Case Study 01 — Automated Media Infrastructure
Sharafi News
news.alsharafihq.com · Autonomous Digital News Platform
The Problem
The traditional news lifecycle is built for delay. Editorial teams manually curate, translate, format, and publish every story — a workflow that is slow, expensive, and impossible to scale across markets, languages, and time zones.
- Manual curation bottleneck — hours lost monitoring RSS, de-duplicating, and selecting publishable stories.
- Operational overhead — translation, SEO formatting, image sourcing, and CMS publishing require constant human intervention.
- Cost-inefficient scaling — covering more markets means hiring more editors, not building smarter systems.
The Solution
Architected and deployed a fully autonomous, AI-driven news pipeline that delivers publication-ready journalism without human intervention — from ingestion to indexing.
- Intelligent ingestion & filtration — fetches and scores RSS feeds, de-duplicates by slug, and selects only high-signal stories with weighted quota logic.
- AI-native publishing — LLMs generate SEO-optimized, structured long-form articles with internal linking, FAQs, and NewsArticle + BreadcrumbList JSON-LD injected at the edge.
- Zero-touch operations — automated image sourcing, Prisma/PostgreSQL persistence, sitemap & Google Indexing API dispatch, and Telegram distribution on every publish cycle.
The Architecture
Systems-engineered for resilience, cache efficiency, and hands-off scale. Every layer is observable and horizontally extensible.
Micro-caching at Nginx + Cloudflare — 18ms origin TTFB, HIT in 30ms
Scheduled cron every 2 hours, 5 verticals, idempotent slug guard
Programmatic SEO: CollectionPage, ItemList, SearchAction, sitemap + Google News
Live metrics — fetching…
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