How La Redoute Gained Control Over LLM Exploration

Introduction

La Redoute, a leader in online retail, combines a historic French brand legacy with a strong digital footprint across fashion and home goods. Operating in highly competitive markets, the company has consistently invested in innovation to stay ahead, balancing performance marketing with long-term brand building.

In 2024, a new challenge emerged: understanding how large language models (LLMs) like Gemini and ChatGPT crawl, consume, and surface site content. 

We used Primelis Outrank, deploying the platform’s capabilities to include AI bot log analysis. The goal: help La Redoute control how LLMs interact with their site and use that intelligence to future-proof their organic strategy.

Challenge

AI models are reshaping the search landscape. Unlike traditional bots, LLMs crawl the web differently, use distinct user agents, and operate with their own indexing and ranking logic.

During the log analysis, we uncovered several critical issues:

  • Some LLM crawlers like Google-Other, used by Gemini, were being unintentionally blocked
  • High-value content was not being accessed by AI bots
  • A significant portion of AI crawl volume was focused on low-priority pages

The risk: losing visibility in generative environments that are becoming core entry points for users. The opportunity: steer AI exploration toward high-impact content to maximize future exposure.

Strategy

The approach was built around a single objective: gain control over how AI crawlers explore and evaluate site content and use that control to improve visibility in AI-powered search environments.

 

Detect and analyze AI bot activity: Using Primelis Outrank, we identified key AI user agents like Google-Other, ChatGPT, Claude and analyzed their behavior including crawl frequency, page types visited, and hit distribution across site areas.

 

Resolve technical blockers: We detected several crawl directives that were unintentionally restricting AI bots. Updates to robots.txt and server headers were deployed to allow clean, targeted access to strategic pages.

 

Redirect AI attention to high-value content: By cross-referencing business priorities and crawl behavior, we identified underexplored content with high strategic value. These became focus areas for improved internal linking and structural visibility, transforming frequently hit pages into entry points for broader product discovery.

Results

  • Crawl volume from AI bots increased 20×
  • Blocked page rate reduced through refined technical directives
  • Higher share of 200-status (valid) pages explored by AI crawlers
  • Content prioritization now includes AI-specific exploration signals

This gave La Redoute new visibility into how LLMs process its site and new ways to influence how future search experiences present its content.

Impact

By integrating LLM exploration into its organic growth strategy, La Redoute gained a critical advantage in the shift toward generative search:

  • Key content is now accessible and properly explored by AI crawlers
  • Exploration is aligned with product priorities and business goals
  • Insights by bot type help tailor both technical and content strategy
  • The framework is now being deployed across the full site

Primelis Outrank proved to be more than a monitoring platform. It became a strategic control panel. In a search ecosystem increasingly shaped by AI, La Redoute now leads with visibility, precision, and readiness for what’s next.