EPIC Mower : Weatherproof Landscaping Field Service

Weatherproof Landscaping Business: Manage weather risk with generative and agentic AI for field service dispatcher. Stay Ahead with Smart Planning: Use AI and proactive planning to minimize delays.

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EPIC Mower : Weatherproof Landscaping Business

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The Importance of Weatherproof Landscaping Field Service

Weatherproofing landscaping field service is crucial due to unpredictable weather, which can cause delays and safety risks for workers. Leveraging advanced technologies like generative and agentic AI can enhance predictive capabilities, ensuring timely service, operational efficiency, and customer satisfaction to ensure smoother operations even in challenging weather scenarios.

What it does

Weatherproofing Landscaping Field Service Operations leverages AI to enhance the efficiency, safety, and reliability of commercial landscaping services. It provides real-time weather forecasts, evaluates weather risks, and optimizes service schedules. The system proactively manages service appointments, dispatches resources, and communicates with customers to ensure seamless operations despite adverse weather conditions.

How we built it

We built the system using Salesforce Field Service (FSL) integrated with the OpenMeteo API for accurate weather forecasting. The AI component evaluates weather risks based on grounded documentation of work type and weather criteria and thresholds. We utilized Salesforce's robust scheduling and resource management capabilities to automate service adjustments and notifications. The project involved developing a single Apex class to handle the dynamic nature of weather conditions and service requirements.

Challenges we ran into

  • Data Integration: Integrating real-time weather data with Salesforce FSL required handling API limitations and ensuring data accuracy.
  • Risk Evaluation: Developing a reliable risk evaluation model that accurately interprets weather data and its impact on various landscaping tasks.
  • User Experience: Ensuring the system is user-friendly for field service managers and technicians, with clear and actionable insights.
  • Scalability: Designing the system to handle a large volume of service appointments and resources across multiple regions.

Accomplishments that we're proud of

  • Successfully integrated real-time weather forecasting with Salesforce FSL using only 1 single Apex class and zero custom object.
  • Developed a robust risk evaluation model that accurately predicts weather-related risks without using predefined information.
  • Automated service scheduling adjustments and resource management based on weather conditions.
  • Enhanced communication with customers and field service teams, improving overall service reliability and satisfaction.

What we learned

  • The importance of accurate and timely data integration for real-time decision-making.
  • How to leverage AI to interpret complex weather data and its impact on field operations.
  • The value of proactive communication and automation in managing field service operations.
  • Best practices for designing scalable and user-friendly AI-driven solutions.

What's next for EPIC Mower

  • Integrate with Slack: Send Slack notification to service resources in the area with unexpected severe weather condition.
  • Expanded Features: Integrate additional data sources, such as soil moisture sensors and historical weather data, for more comprehensive risk assessments.

Built With

  • agentforce
  • datacloud
  • fieldservice
  • open-meteo
  • salesforce
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Updates

posted an update —

This is lightweight solution that can be easily deploy to other weather sensitive industries like Solar, Roof service company. It does not really need Salesforce Field Service package Service Appointment. All the solution needs is a place holder to use geocode location and appointment start and end. And next will be the knowledge of the SME in Agentforce Data Library. Any PDF document with weather threshold parameter defined. Or cancel appointment due to weather, the agent go back the history weather and location to learn and determine the weather threshold.

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posted an update —

I have the project submitted. I didn't expect to move this far. I have never built any integration before this despite having Salesforce Application Architect Certification. I want thanks Salesforce team and DevPost team for given this environment and support to bring my idea to live.

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posted an update —

I have simplified the approach during this hackathon, instead of parse the hourly weather forecast to structure data model in Salesforce/Datacloud to process later. I experiment and successful get AgentForce to interpret the raw data correctly. This save the inefficient in call the API by batch and store the data somewhere. Weather forecast can be changed without notice, it's likely outdated right after the batch is run. I decided to use Agent to call the API only for the scheduled time of the location. It works beautifully.

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posted an update —

I conceived the idea of this project after discussion with a commercial landscaping manager. The traditional way to manage weather depends solely by branch manager, to monitor the weather and make adjustment to the resources the next day. It's not precise as the weather at the site location could be very different from office location. especially the wind condition which can negatively impact the Tree Services.

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