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Use cases by sector

LOOP foundations are universal, priorities vary by profile. Here's how LLM optimisation plays out in four common contexts.

Mise à jour : 14 April 2026 • 10 min de lecture

B2B SaaS

Selling through top-of-funnel search

B2B decision-makers heavily use ChatGPT and Perplexity to frame a subject before contacting a vendor. The comparisons and reviews cited by these engines directly shape the short list.

Priorities

  • Documented comparison content ("X vs Y", feature tables, dated customer feedback).
  • Product pages structured with schema SoftwareApplication and FAQPage.
  • Named, quantified, dated customer case studies.
  • "Alternatives to [competitor product]" pages written with rigour (no bashing, objective data).
  • Controlled presence on credible third-party sources (G2, Capterra, Product Hunt, industry press).

Anti-patterns

  • Heavy landing pages rendered client-side, invisible without JS.
  • Vague or hidden pricing: LLMs favour sharp claims.
  • Generic FAQs copy-pasted from marketing docs.

E-commerce

Capturing product and advisory queries

"Best X for Y", "alternative to Z", "buying guide..." queries increasingly go through AI engines. Product pages and guides are the dual lever.

Priorities

  • Complete Product schema (name, description, image, offers, AggregateRating, Review).
  • Neutral, informative buying guides with inter-brand comparisons.
  • Product FAQ (dimensions, compatibility, after-sales) — extractable by LLMs.
  • Category pages with rich editorial descriptions, not just filters.
  • Verified customer reviews surfaced in static HTML.

Anti-patterns

  • Dynamic pricing exposed to AI bots inconsistently.
  • Empty category pages showing only a product list.
  • Self-promotional buying guides (LLMs favour perceived neutrality).

Media and publishers

Preserving citation while protecting value

Media outlets face an arbitration: allow AI bots to crawl for continued citations (visibility, marginal traffic) or block to preserve the business model (licensing, subscriptions). Both postures are coherent.

Priorities

  • Classic article structure: headline, dek, date, author, short sections — citation-friendly by design.
  • Article schema (NewsArticle, BlogPosting) with Person author and Organization publisher.
  • Clear AI bot policy, documented on the site.
  • Commercial licenses with OpenAI, Google, Perplexity where relevant.
  • Persistence strategy: keep evergreen pages updated — they become recurring sources.

Anti-patterns

  • Infinite scroll or lazy loading that hides the article from non-JS bots.
  • Paywall without HTML alternative for licensed bots.
  • Silent WAF blocking of AI bots without explicit editorial stance.

Professional services

Being findable when you can’t publish everything

Lawyers, accountants, consultants, agencies: the work rests on expertise, trust and references. AI engines become a pre-qualification channel before contact.

Priorities

  • Expert profiles (Person schema) with titles, credentials, publications, sameAs to LinkedIn and bar/professional registers.
  • In-depth articles on recurring client questions (contract templates, complexity zones, legal frameworks).
  • Long, precise industry FAQs with legal or professional nuance.
  • Anonymised use cases, quantified and dated.
  • Consistent presence on recognised professional directories.

Anti-patterns

  • Generic "our services" pages with no substantive content.
  • Total absence of Person or Organization schema.
  • Vague or missing article bylines.

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