Industrial SEO at 100 Pages/Week: My n8n + Claude Code + RAG Stack
Stéphane Jambu discusses his approach to industrial SEO, achieving high output through a structured content generation pipeline. His method involves a three-layer system that ensures coherence across content clusters while utilizing AI tools. This strategy allows his agency to produce 50 to 100 pages per project per week without sacrificing quality.
- ▪Stéphane Jambu runs a French SEO agency based in Siem Reap, Cambodia.
- ▪The agency has produced over 1,300 semantic content clusters for more than 650 brands.
- ▪Jambu's three-layer pipeline includes a RAG knowledge base, n8n orchestration, and a QA loop with Claude Code.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/stephanejambu/industrial-seo-at-100-pagesweek-my-n8n-claude-code-rag-stack-2k58 |
| Publication time | Wed, 27 May 2026 04:51:53 +0000 |
| Retrieval time | 2026-05-27T05:07:56.817Z |
| Last seen | 2026-05-27T05:07:56.817Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | _RLYLxB2FAk1 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3953538) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Stéphane Jambu Posted on May 27 Industrial SEO at 100 Pages/Week: My n8n + Claude Code + RAG Stack #seo #ai #automation #rag I run a French SEO agency from Siem Reap, Cambodia. We've shipped 1,300+ semantic content clusters for 650+ brands — typically at 50 to 100 pages per project per week. That cadence is impossible with a traditional content team. It's also impossible with raw LLM generation: the output looks fine in isolation and rots when you read three pages in a row.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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