Case Study: Agroknow minimizes food-safety risks with the Elastic Stack

Agroknow minimizes food safety risks with help from the Elastic Stack

Agroknow, a Greek data and technology company, built FOODAKAI, a SaaS product that detects hazards in the food supply chain. Their challenge was ingesting and making sense of heterogeneous, multilingual, text-heavy public food-safety data at global scale while keeping the system scalable, monitored, and able to support automated text mining plus time-consuming expert curation.

They adopted the Elastic Stack—Elasticsearch for storing annotated documents and powering NER-based free-text search and aggregations, Logstash/Filebeat for ingest and API tracking, and Kibana/Metricbeat for dashboards and monitoring—to automate annotation, prioritize expert review, and publish searchable incidents. The solution now indexes more than 65 million food-safety data points, speeds prioritized curation based on user queries, delivers actionable analytics to QA/R&D teams and researchers, and was recognized as a 2019 EMEA Elastic Search Awards honoree; next steps include applying ML for anomaly detection.

Agroknow

Mihalis Papakonstantinou

Data Engineer and Team Leader