AI Analysis
Content analysis with NotebookLM
Long documents, research papers, transcripts, books — feed them into NotebookLM (or custom RAG setups) and turn dense content into clear answers, summaries and audio briefings.
What you get
- Source-grounded Q&A across your own document corpus
- Auto-generated summaries, study guides & timelines
- Audio overviews — podcast-style briefings from your sources
- Custom RAG when NotebookLM is not enough (private data, scale)
How I build it
- 1Collect & clean your source documents
- 2Upload to NotebookLM or index in a vector DB
- 3Design prompt templates for the questions that matter
- 4Deliver outputs as docs, audio, dashboards or chat
Stop drowning in PDFs
Most knowledge work is reading. Reports, research, contracts, transcripts, course material. NotebookLM and custom RAG setups let you upload that mountain once — and then ask questions, generate summaries, build study guides and even create podcast-style audio briefings on demand.
NotebookLM first, custom RAG when needed
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.
Outputs people actually use
Insights only matter if they reach a human. We deliver as Notion docs, Slack briefings, dashboards, chat interfaces or audio — whatever fits your team's existing workflow.
Tools & stack
Typical use cases
- Research synthesis & competitive intelligence
- Knowledge bases for teams & consultants
- Course & book breakdowns for learning