From Review to Reel: How AI Turns Customer Praise Into Publishable Video

The pipeline at a glance

Ravelets follows one pipeline: Discover → Analyze → Create → Approve → Publish → Measure. Here is what happens at each stage, honestly and concretely.

Discover & Analyze

Feedback arrives through sources you control — manual imports today, connected platforms as provider integrations roll out. Every item is scored by AI on sentiment, enthusiasm, specificity, storytelling potential and authenticity, then given a composite Ravelet score. Eligible, high-scoring items become candidates.

Importantly, analysis only runs on feedback you are authorized to use, and nothing is invented: if there is no customer quote, there is no customer quote.

Create

For each candidate, the AI drafts a complete short-form video package:

• A hook — what happens in the first two seconds
• A scene-by-scene storyboard — shot on a phone, feasible for a small team
• A spoken script — 60 to 120 words
• A caption and hashtags — copy-ready
• An optional AI-generated thumbnail in your brand palette

The customer's own words may appear as on-screen text — nothing more is fabricated.

Approve (the step that matters)

Nothing publishes without you. Every concept lands in your approval queue where you can approve, regenerate, edit or reject it. This is deliberate: automated content about real customers without human review is how trust gets broken.

Publish & Measure

Approved concepts move to the publishing calendar. Today that means a manual publishing kit — copy-ready captions, hashtags, assets and reminders. Assisted publishing activates per platform as API credentials and account authorizations are configured. Your dashboard tracks the pipeline from discovery to publication so you can see what is flowing and what is stuck.

The takeaway

AI does the repetitive 80% — finding, scoring, drafting. You do the 20% that requires judgment: choosing what represents your brand. That division of labor is the whole product.