Paste URLs, the agent scrapes and embeds all content, and creates a semantic search engine powered by FAISS and Gemini. Ask questions across all pages simultaneously — like having a researcher who has read everything.
Example queries
Build a searchable AI knowledge base from any set of web URLs.
Step 1
User pastes a list of URLs (documentation, product pages, knowledge articles) and names the collection.
Step 2
Agent crawls each URL, extracting clean text, headings, and structured content — handling JavaScript-rendered pages.
Step 3
Content is split into semantic chunks, embedded using Gemini embeddings, and stored in a FAISS vector database.
Step 4
User asks a question in plain English. FAISS retrieves the most semantically relevant chunks from all stored pages.
Step 5
Gemini reads the retrieved context and synthesises a precise, cited answer from across all indexed pages.
Turn any set of URLs into an intelligent, queryable search engine
No manual data entry — auto-scrape and index at submission
Semantic search surfaces relevant content even with paraphrased queries
Works for docs sites, product catalogues, intranets, and knowledge bases
web source
vector search
cross-page retrieval
query response
“We turned our entire product documentation into a searchable AI assistant in under an hour. Customers get precise answers without opening a single support ticket.”
There is no hard limit. You can index entire documentation sites, product catalogues, or enterprise intranets — any collection size is supported.
Talk to our team and see this solution running on your data within days.