AI agents pull content via News APIs, search engines, and compliant web-crawlers, normalizing articles and metadata from international outlets.

Raw articles land in a secure Raw Database and are vectorized with RAG into a Vector Database for fast semantic search and retrieval.

Users ask questions in plain language—by keyword, date, source, or sentiment—and get synthesized answers with linked source articles.

A Large Language Model scores tone (positive/negative/neutral) and detects media stance/bias trends to inform policy and communications teams.

Rules watch for breaking topics (terror incidents, cyber breaches, disasters, markets, unrest, etc.) and issue graded alarms with summaries.

Auto-generated reports and dashboards surface sentiment timelines, key terms, and notable events for decision-makers.

Dedicated workflows answer queries about people and perform picture-based lookups using multimodal LLM APIs.

On-prem architecture with GPU servers, high-speed switching, firewalls, clustered databases, and backup/DR keeps data local and systems highly available.

Kafka/Spark stream processing with Prometheus/Grafana health telemetry ensures real-time ingestion and observability.

Phased plan—Development, Testing, Deployment, Optimization—targets production in ~8 months with ongoing algorithm tuning from user feedback.