Ravit Cohen-Segev
Horizon Scanning Lab - Data Scientist
Haifa, Israel
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I am a Data Scientist with 5 years of industry experience. Although I hold a PhD in Physiology from the Technion, my professional career has focused on applying the rigorous analytical methods of a research scientist to complex, large-scale data challenges. I pride myself on my ability to bridge the gap between academic inquiry and delivering high-impact business insights.
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Building SCAR: Low-Cost, High-Throughput Supply Chain Ingestion and Agentic RAG
In an era of expanding trade restrictions and geopolitical disruption, timely commodity and trade indicators are essential for understanding emerging supply-chain exposure.
SCAR is an operational supply-chain early-warning radar that combines deterministic KPI alerting with agentic RAG. A serverless AWS pipeline ingests and normalizes heterogeneous trade, energy, agriculture, price, and transport data from analyst-approved sources. These sources differ in schema, entity granularity, publication frequency, time lag, revision behavior, and geographic/product coverage from near-real-time market or news signals to monthly, quarterly, and annual official statistics.
The deterministic engine aligns these inputs into supply-chain health indicators that surface anomalous conditions. An Amazon Bedrock–based agent router then directs questions to analyst-approved structured tables in Amazon Athena or curated intelligence sources, returning responses with traceable evidence for analyst review. This case study shows how governed retrieval, temporal data normalization, and agentic tool routing can turn fragmented multi-source data into trustworthy, source-grounded supply-chain intelligence without relying on unconstrained model knowledge.
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