A follower count is a label. CreatorScore finds every account a creator owns across 12 platforms, transcribes and watches the actual content, analyzes the audience comment-by-comment, and scores it through seven specialized agents — into one auditable number, with the evidence attached to every point of it.
The scope
A clean Instagram feed can sit next to a controversial X account or an off-brand Twitch stream. Give us one handle and we resolve the rest of the footprint ourselves — then score the person, not the account.
Videos · comments · replies · transcripts · demographics · live
Posts · reels · stories · highlights · carousels · comments · transcripts
Videos · Shorts · comments · replies · transcripts
Posts · replies · video frames · profile
Profile · posts · comments · transcripts
Profile · clips · VODs · live streams
Profile · clips · VODs · live streams
Profile · posts
Posts · comments
Profile · posts
Profile · posts
Profile · public content
The pipeline
Four stages. Every one of them produces evidence a creator can read and contest, and a brand can act on.
We start by working out who the creator actually is — every account they own — then pull the public record from each one.
Scoring reads a recent window of roughly 180–365 days — enough to describe who a creator is now. When a brand needs the whole history, the Social Background Check paginates back to the first post, up to a decade, and recovers deleted content from web archives.
Then we actually consume the content — audio, pixels and text — instead of scanning the caption and guessing.
Captions can be edited. Audio can't. A creator can't hide a problem in a video just because the caption is clean — and, just as important, a clean creator doesn't get flagged for a caption a keyword scanner misread.
An audience is half the deal. We score the audience the way we score the creator — from the actual comments.
Fake followers are cheap to buy and easy to hide from a follower count. They are very hard to hide from a comment-by-comment forensic pass over hundreds of thousands of comments.
Seven agents each answer one question from the evidence. The weighted average of those seven is the CreatorScore.
When a signal genuinely doesn't apply — no video to transcribe, no web coverage to find — its weight is redistributed across what we did measure. We never hand a creator a default penalty for data we don't have.
The seven agents
One general-purpose AI giving an opinion is a vibe. Seven specialized agents, each scoring one dimension from evidence, is a system.
What the creator publishes — hate signals, deceptive and scam content, NSFW, misinformation, on-screen visuals and spoken transcript moments, weighted by flag severity.
Whether the audience is real. Comment-level bot analysis blended with follower authenticity, engagement-pod detection and growth-curve anomalies.
Whether attaching a brand to them is safe. FTC disclosure compliance, brand-partnership patterns, controversy breadth, corroborated web reputation, feuds, following-graph risk.
Whether the audience is worth reaching. Engagement rate and depth, community health, velocity, niche fit, demographics and loyalty.
Whether their voice is coherent and how the audience receives it — voice consistency, audience sentiment, tone safety, engagement style. Shown as “Voice Stability” in the dashboard.
Whether their recommendations hold up. Disclosure compliance on verified partnerships, brand alignment, and conduct toward their own community.
Whether a campaign is likely to work. Engagement, growth trajectory, shares and saves, community health — forward-looking, not a history lesson.
Every component inside every agent — down to the decimal — is published on the benchmarks page, along with the full score bands.
See the rubricThe guardrails
Some failures can't be averaged away — a creator with world-class engagement and a bot farm for an audience is not a 78, they're capped at 20. But a cap is the most damaging thing we can do to a creator, so every one of them has to clear a bar first.
Every content-based knockout requires a pattern — multiple confirmed posts, or a prevalence threshold across the whole history. One heated caption or one bikini photo is a flag, not a cap.
Hate caps require the classifier AND an LLM review confirming the creator produced it. Disclosure caps require verified partnership records, not caption keywords. NSFW caps require vision confirmation, never a raw thumbnail score.
Commentary, news and true-crime creators discuss hate, crime and scandal for a living. Knockouts are gated so reporting on a thing doesn't score like doing it — the distinction keyword scanners get wrong constantly.
If we couldn't fetch enough of something to judge it, that signal abstains and its weight moves to what we did measure. A creator is never marked down for a gap in our collection.
A cap is a state, not a permanent mark: clear the condition and it lifts on the next rescore. Every knockout in production is published — the cap, what fires it, and the evidence it has to clear.
The output
The score is what you show your CMO. The evidence underneath it is what survives the follow-up question.
No count without a cause. Each risk shows what it is, why it fired, how severe it is, and links to the post, comment or frame behind it — for the brand and for the creator.
Monitored creators sync daily, rescore on a rolling monthly cycle, and get high-frequency signals — live streams, new posts, comment shutoffs — polled more often. Material changes trigger an email the same day.
Scoring describes a recent window of roughly 180–365 days. Where a brand needs the entire history, the Social Background Check goes back to the first post and recovers deleted content from web archives.
The same score and evidence in the dashboard, in white-label PDF reports, over the public API, and through the CreatorScore MCP so an AI assistant can vet a creator in the chat you're already in.
Both sides of the deal
Questions
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 & Voice (10%, shown as Voice Stability in the dashboard), 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. Every sub-weight inside every agent is published on the benchmarks page.
All of them. A CreatorScore is always the creator's complete cross-platform footprint — give us one handle and we resolve the other accounts they own, then return one unified score with the per-platform breakdown underneath as supporting detail. A creator is a person, not a TikTok account, and the person is what a brand signs. Billing matches the model: one credit is one creator, however many platforms they're on.
Scoring reads a recent window of roughly 180–365 days, which is enough to describe who a creator is now, and the window is stated on the score itself rather than implied. When a brand needs the entire history — the 2019 post someone might resurface — the Social Background Check paginates back to the first post, up to a decade, and recovers deleted content from web archives.
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.
One handle in. Their whole footprint, one score, and every risk named with the evidence behind it.