AI Content Automation Pipeline
From approved data sources to structured, reviewable drafts. Dashboard values are DEMO DATA. This is a controlled workflow, not uncontrolled mass publishing.
Project overview
The demo shows a review queue: collect from RSS/APIs/authorized sources, dedupe, extract, analyze, draft, suggest metadata, wait for a human, then optionally publish to a CMS.
Problem being explored
Editors spend time collecting the same source updates instead of reviewing drafts that already wait in a queue.
Example user scenario
A scheduler checks permitted feeds at 06:40 (DEMO DATA). New items land, duplicates drop, drafts wait for review. Nothing publishes because no CMS is connected.
Proposed workflow
- RSS / APIs / AUTHORIZED SOURCES
- COLLECT
- DEDUPLICATE
- EXTRACT
- AI ANALYSIS
- EDITORIAL DRAFT
- SEO METADATA
- QUALITY CHECK
- HUMAN APPROVAL
- CMS PUBLISH
Architecture explanation
Scheduler → collect → new-item detection → deduplicate (database state) → extract → AI analysis → editorial draft → SEO metadata → quality check → human approval → CMS publish. Errors: retry → alert.
Key features
- Source monitoring on a schedule
- Deduplication stored in a database
- Extraction, classification, summarization for editors
- Draft SEO titles/descriptions as suggestions
- Retry + alert instead of silent failure
- Human approval before CMS publish
DEMO DATA
SCHEDULER → PIPELINE
SCHEDULER → PIPELINE. ERROR → RETRY → ALERT. DATABASE → prevents duplicate processing.
Demo interface
Counts on the dashboard are DEMO DATA. Try Demo does not crawl the web or post to WordPress.
FAQ
Does this publish to our blog automatically?
The concept keeps a person in front of CMS publish when rights or quality are unclear. Automatic publish can be designed only where you explicitly allow it. This DEMO / CONCEPT does not publish to WordPress or any live CMS.
Is this scraping websites for SEO?
The design uses authorized sources, APIs, or feeds, plus editorial review. It is not a copy-and-rewrite service and does not ignore robots or copyright. Collection is meant to save editor time, not to clone the web.
What does the database do?
It remembers processed IDs and hashes so the same item is not drafted twice. That keeps the review queue usable. Deduplication is part of a real pipeline, not a dashboard decoration.
What happens if extraction fails?
The run can retry, then raise an alert. Other sources should keep processing. Failed items stay visible so an editor can see what stopped rather than a silent gap.
Will this improve Google rankings?
The system can draft titles, descriptions, and internal-link suggestions for an editor. That supports SEO work; it does not buy rankings. Search performance still depends on competition, content quality, and continued optimization.
Limitations
No live crawl. No CMS is connected. Counts are labeled DEMO DATA.
What production would require
Authorized sources, a database that blocks duplicates, retries, alerts, an editor, and a human publish step. Not a ranking product.