AI summarization
Long-form proceedings, distilled
Hours of recorded sessions become a four-minute letter — every claim time-linked to the tape.
The system
An ingestion pipeline watches long-form recorded sessions nobody has time to watch, extracts what was actually decided, and threads related items into narratives that span months.
Every extracted claim carries a timestamped citation into the source video. Receipts, not summaries — a reader can check any sentence against the tape in one click.
The hard parts
Auditable summarization
The citation-first design makes model output verifiable. Trust comes from the link, not from the prose.
Threading across months
Items are resolved into long-running threads across sessions, so a decision's entire history reads as one timeline around a today-line.
Six days to a working system
Ingestion, extraction, threading, a reader, and the render-to-send email rail — zero to working in six days.
In numbers
Python · caption harvesting · Next.js · email rail
Happy to walk through this one properly — what it does, how it's put together, and what it would take to build something like it for you.