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How Lumo Uses Generative AI to Automate Irrigation Decisions

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About Lumo


Lumo is an agricultural technology company focused on optimizing irrigation through intelligent automation. Founded to solve the inefficiencies of traditional irrigation systems, Lumo empowers farmers to reduce water waste, improve yield outcomes, and simplify planning with real-time data. Lumo’s sustainable mission aligns with responsible water usage and data-driven farming practices.
Starting with irrigation, Lumo envisions building a full-spectrum smart farm intelligence platform. Their roadmap includes predictive irrigation planning, weather-aware automation, mobile interfaces, and even image-based anomaly detection—all powered by cutting-edge AI infrastructure.


Challenge


Lumo wanted to provide farmers with intuitive, real-time access to critical irrigation data and system status. The project focused on a business-led transformation of irrigation management using generative AI. The inability to dynamically adapt irrigation strategies to real-time weather or flow anomalies resulted in suboptimal water usage and delayed responses, often requiring manual support intervention. With the addition of an AI chatbot and real-time analytics, modularity and cloud-native scalability became critical. The company turned to kloia for expertise in decomposing the application into modern microservices and integrating AWS-native solutions.

The growing demand for real-time analytics, user-friendly interfaces, and intelligent automation revealed two key needs:
Modernize the architecture to be modular, observable, and scalable
Introduce a natural language interface powered by generative AI for seamless farmer interaction

Client: Lumo
Project type: Generative AI
Website: www.lumo.ag

Solution

A generative AI chatbot powered by Amazon Bedrock was implemented to handle natural language queries like "Show me my last 4 irrigations." Built with a React frontend hosted on AWS Amplify and backed by CloudWatch monitoring, the system incorporated an SQL Agent to enhance database interaction, boosting both operational efficiency and insight-driven irrigation planning. 

Technologies Used


AWS Bedrock: Provided the core generative AI models and capabilities for the chatbot's natural language understanding and response generation.

AWS Cloudfront: Used for hosting the static web assets of the React-based chatbot frontend.

AWS CloudWatch: Implemented for monitoring application performance, logging, and system health.

AWS Lambda: Hosting the SQL Agent + connection to the databases.

AWS Relational Databases: For the customer databases

 

 

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Results

Prior to the chatbot integration, farmers often relied on support teams to access irrigation logs, clarify anomalies in water usage, or confirm upcoming schedules. The generative AI assistant now handles these routine queries in real time, reducing the need for manual intervention and empowering users with self-service tools.

Farmers can now retrieve irrigation insights within seconds using natural language queries, eliminating the need to navigate complex dashboards or rely on manual support channels.

Sample user interaction:

“Show me my last 4 irrigations.” → [Response: Tabular data + AI-generated summary + variance detection]

- Farmers can now access detailed irrigation data in real time using natural language. 

- An advanced SQL Agent was integrated to allow the chatbot to communicate seamlessly with Lumo's databases for robust reporting and streamlined farmer data management.

- Reduced support dependency by over 50% thanks to self-service chatbot

Some of our results:

  • 50%
    Reduced support dependency 
  • 99%
    Real-Time Data Access

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