Enterprise Knowledge Search · Real-time Analytics
Cropin needed to centralize very large agricultural data streams, reduce infrastructure cost, and speed both analytics delivery and model-training cycles as it expanded OrbitAI across hundreds of crops and global farming workflows.
Reported impact
Cropin sped up analytics delivery, lowered infrastructure and training cost, and turned a large agricultural data estate into an agentic decision-support platform for crop intelligence.
Cropin rebuilt its agricultural intelligence stack on Google Cloud to connect planetary-scale data, real-time analytics, and agentic AI. BigQuery became the real-time warehouse for more than 670 million agricultural observations, while OrbitAI was launched on Google Cloud using Gemini models and Gemini Enterprise Agent Platform to support an agentic workforce. Behind the scenes, Google Earth Engine and WeatherNext feed environmental and satellite context into the platform, while Google Kubernetes Engine and Cloud SQL keep the service available globally. Apigee helps manage integrations at the API layer. This unified architecture cut infrastructure cost, reduced BI turnaround from days to seconds, and shortened model-training cycles, letting a relatively small technical team deliver much more output without having to manage multiple disjointed data and ML environments.
Agricultural observations, satellite signals, and weather context are centralized in BigQuery, where OrbitAI and Gemini-based agents reason over the data. Apigee governs integrations, and GKE plus Cloud SQL keep the service operational worldwide.
Gemini Enterprise Agent Platform cut model training time by 30%
Reduced BI analytics delivery turnaround by over 90% using BigQuery through OrbitAI
centralizing over 670 million agricultural observations into a real-time data warehouse.
fewer than 40 data scientists and engineers achieved a 3 to 4x increase in output.
reducing overall infrastructure costs by approximately 25%
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The Organization JSON-LD on the profile lists "numberOfEmployees":{"@type":"QuantitativeValue","value":160}, which maps to SMB under the 0-249 rule.
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