How a Two-Person Team Built an AI Video Editing Pipeline Using Runway Aleph

A small two-person team integrated Runway Aleph, an in-context AI video model, into a content pipeline to produce short product videos at scale without manual effort. Unlike text-to-video tools, the in-context model accepts real footage as input and applies scoped, described edits — such as removing a price tag while preserving lighting — ensuring visual continuity across clips. The team structured their workflow around a JSON-based shot manifest that queued one operation per edit pass, making runs replayable and individual failures easier to isolate and debug. They found that explicitly defining what should be preserved in each prompt — not just what to change — delivered the single biggest improvement in output quality. Automated quality checks proved inadequate for catching semantic errors like malformed hands or garbled signage, leading the team to require human review of five specific high-risk frame regions at full resolution before publishing.