Collect the public X signals
Kollab reads profile and recent timeline signals from each handle, then normalizes account age, follower data, bio links, verification, and visible engagement.
Audit X creators for fake followers, bot-like engagement, paid-post networks, and collaboration risk before you spend campaign budget.
X has too many accounts that look useful at a glance: inflated followers, paid verification, generic replies, copied engagement, and networks that amplify each other without real audience trust.
Kollab turns influencer vetting into an evidence-based workflow. Paste X profiles, let the agent collect public signals, calculate health metrics, score risk dimensions, and leave a report your brand team can review before outreach or payment.

Прочитайте процесс целиком, а затем подставьте свои роли, источники и результаты.
Kollab reads profile and recent timeline signals from each handle, then normalizes account age, follower data, bio links, verification, and visible engagement.
The workflow checks engagement rate, follow-back ratio, generic replies, repeated promotional language, suspicious URLs, and network overlap.
Every creator receives evidence, risk score, and a contact decision so brand teams can compare real audience quality instead of raw follower counts.
Kollab leaves the report, shortlist, rejected accounts, manual-review questions, and payment guardrails in the campaign workspace.
Kollab helps brand teams judge X accounts by audience quality, risk signals, and reviewable evidence.
| Manual vetting | With Kollab | |
|---|---|---|
| Profile review | Marketers open profiles one by one and rely on follower count, paid verification, and a quick timeline scan. | Kollab normalizes account age, bio, links, verification, follower data, and recent timeline signals for every account. |
| Fraud signals | Bot-like replies, engagement pods, and paid-post network clues are easy to miss when the list is long. | The report flags suspicious ratios, generic replies, repeated wording, suspicious links, and connected-account patterns with evidence. |
| Decision quality | A creator may be approved because the top-line follower number looks large. | Each account gets a risk score and a decision: reject, manual review, or safe to contact. |
| Campaign memory | The reasons behind a rejection often disappear after the campaign list changes. | Audit evidence, shortlist, review questions, and payment guardrails stay attached to the campaign workspace. |
| Total time | Follower count plus scattered notes | Risk-scored creator audit with evidence |
A good creator check should leave a decision record the whole brand team can trust.
Metrics
Risk
Decision
Campaign
Откройте страницы связанных возможностей и посмотрите, какие слои продукта и инструменты делают этот сценарий повторяемым для команды.
Audit X creators for fake followers, bot-like engagement, paid-post networks, and collaboration risk before you spend campaign budget.
Скопируйте промпт настройки в Kollab, замените плейсхолдеры на контекст своей команды и запустите его как задачу, которую можно проверить прямо в рабочем пространстве.
A health check report with profile metrics, engagement calculations, fake-account risk evidence, network overlap notes, reject / review / contact recommendations, and a brand-safe outreach shortlist.
Нет. Процесс сначала готовит черновики, брифы, артефакты или обновления базы данных для проверки — команда одобряет результат до того, как что-либо будет опубликовано или изменено.
Turn profile links into a risk-scored influencer audit your brand team can review before outreach, contract, or payment.
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