Knowledge Ingestion & Indexing

What Is This Feature?

For your AI assistant to give accurate, useful answers, it needs to know things — specifically, the things your business knows. The Knowledge Ingestion feature is how your content gets into the assistant: documents, help articles, PDFs, web pages, internal wikis. This deep dive explains how that process works, why it's built the way it is, and what it means for the quality of your assistant's answers.

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Why It Matters to Your Business

An AI assistant is only as good as the information it has access to. If your knowledge base is stale, incomplete, or poorly indexed, your assistant will give outdated answers, miss key details, or confidently say things that aren't true.

• Accuracy depends on fresh content. When your documentation changes, the assistant needs to reflect that — quickly and reliably.
• Partial updates save time and money. Reprocessing your entire knowledge base from scratch every time you make a small change is slow and expensive. The system is designed to update only what changed.
• Visibility into what's happening. If a document fails to ingest properly, you need to know about it — not discover it weeks later when a customer gets a wrong answer.
• Consistent quality over time. As the underlying AI technology improves (better indexing methods, better embeddings), your content needs to be reprocessed to take advantage of those improvements. The system tracks which content was processed with which version, making targeted updates possible.

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How It Works (No Technical Jargon)

Think of ingestion as a production line with several stations. Each document moves through each station in order:

1. Fetch — The system retrieves the content from wherever it lives: a URL, a file upload, an API connection to your existing docs platform.
2. Extract — Raw content is cleaned up and converted to a consistent format. Tables, images, and metadata are handled appropriately.
3. Chunk — Long documents are broken into smaller pieces that the AI can reason about effectively. The chunking strategy is tracked so the system knows when it needs to be re-done.
4. Embed — Each chunk is converted into a mathematical representation that allows the AI to find relevant content quickly, even when the exact words don't match the user's question.
5. Index — The embedded chunks are stored in a searchable database so the assistant can retrieve them in real time during a conversation.

If a piece of content fails at any station, the system logs exactly where and why — and you can re-run just that station without starting over.

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What You Get as an Operator

• A dashboard showing the status of every content source: how many documents were ingested, when they were last updated, and whether any failed
• Alerts when ingestion failures exceed a threshold
• The ability to re-process specific documents or specific stages (e.g., re-embed without re-fetching)
• Version tracking so you know exactly which content was processed with which version of the indexing logic

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Handling Sensitive Content

Not all content should be equally accessible. Documents tagged as sensitive can be:

• Excluded from certain assistant configurations (e.g., only available to authenticated internal users)
• Stripped of specific fields before indexing
• Kept in a restricted index that requires elevated permissions to query

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What to Expect on the Roadmap

The team is building toward:

1. Full version tracking for all ingested content, with a partial reprocess API (estimated 3 weeks)
2. Migration to a dedicated vector database for faster, more scalable retrieval (estimated 2 months)

These improvements will make the ingestion pipeline more transparent, more efficient, and easier to maintain as your knowledge base grows.