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I built this authority-scoring + matching system in n8n for a podcast-guesting platform. A guest gets scored across podcast history (Listen Notes), domain authority (Moz DA/PA), website quality, LinkedIn influence and social engagement -- all assembled into one composite score by modular sub-workflows. A custom matching algorithm then pairs each guest with the right shows. Webhook-triggered, runs completely hands-off. Replaced a fragile Zapier setup and cut manual scoring to zero.
This is a great example of using automation to improve decision quality, not just save time.
Thank you 😊
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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Hierarchia is a production AI-powered document review platform built for inspection and audit teams. It validates inspection reports against quality rules using AI in under 60 seconds, replacing 6-10 hours of manual review per manager per week.
CHALLENGE: Inspection teams were manually reading every submitted report, checking quality against dozens of rules. Reviews were inconsistent across managers, submissions arrived via WhatsApp/email with no audit trail, and deadlines were missed.
MY SOLUTION:
AI Validation Engine: Built an AI system that scores every inspection report against configurable quality rules in under 60 seconds. Managers see scores, summaries, and risk flags without reading a single page.
RAG-Based Smart AI Chat: Implemented a Retrieval-Augmented Generation chat system (pgvector) allowing managers to ask questions about any submission and get instant, context-aware answers.
17-Module Platform: Delivered Overview, Tasks, Submissions, Messages, Announcements, Materials, Smart AI, Reports, Activity Log, Validation Rules, Team Members, Departments, Access Control dashboard.
Full RBAC: Granular role-based access control for different team hierarchies.
TECH STACK: Next.js, Supabase, pgvector, RAG, AI Validation, RBAC
RESULTS: 82% AI pass rate on submissions. Average quality score 84.2. Saves each manager 6-10 hours/week. Live at showcase.system.zamdevai.com
I built OmniBot AI, a full-stack dashboard designed to simulate and test automated WhatsApp Business workflows powered by AI. It bridges the gap between raw data storage and real-time LLM grounding.
Key implementation details:
• Knowledge Base Engine: Implemented a system context pipeline that allows users to write dynamic business rules/FAQs and sync them directly into vector context for the bot.
• Bilingual AI Support: Integrated Gemini AI with explicit prompt routing to automatically detect, process, and reply in both English and Arabic.
• Live MongoDB History Stream: Built a real-time JSON log viewer on the frontend that streams database updates, tracking session language, token usage, and cache success states.
• Infrastructure Analytics: Developed a monitoring component to track total synchronization logs and calculate average API response latency (optimized down to 1.2s).
Tech Stack: MongoDB, Express.js, React, Node.js, and Google Gemini API.
Built B2B lead generation system—scraped 4,000+ verified US university
and Indian universities' contacts using Python, BeautifulSoup, and openpyxl. ~1% bounce rate.
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