Antispam Bee is the privacy-friendly alternative to Akismet that many German and European WordPress sites pick: it filters comments locally instead of sending data to a third-party service. The plugin stores its settings in wp_options under antispam_bee and stores per-comment spam-reason logs (when enabled) under antispam_bee_spam_reasons. Filters include empty session token, BBCode, fake IP, and country block, each catching a different kind of automated noise.

SleekAI runs as a separate plugin and reads the comment policy, privacy note, and moderator contact you have already published. The chatbot never reads the Antispam Bee options or the per-comment spam reasons, since the team's published policy is what matters to a confused commenter. Antispam Bee handles filtering; SleekAI handles explanation.

Display conditions scope the chatbot to the policy page, the help section, and the contact template. Every conversation logs the originating URL, model name, and token usage so the team can see which filter rules generate the most questions, then either soften the policy wording or document a recovery path more clearly. Generic chatbots cannot reach this level of specificity because they have no view of your published policy at all.

Teams that pick Antispam Bee over Akismet are often making a deliberate privacy choice: they want spam filtering without sending visitor data to a third-party service. That choice is real and meaningful, and it shows up in audited European publications, niche blogs, and any site that takes GDPR seriously. The trade-off is that Antispam Bee is more rule-based and less learned, so a real commenter occasionally trips a heuristic they did not realize existed.

From their seat the comment just vanishes, and the silent drop is more disorienting than an explicit "moderation queue" message would be. A help-page chatbot turns that silent drop into a conversation. SleekAI is the right shape for this because it shares the same privacy framing.

The bot reads only the policy and privacy content the team has already written, not the filter's working data. When a commenter asks about IP logging or whether anything is shipped to a third party, the bot can answer honestly with the wording the team approved, without making the situation worse with a generic chatbot guess. Conversation logs reveal which filter rules confuse people most, and the team can either soften the rule, document the recovery path more visibly, or rewrite the policy page in one place.

The privacy stance stays intact and the legitimate commenters stop getting lost.