Proof

Use cases by sector

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

Updated 14 April 2026 10 min read

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.

Related reading

LOOP methodology The frame that structures priorities. Audit and checklist The grid to set your baseline. Insights In-depth analyses and field notes.