instead, and turn those gaps into a technical and content roadmap for better AI visibility. Work begins with a repeatable prompt baseline and a review of the site as both a buyer and crawler experience. We then prioritize changes to crawlability, structured data, entity consistency, internal links and decision-stage content.
We work with B2B companies, SaaS teams, tech companies and service businesses whose buyers use AI tools to research vendors, compare options and build shortlists. Founder-led and niche companies often benefit because their expertise is real but not yet expressed in a way that machines can verify. The methodology is also useful to established teams that need AI visibility measurement alongside SEO.
CEO, Co-founder, GEO/SEO Specialist
Ilia leads strategy, buyer-prompt research and client roadmaps. His work connects commercial positioning with the technical signals and content evidence that influence how a company is summarized in AI-assisted research.
LinkedInCo-founder, GEO/SEO Specialist, Marketing Head
Artiom leads technical GEO implementation, structured content systems and visibility measurement. He translates audit findings into maintainable schema, crawlability and publishing workflows for engineering and marketing teams.
LinkedInMethodology
AI answers vary, so we do not treat one screenshot as a result. We record prompt wording, citations, competitors and inaccuracies, then connect those observations to public evidence that can realistically be improved.
Recommendations are tied to pages, templates and owners. A finding should become a concrete action: correct a canonical, define a service, connect a founder entity, publish a fair comparison or repair a crawler path.
We do not manufacture testimonials, ratings, awards or schema claims. Entity clarity works best when visible content and structured data describe the same defensible facts. Limitations and buyer fit belong in the content too.
Clients receive a baseline of current AI visibility, a map of technical and content gaps, implementation priorities and a measurement framework. Explore the GEO Audit for B2B companies, our AI citation tracking approach, or the schema markup system for AI search.
The practical sequence usually starts with discovery: can crawlers reach the right canonical pages and read the important content? Next comes interpretation: do the company, founders, services and audience form one consistent entity graph? We then examine decision support, including category definitions, use cases, alternatives and implementation detail. Finally, a stable prompt set measures whether mentions, citations and descriptions become more consistent. This order keeps teams from publishing more pages before the underlying evidence system is ready.
We share limitations plainly. Model outputs change, third-party sources affect recommendations, and recrawling takes time. The work improves the conditions for accurate representation; it does not buy control over ChatGPT, Perplexity or any other answer engine. That boundary is part of the methodology, not fine print.
Contact
We will review how AI systems see your company and show which entity, schema, crawlability or content gaps should be fixed first.
