What challenges did Cropin Technology Solutions Pvt. Ltd face before AI implementation?

The previous cloud environment could not handle the scale and complexity of Cropin's time-series agricultural data. Query performance degraded and analytics reports that customers needed in hours took days to complete. The company lacked native geospatial and climate intelligence layers and needed faster, trustworthy hyper-local advisory workflows.

What was the impact of the AI solution?

Overall infrastructure costs fell by approximately 25%. Analytics latency improved by 90%, turning multi-day reporting cycles into real-time insights delivered in seconds. Model training cycles shortened by 30%, moving from weeks to days. A lean team of fewer than 40 data scientists and engineers achieved a 3 to 4x increase in output. The platform now supports over 100 enterprise clients and massive government digitization projects without proportionally increasing headcount.

Source

GCPEvidence: Medium50/100

Cropin scales agricultural intelligence with BigQuery and Gemini Enterprise Agent Platform

Use case typeAgronomic advisoryUpdated Jan 1, 2024

Cropin Technology Solutions processes agricultural intelligence across more than one billion acres in 102 countries, using a large proprietary ground-truth dataset and Google Cloud services to deliver hyper-local crop insights. The company migrated core data to BigQuery, launched OrbitAI on the Gemini Enterprise Agent Platform, and uses Google Earth Engine, GKE, and Cloud SQL to support verified recommendations and global reliability.

Organization: Cropin Technology Solutions Pvt. Ltd
Industry: Agriculture
Location: India
Published: January 2024

Reported outcomes

+90%

analytics latencyTime & speed

−25%infrastructure costs−30%model training cycles3-4%team output

Strategic outcomes

Speed & agilityDelivered real-time crop intelligence instead of multi-day reportsCustomer experience & trustImproved trust through verified hyper-local adviceScale & capacitySupported global agricultural operations and expansion

Catalog median for time & speed deployments: +60% across 143 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Infrastructure costs: 25% decrease

Google Cloud Customer StoryJan 1, 2024Customer storyExplicit claimMedium evidence strength

reducing overall infrastructure costs by approximately 25%

Normalized claim

Analytics latency: 90% increase

Google Cloud Customer StoryJan 1, 2024Customer storyExplicit claimMedium evidence strength

Analytics latency improved by 90%

Normalized claim

Model training cycles: 30% decrease

Google Cloud Customer StoryJan 1, 2024Customer storyExplicit claimMedium evidence strength

Gemini Enterprise Agent Platform shortened model training cycles by 30%

Normalized claim

Team output: 3-4% increase

Google Cloud Customer StoryJan 1, 2024Customer storyInferred claimMedium evidence strength

achieved a 3 to 4x increase in output

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Cropin Technology Solutions Pvt. Ltd
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Story

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Agronomic advisory
2Data platform modernization
3Developer productivity

The previous cloud environment could not handle the scale and complexity of Cropin's time-series agricultural data.
Query performance degraded and analytics reports that customers needed in hours took days to complete.
The company lacked native geospatial and climate intelligence layers and needed faster, trustworthy hyper-local advisory workflows.

Cropin centralized more than 670 million agricultural observations in a real-time BigQuery warehouse.
It launched OrbitAI on Gemini Enterprise Agent Platform, using Gemini models to power an agentic workforce for agricultural intelligence.
Google Earth Engine and WeatherNext process satellite and climate data natively, while GKE and Cloud SQL keep OrbitAI available globally.
The platform closes the loop by sending field-coordinate mitigation guidance through Google Maps integrations and continuously retraining from farmer feedback.
Cropin constrains AI to verified historical ground-truth data to reduce hallucinations and increase trust.

Technologies

BigQuery Gemini Enterprise Agent Platform Gemini Google Earth Engine Google Kubernetes Engine Cloud SQL Google Maps WeatherNext

Overall infrastructure costs fell by approximately 25%.
Analytics latency improved by 90%, turning multi-day reporting cycles into real-time insights delivered in seconds.
Model training cycles shortened by 30%, moving from weeks to days.
A lean team of fewer than 40 data scientists and engineers achieved a 3 to 4x increase in output.
The platform now supports over 100 enterprise clients and massive government digitization projects without proportionally increasing headcount.

Architecture

Google Cloud architecture centered on BigQuery, Gemini Enterprise Agent Platform, Google Earth Engine, WeatherNext, GKE, and Cloud SQL, with Google Maps integrations for field-level action and feedback loops.

Sources & evidence1

Evidence: Medium50/100Evidence strength