Turn Your Website Into an AI Knowledge Base
Crawl your entire website and convert it into a searchable AI knowledge base with automatic vector embeddings and incremental refresh.
課題
Your website contains the most up-to-date information about your products, pricing, documentation, and policies — but your AI workflows can't access it. Teams manually copy-paste web content into documents, which go stale immediately. Support agents answer questions from outdated knowledge bases. Sales reps reference pricing pages that changed last week.
ソリューション
JieGou's website crawl pipeline automatically discovers, crawls, and indexes your entire website into a searchable knowledge base. Point it at your sitemap, configure crawl rules, and the pipeline handles the rest — extracting content, chunking for optimal retrieval, generating vector embeddings, and storing everything in Firestore with sub-second search. Incremental refresh keeps your knowledge base current without re-crawling unchanged pages.
ワークフローステップ
Sitemap Discovery
レシピステップFetches your sitemap.xml and discovers all indexable pages. Supports sitemap index files, nested sitemaps, and URL-based discovery fallback.
Smart Filtering
条件Applies exclusion patterns (e.g., /admin/*, /staging/*), URL canonicalization, and depth limits. Pre-crawl estimation shows page count and estimated processing time.
Crawl & Extract
並列処理Crawls pages in parallel with configurable concurrency. Opt-in headless Chromium for JavaScript-rendered SPAs. Extracts clean text content, stripping navigation, footers, and boilerplate.
Chunk & Embed
レシピステップSplits content into optimal chunks using heading-based splitting with paragraph fallback. Generates vector embeddings via OpenAI text-embedding-3-small and stores in Firestore.
Incremental Refresh
ループScheduled re-crawl checks for changed pages using content hashes. Only re-processes pages that have actually changed — saving compute and embedding costs.
Vector Search Ready
レシピステップKnowledge base is immediately available for all recipes and workflows. Firestore-native vector search with Redis caching delivers sub-second retrieval.
期待される成果
- Your entire website becomes a searchable AI knowledge base in minutes
- Support workflows reference the latest product docs automatically
- Incremental refresh keeps knowledge current without manual intervention
- Sub-second vector search retrieves relevant content for every AI interaction
- No external vector database required — Firestore handles everything
ラーニングループの実例
Website is fully indexed. Recipes and workflows start retrieving web content via RAG. Retrieval relevance is good for well-structured pages.
Incremental refresh has run multiple cycles — knowledge base tracks website changes automatically. Teams stop manually updating FAQ documents.
Knowledge base covers 100% of website content. Redis caching delivers sub-second retrieval for repeat queries. Support accuracy improves measurably from always-current web content.
その他のユースケース
リード評価の自動化
新規リードの調査、スコアリング、アウトリーチメールの作成を手作業なしで自動化します。
Marketingブログ・オムニチャネルコンテンツワークフロー
ブログ記事を1本書くと、ソーシャル、メール、ニュースレターのコンテンツが自動生成されます。
Supportサポートチケット解決ワークフロー
1つのフローでチケットの分類、返信草稿の作成、ナレッジベース記事の作成を行います。
HR採用ワークフローの自動化
求人票の自動生成、候補者の一括スクリーニング、面接資料の準備を行います。
Finance請求書処理の自動化
請求書データの自動抽出、差異チェック、承認ルーティングを行います。
Engineeringエンジニアリング・インシデント対応ワークフロー
インシデントの詳細からインシデントレポートの生成、ランブックの更新、ポストモーテムの作成を行います。