Case study: An AI content pipeline we built for a media client.

The challenge: turn a stream of source documents (including scanned PDFs) into automated daily email digests.

What made it hard: → Half the inputs were scanned PDFs of variable quality. Vision models for OCR > legacy OCR engines. → Hundreds of inputs/day. Pipeline extracts, groups, dedupes, rewrites, and scores by importance. → LLM landscape moves fast — we built a swappable provider layer.

Stack: Next.js + Python + provider-agnostic model layer.

If you're staring at messy inputs and wondering whether an LLM can make sense of them: yes — but the engineering matters more than the model.