A follower count is a label. CreatorScore collects every public signal across 12+ platforms, transcribes and watches the actual content, analyzes the audience comment-by-comment, and scores it through 7 specialized agents — into one auditable number.
The scope
A clean Instagram feed can sit next to a controversial X account or an off-brand Twitch stream. Single-platform tools miss it. We see the whole person.
Videos · comments · replies · demographics · transcripts · live
Posts · reels · stories · comments · transcripts
Videos · Shorts · comments · replies · transcripts
Tweets · replies · transcripts · profile
Profile · posts
Profile · posts · comments · transcripts
Posts · comments
Profile · posts
Profile · clips · live streams
Clips · live streams
Profile
Engagement · follower-growth history
Boards · pins
Profile · posts
Most competitors cover 2-3 platforms. We cover 12+.
The pipeline
Here's exactly what happens between "enter a handle" and "here's the score." Each stage feeds the next.
We pull the creator's full footprint from every connected platform in parallel — and we don't sample.
For a full background check we paginate up to a decade of content. The 2019 tweet a brand is terrified of? We find it.
This is where we separate from every metrics-only tool. A caption says 'fun day at the beach.' The video might say something else. So we analyze the actual media.
Most 'brand safety' tools scan captions and hashtags. We transcribe the audio and watch the frames. A creator can't hide a problem in a video just because the caption is clean.
A creator is only as safe as their audience. We analyze the comments themselves — one by one.
Fake followers are easy to buy and easy to hide from a follower count. They're very hard to hide from a comment-by-comment forensic analysis.
Only now — content watched, audience analyzed — do we score. Seven specialized agents, each a domain expert, blend into one 1-100 number.
One generic AI giving an opinion is a vibe. Seven specialized agents, each scoring one dimension from evidence, is a system.
The 7 agents
Each agent evaluates one dimension from evidence. The weighted blend is your score. The brand isn't trusting "an AI" — they're trusting seven independent reads that have to agree.
Hate speech, NSFW, violence, profanity, visual risk — across captions, transcripts, and frames
Real vs. bot/purchased audience, blended with follower-growth authenticity
FTC disclosure, public controversy, brand-mention patterns, web reputation
Community health + engagement depth — substantive comments vs. spam
Sentiment stability over time + how the audience actually feels
FTC compliance + creator conduct — how they handle conflict and crises
Forecasted partnership performance — engagement, community health, growth
One 1-100 number, tiered Exceptional → Poor, auditable to the post.
The fairness layer
Severe issues cap the score, so a high engagement rate can't paper over a real problem.
A focused 5K-follower B2B creator and a 5M-follower lifestyle creator are scored on a level playing field. A clean micro-creator can score Excellent.
Scale alone doesn't earn or lose points. Content quality, audience authenticity, and brand safety do.
The pipeline produces an auditable case file, not just a number.
A reputation isn't a one-time snapshot. The score re-runs on a rolling 30-day cycle across every connected platform.
Score drop, new flag, new sponsored deal, audience-health shift — the creator and any watching brand are notified the same day. High-frequency signals (live streams, breaking activity) are polled more often than the monthly base cycle.
The actual video. Every video and Short is transcribed (we read what was said, not just the caption), and we extract and analyze video frames and thumbnails for visual risk that never appears in text. On-screen text and story overlays are read via OCR. A clean caption on a risky video doesn't get a pass — and a good creator gets full credit for clean content a text-only keyword scanner would have wrongly flagged.
At the comment level, not the follower level. Follower counts are trivial to inflate and hard to verify. We score comments individually for bot signals, detect engagement-pod clusters (the coordinated 20-200 account rings that fake organic reach), and cross-reference follower-growth spikes against content output. That's how we tell a real 500K audience apart from a bought one.
Content Risk (20%), Authenticity (20%), Brand Safety (15%), Audience Quality (15%), Sentiment (10%), Community Trust (10%), and ROI Prediction (10%). Each is a specialized evaluator scoring one dimension from evidence. The weighted blend is the final 1-100 score. The brand isn't trusting 'an AI' — they're trusting seven independent reads that have to agree.
No. Knockout factors cap the score when severe issues are detected: a bot audience over 60% caps the score at 20, engagement pods over 80% cap at 30, and so on. A high engagement rate can't paper over a real problem — which is exactly why brands trust the number.
Yes. Tier normalization means a focused 5K-follower creator and a 5M-follower creator are scored on a level playing field. A clean micro-creator can score Excellent. Scale alone doesn't earn or lose points — content quality, audience authenticity, and brand safety do.
It re-runs on a rolling 30-day cycle across every connected platform, with high-frequency signals (live streams, breaking activity, new stories) polled more often. If something material changes — a score drop, a new flag, a new sponsored deal — the creator and any watching brand are notified the same day.
Always. Every risk flag links to the specific post, comment, or video frame that triggered it, with the reasoning. There's no black box — a creator can see exactly what to fix, and a brand can see exactly why a flag exists and decide whether it's a dealbreaker or a non-issue for their campaign.