The term “be to see” has been circulating for a few months in French-speaking marketing circles, often without a stabilized definition. The central idea can be summed up in one sentence: a brand must exist in the informational environment of its target audience even before a purchasing need arises. This logic goes beyond traditional SEO and questions how companies build their presence, especially in the face of generative search engines.
Visibility in AI responses: the territory that classic SEO no longer covers
Traditional SEO measures positions, impressions, and clicks. These indicators remain useful, but they only capture part of the reality. Buyers, particularly in B2B, now query ChatGPT, Gemini, Copilot, or Perplexity to compare suppliers or understand a market.
The question is no longer just “does my site appear on the first page?”, but rather: is my brand mentioned, cited, or recommended in the responses generated by AI? This distinction gives rise to what is called GEO (Generative Engine Optimization), an extension of SEO that focuses on how language models synthesize information.
Understanding the strategies of be to see requires integrating this new layer of visibility, where a company’s content is evaluated not only by a ranking algorithm but by a system that reformulates, attributes, and sometimes recommends.
External reputation and trust signals: what LLMs really synthesize
Language models do not just index a site. They aggregate third-party sources: review platforms, comparisons published by specialized media, professional communities, documented client cases. A “About” page self-proclaiming a company as “leader” or “innovative” carries little weight in this context.
A documented client case is worth more than a generic promise in the realm of LLMs. AI systems favor precise usage descriptions, recommendations attributable to identifiable third parties, and verifiable technical evidence.
For a company looking to apply the be to see, this implies preparatory work on several fronts:
- Obtaining detailed reviews on platforms consulted by buyers in its sector, not just Google My Business
- Publishing client cases with concrete data (context, problem, measurable result) rather than vague testimonials
- Being cited in third-party content (specialized press articles, comparative studies, professional forums) that serve as sources for generative engines
Field feedback varies on the exact weight of each signal, but the logic remains consistent: visibility is built through verifiable reputation, not self-promotion.
Be to see in B2B: human validation remains the final filter
The enthusiasm around AI in the purchasing journey should not obscure a point of friction. B2B buyers use LLMs for initial exploration, to narrow down a long list of potential suppliers, or to understand the differences between two technical approaches. The final decision, however, still goes through human validation.
This means that be to see is not just about appearing in a generated response. It must ensure that technical and commercial evidence is easily verifiable by a decision-maker who will then consult the site, contact a sales representative, or request a demonstration.
Content optimized for LLMs but disconnected from real-world experience produces the opposite effect: the brand is visible, but the prospect disengages at the first contact. The marketing lever works when the consistency between the message synthesized by AI and the actual experience remains intact.
Combining trust signals: a still minority practice
The most successful B2B campaigns combine several types of signals: quality editorial content, presence on professional social networks, verified customer reviews, mentions in third-party media, and content strategy aligned with generative engine queries. The available data does not allow for a conclusion of an automatic causal link between this combination and business results, but the association is regularly observed.
The difficulty lies in the fact that most companies treat these levers in silos. SEO is assigned to one team, customer reviews to another, content production to an external provider. Be to see, on the other hand, requires tight coordination, as LLMs do not distinguish between channels: they synthesize everything.
- The content published on the blog must use the exact terms that prospects use in their conversational queries
- Reviews and client cases must be accessible and structured to facilitate their extraction by AI systems
- The presence on professional social networks must reflect the same messages as the site, without positioning discrepancies
- Digital marketing actions (SEA, inbound, gated content) must feed the same evidence base that is exploited by generative engines
GEO does not replace SEO; it overlays it and modifies the hierarchy of priorities. Companies that integrate it early into their marketing strategy have a structural advantage, provided their external reputation withstands scrutiny.
Be to see remains a lever in the process of maturation. Metrics for measuring visibility in AI responses are not yet standardized, and best practices evolve with the pace of model updates. What seems certain is that marketing visibility is now played out on two simultaneous fronts: traditional search results and generative responses. Ignoring one of the two amounts to leaving a blind spot in one’s acquisition strategy.
