Making decades of archive searchable with RAG

Every broadcaster and publisher sits on a goldmine it can't dig into. Decades of footage, articles, and audio — findable only if someone remembers the right keyword from a rundown written twenty years ago. Retrieval-augmented generation (RAG) changes that equation: instead of matching keywords, you search by meaning, and a language model assembles the answer with citations back to your own material.

Why RAG fits media archives

Fine-tuning a model on your archive is expensive, slow to update, and prone to hallucination. RAG keeps your content in a searchable index and feeds only the relevant passages to the model at question time. New content is searchable the moment it's indexed, and every answer can link back to the source asset — which matters enormously for editorial trust and rights clearance.

The traps we see most often

The technology is the easy part. Projects stall on three things: transcripts that were never generated or are too poor to index, metadata that doesn't say who may use the material and where, and chunking strategies that split interviews mid-thought. Getting speech-to-text quality and rights metadata sorted first typically doubles the usefulness of the final system.

Where to start

Pick one high-value collection — an election archive, a sports back-catalogue — and build a narrow pilot with real editorial users. Measure time-to-find against the current process. In our experience the business case writes itself after the first week of newsroom use.

Working on an archive project? We'd be glad to compare notes.