How CatzAI Works with Website, PDF and Document Knowledge

Website knowledge needs more than page scraping

A webpage contains far more than its main article or service information. Navigation, cookie notices, accessibility controls, footer links and repeated interface elements can overwhelm the useful content if everything is treated equally.

A knowledge pipeline should therefore prioritise the primary page content while retaining useful structure such as titles, headings and relevant links.

Documents require their own ingestion path

PDFs, word-processing files, presentations and spreadsheets have different structures from HTML pages. Some contain selectable text, while others depend on OCR because the page is effectively an image.

For that reason, CatzAI separates website discovery from file ingestion. A website may reveal that a document exists, but a discovered document does not have to become knowledge until it is explicitly approved or uploaded.

• Normal text extraction when document text is available.
• OCR paths for scanned or image-based material.
• Preservation of useful document context and source identity.
• Explicit control over which discovered files are approved.

Good retrieval begins with good source quality

The AI can only retrieve what the knowledge pipeline has captured. Document quality, extraction accuracy, chunk boundaries and source metadata therefore directly affect the quality of the final answer.

This is why CatzAI treats knowledge ingestion as a core product capability rather than a one-time upload step.