Turn your business documents into an AI-powered knowledge system that can retrieve and answer questions instantly.
Businesses store valuable knowledge across documents, PDFs, guides, and internal resources, but finding the right information quickly can be difficult.
Employees often spend time searching through documents manually to find specific information.
As the amount of business knowledge grows, traditional document searching becomes slower and less efficient.
Without a structured knowledge retrieval system, valuable information remains difficult to access when it is needed.
Business knowledge is only valuable when people can access it quickly.
Manual document searching wastes time and makes it harder to consistently retrieve the right information.
Information becomes scattered across multiple files. Employees spend time searching instead of working. Important context can be difficult to locate. Repeated questions require repeated manual research.
A knowledge retrieval system creates a faster way to interact with stored business information.
The system has two primary workflows: knowledge ingestion and question answering.
When a document is submitted, the system retrieves the file and processes its contents for knowledge storage.
The document is converted into structured information and transformed into embeddings using Google Gemini.
The resulting knowledge is stored inside a Supabase vector database, creating a searchable knowledge layer.
When a user sends a question through the webhook, an AI Agent processes the request and searches the vector database for relevant information.
The retrieved context is passed into the AI reasoning process so the system can generate an answer based on the stored knowledge.
A second AI Agent then cleans and structures the generated response before returning the final answer through the webhook.