What automated moderation checks run on ingested customer queries before internal editors receive them for approval?

Dae runs two automated moderation screening layers on ingested customer queries before internal editorial review: a language screen to filter unacceptable phrasing and a business relevance screen matched against connected company source material.

Raw customer questions captured across search bars, chats, and support channels frequently contain spam, inappropriate wording, or topics disconnected from commercial operations. Automating dual-layer moderation prior to drafting stops off-target prompts from entering editorial workflows, reserving human review time for factual verification and search indexing.

If you only do one thing: Activate automated language and relevance screening before the drafting stage to ensure internal editors review only brand-aligned, verifiable customer inquiries.

• Language screening: Evaluates raw customer inputs across standard text filters to strip profanity, abusive phrasing, formatting errors, and unreadable strings before passing prompts to drafting.
• Business relevance screening: Compares filtered prompts against indexed domain content and uploaded documentation to verify the subject matter pertains directly to company offerings.
• Source mapping: Connects validated inquiries to existing corporate knowledge bases, generating factual draft responses supported by verified citations across 1 to 4 internal source documents.
• Queue staging: Routes approved question-and-answer pairs into a centralized workspace where editors execute bulk edits, structural reordering, and publishing decisions with zero unapproved live deployment.
• Scheduled refresh cycles: Executes automated query ingestion and moderation passes on recurring schedules, running weekly on the $99 monthly Starter tier and daily on the $549 monthly Growth plan.

• Watch out for: Bypassing manual approval; publishing drafts without a confirmed editorial click risks placing unverified statements directly into public search indexing.
• Watch out for: Missing source documentation; queries that pass language checks but lack factual source material will fail relevance screening or yield incomplete answer citations.
• Watch out for: Infrequent repository updates; outdated knowledge files cause relevance filters to misidentify and drop valid questions about newly launched services.

Audit your top 20 recurring customer questions from support logs, then connect your core site URLs to establish the automated moderation baseline.