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ANDREA MILANO AI
Case study

Proprietary AI Management Software

The client faced 48-hour latency in pricing decisions due to fragmented data across spreadsheet silos and manual checks of 14+ different wholesale portals.

Operational HQ: Berlin, GermanyTarget sector: Agri-foodOperating language: German

Operational Context

The client faced 48-hour latency in pricing decisions due to fragmented data across spreadsheet silos and manual checks of 14+ different wholesale portals.

System Architecture

  • Distributed Scraping Network: Implemented Scrapy spiders on a rotating proxy network to asynchronously harvest pricing data every 15 minutes.
  • Task Queue Orchestration: Utilized Celery with Redis as a message broker to handle high-concurrency data normalization tasks.
  • Predictive Analytics Engine: Developed a Pandas/NumPy algorithm cross-referencing real-time weather API data with historical supply logs to forecast shortages.

Verified facts

  • From 48 hours to real time on pricing decisions.
  • 14+ wholesale portals, previously checked by hand, now aggregated automatically.

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