Podcast Transcript RAG System

The Problem

An enterprise software company had invested heavily in thought-leadership content — hundreds of podcast episodes featuring industry experts, customers, and internal leaders discussing trends, use cases, and best practices. It was some of the best sales enablement material the company had ever produced.

Nobody was using it. The content was locked inside audio files. A sales rep preparing for a call with a healthcare company would need to somehow know that episode 47 had a great segment on healthcare compliance, then listen to a 45-minute episode to find the three minutes that were relevant. So they didn't. Hundreds of hours of expert knowledge sat unused while reps relied on the same recycled pitch decks.

The Solution

We built a retrieval-augmented generation (RAG) system that ingests podcast transcripts, chunks them intelligently, embeds them into a vector store, and makes the entire library searchable through natural language queries. A rep types "what did customers say about compliance challenges in healthcare?" and gets sourced, cited answers pulled from across hundreds of episodes — with links back to the specific segments.

The system handles new episodes automatically as they're published. Transcripts are processed, chunked, embedded, and indexed without manual intervention. The knowledge base grows on its own.

How We Thought About It

RAG systems are having a moment, and most of them are built the same way: dump documents into a vector database, run a similarity search, and paste the results into a prompt. That works for demos. It falls apart in production when users ask nuanced questions and get irrelevant chunks back.

The difference between a demo RAG system and a useful one is in the chunking strategy and the retrieval logic. Podcast transcripts are conversational — topics drift, circle back, and overlap. A naive fixed-length chunking approach splits a coherent thought across two chunks or combines two unrelated topics into one.

We built topic-aware chunking that respects the conversational flow of each episode. The retrieval layer uses hybrid search — combining semantic similarity with keyword matching — because sometimes a rep is searching for a concept ("digital transformation challenges") and sometimes for a specific term ("SOC 2 compliance"). Both need to work.

Every answer includes citations back to the source episode and timestamp. The system doesn't just answer questions — it shows its work. That's the difference between a tool people trust and a toy they abandon.

The Result

• Hundreds of podcast transcripts indexed and searchable in seconds
• Sales reps find relevant talking points without listening to full episodes
• Every answer cited back to source episode and timestamp
• New episodes automatically indexed as they're published
• Adopted by sales teams as part of daily call preparation