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AI Foundations

Knowledge Base (RAG) Database

Your documents and answers become a searchable memory that chat, support and agents can all use.

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TRIGGER Content changes new or updated sources DATA STEP Collect sources docs, FAQs, tickets DATA STEP Chunk & embed split and vectorise DATA STEP Vector index fast semantic search AI STEP Retrieve & answer with cited sources HUMAN CHECKPOINT Curate gaps approve missing answers RESULT Serve everywhere chat, support, agents TRIGGER Content changes new or updated sources DATA STEP Collect sources docs, FAQs, tickets DATA STEP Chunk & embed split and vectorise DATA STEP Vector index fast semantic search AI STEP Retrieve & answer with cited sources HUMAN CHECKPOINT Curate gaps approve missing answers RESULT Serve everywhere chat, support, agents
TriggerData stepAI stepHuman checkpointResult

Aggregated main logic. Real projects add error handling, retries and logging around every step.

The problem

Company knowledge is scattered across documents, tickets and heads. AI tools need one trusted place to read from.

  1. A webhook or schedule notices changed content and re-indexes only what moved.
  2. Text is split into meaningful chunks and stored with metadata such as topic, language and approval state.
  3. Questions retrieve the best chunks first; the model answers only from them and cites where it looked.
  4. Unanswered questions are collected so a person can write the missing answer once.

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