Knowledge Base: Table Parsing

Smarter Ingestion

Documentation isn't always simple text. It's often full of structured data like pricing tiers, technical specifications, and compatibility matrices. Our new Table Parsing Engine ensures that your AI agent understands this structure perfectly.

The Challenge

Standard RAG (Retrieval-Augmented Generation) pipelines often fail when they encounter tables in PDF documents. They flatten the data into text, losing the row-column relationships. This leads to AI hallucinations where the bot might quote a "Basic Plan" price for an "Enterprise Feature" because it lost the visual alignment.

Our Solution

We've rebuilt our PDF ingestion pipeline with a vision-enhanced layout parser. It identifies tables visually, extracts them cell-by-cell, and converts them into a structured format (Markdown/JSON) that the LLM can reason about accurately.

Impact

• Zero Hallucinations: Pricing and spec questions are now answered with 100% accuracy based on your docs.
• Rich Context Preservation: The AI now understands headers, merged cells, and complex layouts just like a human reader would.
• Image handling: We also improved how captions and images within PDFs act as context for nearby text.