Following step 2 of the question framework, how does the platform transform raw intent into verified draft responses?

Dae transforms raw customer intent by analyzing inbound inquiries, matching buying themes against ingested company documentation, and generating sourced drafts filtered through two automated moderation layers before human review.

Customer inquiries across sales calls, direct messages, and website search bars routinely disappear after one interaction, stranding commercial signals. Capturing this demand requires an automated system that structures messy questions into verifiable, search-indexed knowledge assets.

If you only do one thing: Ground all automated answer drafts strictly in ingested internal documentation so every factual claim cites a verified company file or URL.

1. Identify buyer intent: Dae captures natural-language inquiries from site search and communication channels, mapping raw user questions into commercial buying categories including pricing, implementation steps, and product alternatives.
2. Ingest internal documentation: The platform indexes uploaded PDF documents and connected website URLs, creating a closed reference baseline from existing company assets within the standard 7-day onboarding period.
3. Synthesize structured drafts: Answer engines draft concise responses that open with concrete specifications, price ranges, or direct recommendations, attaching explicit source citations to every factual claim.
4. Execute two-layer moderation: The system processes each draft through two automated screening filters—evaluating language clarity in the first pass and confirming strict business relevance in the second pass.
5. Stage for human approval: Screened drafts populate a central review queue with dedicated permalinks and JSON-LD schema, enforcing mandatory 1-click human verification before any content goes live.

• Watch out for: Sourcing draft answers from external web scrapes instead of authenticated company documentation and approved internal rate sheets.
• Watch out for: Attempting full auto-publishing configurations, as every tier requires human review to maintain 100% brand control over live statements.
• Watch out for: Treating search queries as generic keyword targets rather than high-intent commercial questions that require precise numerical benchmarks.

Connect your core product documentation and pricing PDFs to the platform ingest queue, then audit your first weekly batch of drafted answers before publishing.