Methodology
How we score creators.
Every number in the product traces back to evidence you can see: the videos themselves. Here is exactly how content becomes a score.
01
Ingest
We index a creator's recent public videos: the footage, captions, stats, and comments. No private data, no platform credentials.
02
Extract six signals
AI converts each video into structured signals: transcript, on-screen text, comment intent, visual context, language and dialect, and brand-safety markers.
03
Score components
Signals roll up into creator-level component scores on a 0 to 5 scale: content quality, storytelling, and brand safety.
04
Match your brief
At search time, your brief is parsed into hard filters and content intent. Brief fit reflects how strongly a creator's actual content matches, never just follower counts.
What each score measures
Content quality
Production consistency, clarity of delivery, and how well videos hold attention. Derived from visual analysis and watch-pattern signals across the analyzed sample.
Storytelling
Narrative structure: hooks, pacing, and whether product mentions feel natural inside the content or bolted on.
Brand safety
Language, claims, and content risk. Specific flags (for example mild language) are always listed on the profile, so you see why a score is what it is.
Engagement signals
Comment rate and save rate relative to views, computed directly from the analyzed videos and shown against benchmarks. High saves signal reference-worthy content; rich comments signal purchase intent.
Reading the 0 to 5 scale
4.5 - 5.0
Exceptional. Consistently strong across the analyzed sample.
4.0 - 4.4
Strong. Reliable quality with minor variance.
3.5 - 3.9
Solid. Good content with notable inconsistency or flags worth reviewing.
Below 3.5
Review carefully. Flags or quality variance need a human look before briefing.
Detecting suspicious followers
Follower counts are the easiest number to fake, so we treat them as a claim to verify, not a fact. Each follower base is checked against behavior patterns that separate genuine accounts from bots and engagement farms. The signals we weigh:
- Missing or generic profile pictures
- Extreme following-to-follower ratios
- Account age and history
- Posting activity and cadence
- Empty or templated bios
- Sudden, unnatural follower growth
Who is actually behind the follower count
A follower count is a mix of fans, passers-by, and noise. Every profile splits it into five groups, so you can price the reach you are actually buying:
Genuine audience
Active, real accounts that watch, comment, and save. The people your campaign actually reaches.
Amplifiers
Real followers with an audience of their own. When they engage, the content travels beyond the creator's page.
Passive followers
Real accounts that follow thousands of profiles. They count toward reach on paper but rarely see a post.
Inflated accounts
Mass-following profiles with follow-for-follow or purchased-growth patterns. Reach you should discount.
Likely bots
Automated accounts: empty profiles, zero history, machine-generated handles. No value to a campaign.
Honest limits, beta methodology
- Scores are AI-assessed estimates from a sample of recent videos, not audits of a creator's full history. They update as new videos are indexed.
- We analyze public content only. Some platform metrics (for example Instagram saves) are not public; where shown, they are estimated from observable signals and labeled with benchmarks.
- Audience breakdowns are statistical estimates from observable account signals, not a census of every follower.
- Scores predict content fit, not campaign outcomes. A 4.7 is a strong starting shortlist, not a guaranteed result.
- The methodology is in beta and will be versioned. When the scoring changes, profiles will say so.