AI Analysis — Deep Dive
Use NotebookLM by default. Build custom RAG only when you actually have a reason.
For most teams NotebookLM is enough — fast, source-grounded, no setup. When data has to stay private, scale beyond NotebookLM's limits or integrate into other tools, we build a custom RAG pipeline on OpenAI/Anthropic plus Pinecone or Supabase Vector.
Up to a few hundred sources, non-confidential data, one team, casual querying. NotebookLM gives you 80% of the value at 5% of the engineering effort. Don't over-build.
Confidential or regulated data, integration into your own product, scale beyond NotebookLM's quotas, fine-grained access control, custom retrieval logic. We build on OpenAI/Anthropic + Pinecone or Supabase Vector + clear eval harness.