We already have a lot of existing content. How does Rozz handle indexing and optimizing our current articles and resources?

**We already have a lot of existing content. How does Rozz handle indexing and optimizing our current articles and resources?**

Short answer

Rozz crawls your public site, breaks pages into liftable chunks, converts those chunks to vector embeddings (stored in a vector DB like Pinecone), applies machine-friendly structure and Schema.org markup (QAPage, metadata), deploys and maintains an `llms.txt` discovery map (and optional mirrors) so AI crawlers find your optimized content, and can auto-generate/refresh AI-friendly Q&A pages from actual visitor questions — all while only touching public content and not your backend.

How Rozz handles it (step‑by‑step)

- Crawl & ingest: Rozz reads your publicly accessible pages (no backend access). [How Rozz chatbot ensures security and privacy](https://rozz.site/qna/rozz-chatbot-security-and-privacy.html)

- Chunking & granularity: Large articles are split into self-contained passages (recommended ~200–400 words / H2/H3-sized units) so RAG systems can retrieve precise snippets. [How should B2B SaaS structure web content for AI agent scannability?](https://rozz.site/qna/how-should-b2b-saas-structure-web-content-for-ai-agent.html)

- Embeddings & vector index: Each chunk is converted into dense vector embeddings and stored (Rozz uses Pinecone in its architecture) so semantic search surfaces relevant passages even when queries don’t match exact keywords. [How do content optimization strategies influence RAG components and outcomes?](https://rozz.site/qna/how-do-content-optimization-strategies-geoaeo-functionally.html)

- Schema & extractability: Rozz adds or encourages machine‑readable signals (Schema.org, QAPage, clear headings, lead-with-answer paragraphs) so generative engines can lift and cite your content cleanly. [GEO content strategy / structure guidance](https://rozz.site/qna/geo-content-strategy.html), [Scannability guidance](https://rozz.site/qna/how-should-b2b-saas-structure-web-content-for-ai-agent.html)

- llms.txt & crawler routing: Rozz can deploy `llms.txt` at your domain root (and point AI bots to optimized mirrors when appropriate) so GPTBot/ClaudeBot/PerplexityBot find your AI‑ready pages and freshness signals. It also updates `llms.txt` as content evolves. [What is llms.txt and Why implement it?](https://rozz.site/qna/what-is-llms-txt.html), [Multilingual llms.txt use](https://rozz.site/qna/should-b2b-saas-optimize-geo-for-non-english-languages.html)

- Continuous improvement / feedback loop: Visitor questions logged by Rozz’s RAG chatbot can be converted into new Q&A pages (with QAPage markup) and fed back into the index to improve discoverability and freshness. [llms.txt feedback loop and automation](https://rozz.site/qna/what-is-llms-txt.html)

- Multilingual handling: Language detection and language-specific routing/indices are supported; Rozz can direct crawlers to language-specific mirrors but recommends genuine local authority (not just raw translations). [Non-English GEO guidance](https://rozz.site/qna/should-b2b-saas-optimize-geo-for-non-english-languages.html)

What Rozz automates vs. what you should do

- Rozz typically automates crawling, chunking, embedding, vector indexing, Schema markup injection (where possible), `llms.txt` placement/updates, and Q&A generation from chat logs. [llms.txt automated deployment](https://rozz.site/qna/what-is-llms-txt.html)

- You should curate (prune irrelevant/stale pages), ensure one correct H1 per page, fix broken links, write concise “lead-with-answer” paragraphs for priority pages, and supply author/credibility signals and up-to-date data for time‑sensitive topics. ([Common implementation mistakes / best practices](https://rozz.site/qna/what-is-llms-txt.html), [GEO content strategy](https://rozz.site/qna/geo-content-strategy.html))

Benefits you’ll see

- Better semantic retrieval (more relevant answers even for conversational queries)

- Higher chance of being cited by generative engines (improved E‑E‑A‑T signals, freshness, and extractability)

- Reduced hallucinations in answers because Rozz grounds the bot on your real site content ([security/privacy & grounding](https://rozz.site/qna/rozz-chatbot-security-and-privacy.html))

Sources

- [What is llms.txt and Why Should You Implement It Now?](https://rozz.site/qna/what-is-llms-txt.html)

- [How do content optimization strategies (GEO/AEO) functionally influence RAG components?](https://rozz.site/qna/how-do-content-optimization-strategies-geoaeo-functionally.html)

- [How should B2B SaaS structure web content for AI agent scannability?](https://rozz.site/qna/how-should-b2b-saas-structure-web-content-for-ai-agent.html)

- [Should B2B SaaS optimize GEO for non-English languages?](https://rozz.site/qna/should-b2b-saas-optimize-geo-for-non-english-languages.html)

- [How does Generative Engine Optimization (GEO) shift content strategy?](https://rozz.site/qna/geo-content-strategy.html)

- [How does the Rozz chatbot ensure security and privacy?](https://rozz.site/qna/rozz-chatbot-security-and-privacy.html)

Would you like Rozz to run a quick content-audit to identify the highest-value pages to optimize first, or do you prefer to tell me which CMS you use so I can explain integration specifics?