Rabab Benazizi

Designed an autonomous IoT anti-theft surveillance system protecting copper cables along a 5 km phosphate conveyor belt, integrating a Raspberry Pi and an IP camera with automatic vehicle ID recognition via OpenCV and real-time alert transmission via long-range LoRa with solar power supply, enabling 24/7 autonomous monitoring and eliminating the need for permanent human surveillance. Deployed a RAG architecture (LLMs + Vector DB) with an automated extraction pipeline from technical PDF documents feeding a vectorial knowledge base queryable by a multimodal AI assistant (text & image), embedding a Smart Solution Generator that converts field expertise into actionable recommendations, drastically reducing manual diagnostic workload for technicians.

Architected a multi-agent AI system (NOVEC Assistant, Opportunity Finder, Smart Web Agent, DocuLeader) with intelligent crawling via Crawl4AI, dynamic scraping, and NLP-driven semantic analysis to automate end-to-end intelligence gathering and replace manual market research with a real-time AI pipeline capable of surfacing high-value business opportunities autonomously, leveraging RAG for contextual retrieval and MCP for seamless agent coordination. Implemented a full RAG pipeline (GPT-4 · FastAPI · n8n · Supabase Vector DB · Railway) transforming unstructured web data into a real-time queryable knowledge base, integrating MCP to orchestrate multi-agent workflows and automating generation of strategic decision deliverables, reducing time-to-insight from days to seconds.

Designed a decentralized multi-agent architecture combining IoT sensor networks and Edge AI (Jetson Nano) to deliver real-time agronomic recommendations via a mobile application, bridging the gap between raw sensor data and actionable field decisions for smallholder farmers, earning 1st place nationally at AI2SD 2025.

Built automated Python + SQL pipelines to extract, clean, and aggregate 1M+ records from the 2014 national census, delivering reliable analysis-ready datasets that eliminated manual processing bottlenecks and directly fed into national planning reports. Designed interactive statistical dashboards using Apache Superset to enable decision-makers to visualize complex census data intuitively, supporting evidence-based national planning decisions.

Developed a dynamic Power BI dashboard with KPI tracking, multi-dimensional filters (site, product, supplier), and spend analysis, enabling procurement managers to identify cost optimization opportunities across industrial sites and improving budget visibility.