AI is already deciding who gets cited, recommended and chosen.

It depends on where the problem sits. If the goal is understanding how ChatGPT, Perplexity, and other AI engines talk about your brand, start with AI Visibility Intelligence. If you sell products online and want to know whether they show up in AI recommendations, start with AI Product Visibility. If the issue is internal AI adoption in company processes, start with AI Strategy & Integration. If you have a team and want to build something real on an actual case, Agentic Execution Lab is the right path. Write to me and we will work it out together.

SEO works on ranking in search results. AI visibility measures something different: whether generative engines cite your brand, recommend your products, or ignore you in synthesized answers. They are not competing channels: they are two layers of the same digital presence. To be citable you need clear entities, readable structure, verifiable data, schema markup, source pages, explicit comparisons, and consistency across the website, third-party sources, and external signals. Adding text is not enough: the answer must become easier for the model to construct.

Both. We start from a real company case and build a concrete, usable workflow or asset from day one. The team acquires the method while producing: not a course on tools, but real work with an output that stays in the company. No prior technical expertise is required: the technical side is handled together, the focus is on method and result.

The first 48-hour reading is free. After that, each project is structured around its scope: single sessions, 30-90 day roadmaps, and ongoing contracts for AI monitoring. I do not have fixed package prices because each case has different requirements. Tell me what you have in mind: within 48 hours you will know whether and how it makes sense to work together.

I take the real questions your customers would ask ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google AI Overviews. For each question I record whether you are cited, in what position, with what tone, from which sources, and who is cited instead of you. The output is a map: where you are strong, where you are invisible, and which signals to fix first. It is not an automated audit: each query is read and interpreted. For products the logic goes deeper: specific categories, pages, technical attributes, reviews, and buying guides where AI may recommend a competitor before the user even opens Google.

It is the service for companies that want to understand where and how to use AI in real processes before delegating everything to an internal team or external vendor. I identify the use cases with the best impact-to-effort ratio, design the target process, and start implementation on a first pilot case. It is aimed at leadership, operations, and functional teams. No prior technical expertise is required: we start from the operational problem, not the technology.

It depends on the phase and the case. OpenAI Codex for code generation and automation. Claude Code for agentic execution on real codebases, autonomously and verifiably. Google Antigravity for orchestrating parallel sub-agents on complex tasks. Claude Cowork for collaborative sessions with teams. I do not use a single tool for everything: choosing the right one for each phase is part of the method.

The first diagnosis arrives within 48 hours. Work on content, schema, sources, product pages, or workflows typically takes 30-90 days. For visibility: citation frequency, share of answer, cited competitors, sentiment, and domain citations. For workflows: time saved, adoption rate, and friction reduction. Nobody controls AI engine answers directly: the work increases the probability of being cited by making brands, products, and processes clearer and more consistent.