02:18
Clarity
Reveal what changed and why it matters.
- Lead with the decision.
- Use plain language.
- Keep the important nuance.
ABOUT VIDEORADAR
Video knowledge
should be clear enough
to question.
We build evidence-linked tools for people who learn, decide, and collaborate from the world's largest video library.
Independent product · remote team · built with user feedback
WHY WE STARTED
Watching is linear. Knowledge work is not.
Long videos hold expert insight, but finding one claim again—or sharing it responsibly—still requires replaying, scrubbing, copying, and asking teammates to trust your notes.
42 minaverage long-form video in a research queue
7 tabsopened to verify one manually written takeaway
1 sourceshould be enough to resolve a disagreement
THE PROBLEM
Information gets compressed.
Context gets lost.
Most AI video notes optimize for shorter text. We care about a different outcome: making the meaning easier to inspect and reuse.
WITHOUT A SOURCE TRAIL
A fluent answer can still be impossible to verify.WITH VIDEORADAR
Every important claim points back to the moment.ONE CLAIM, THREE STATES
SUMMARY
Teams adopt AI faster with clear ownership.EVIDENCE
“Adoption accelerated after one owner defined review rules.” · 18:42CONTEXT
Speaker: Head of Ops · 80-person company · Pilot lasted 6 weeksOUR PRINCIPLES
Three constraints guide every product decision.
Evidence
Keep every important takeaway inspectable.
- Link timestamps by default.
- Separate claim from context.
- Show uncertainty honestly.
Control
Users choose the question, filters, and destination.
- No forced feed.
- No invented urgency.
- Export your work anytime.
HOW THE PRODUCT GREW
A small timeline built from specific user problems.
- 12024
Prototype
Researchers asked for a summary they could cite.
Timestamps - 22025
Channel monitoring
Creators needed to notice changes without checking feeds.
Watchlists - 32026
Team recaps
Teams wanted one recurring brief with shared evidence.
Delivery - 4NEXT
Deeper synthesis
Compare claims across time while preserving context.
In progress
WHAT DID NOT CHANGE
We still reject summaries that hide uncertainty, features that trap user data, and metrics that reward noise.
HOW WE WORK
Small team.
Short distance to users.
The people reading feedback are the same people shaping the product. We favor direct conversations over layers of process.
Review source-quality failures before growth metrics.
Publish feedback-linked product changes.
Let users export work and verify our output.
ABOUT FAQ
The questions behind the company.
No. Product inputs and saved notes are not used to train public models without explicit permission.
No. We charge for the product; we do not sell individual watchlists, notes, or usage profiles.
YouTube combines expert depth with source timestamps. Focusing lets us build a better evidence workflow first.
The product is automated. We evaluate quality with sampled audits, user reports, and source-link accuracy tests.
Use the Feedback page for product notes or email support for account and billing questions.