Reduce cross-community spam with coordinated bot detection and reporting

Where this came from

The pain

Stop recurring bot spam across communities by detecting cross-community patterns, automating reports and actions, and surfacing false-positive risks so moderators spend time on real members, not cleanup.

Who it is for

Community moderator teams for SMB and mid-market communities (subreddits 5K–200K subscribers, Discord servers 1K–100K members, Facebook groups with active moderation). Typical buyer is a head moderator or community manager who spends hours per week triaging spam, uses browser-based tools, and values explainable evidence, SLAs, and time-to-value under 2 weeks. Technographically they use browser extensions, webhooks, and lightweight admin dashboards and are willing to adopt SaaS tools via credit-card or invoice billing.

Source pains

• Risk — platform policy or API changes could remove integrations and revenue; mitigate by diversifying connectors and maintaining close platform partnerships.
• Risk — false positives could erode moderator trust and adoption; mitigate with human-in-the-loop verification, explainable evidence, and appeals workflows.
• Risk — model drift as bot behavior evolves could reduce detection accuracy; mitigate via continual labeling, active learning loops, and community feedback.