A 8-step rag & knowledge automation, triggered by schedule, connecting OpenAI Embeddings, Airtable, Supabase Vector, with built-in AI reasoning.
In plain terms: this turns your documents into a smart Q&A brain. It reads and remembers your content, then answers questions accurately using OpenAI Embeddings, Airtable, Supabase Vector — great for support and internal knowledge.
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Builds a retrieval-augmented knowledge system: content is embedded into a vector store and served back as grounded, citeable answers. Ideal for support deflection, internal knowledge bases, and AI assistants that must stay factual.
Generic chatbots hallucinate; companies need answers grounded in their own documents. RAG is the proven pattern, and a working pipeline is scarce and valuable.
RAG assistant implementations typically run $5,000–$20,000 bespoke. Packaged, it underpins support-deflection tools that sell for $99–$999/mo.