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Case Study
A centralized, cloud-based platform developed to enhance field data collection and management throughout the life cycle of energy projects.
The technologies leveraged on this project included:
The Connected FieldForward (CFF) platform bridges the gap between field and office teams to ensure assets are inspected, compliant, and documented. The solution includes a customizable validation engine, real-time data verification, and seamless integration into the GIS system of record. This approach enhances data accuracy and operational efficiency.
The client – a global consulting, engineering, and construction management firm –collaborated with Chetu to improve field data collection for gas, electric, and roadway organizations. Chetu’s developers helped create Connected FieldForward (CFF), an AI-driven web application that verifies and integrates field data into the record system.
Recent research shows that AI adoption in the energy sector is surging. IBM reports that 74% of companies in the energy and utility industry are leveraging AI to optimize their data processes.
AI use in the global energy market is expected to rise to $14 billion by 2029, demonstrating a CAGR of 17.2%, as per Allied Market Research. Considering the current and future of AI, Chetu’s developers knew they had to integrate this powerful technology.
The goal was to develop a centralized, cloud-based system that allows for accurate and efficient field data collection during the entire life cycle of the client’s projects. The platform had to guarantee data integration, validation, and adherence to regulatory standards.
CFF was developed as middleware to manage asset data before migration into the record system. The platform includes a customizable validation engine, a rapid feedback loop for data verification, and seamless GIS integration.
AI-powered algorithms in CFF enable real-time data analysis, predictive analytics, and seamless data integration. These features improve operational efficiency and reduce downtime with enhanced data consistency and accuracy, network validation, and geospatial data.
Geospatial Artificial Intelligence (GeoAI) leverages the synergy of Geographic Information Systems (GIS) and Artificial Intelligence for enhanced real-world insights into business opportunities and operational risks. The CFF platform directly integrates GIS and AI to:
Detect patterns and automate geospatial data analysis.
Provide predictive insights that enhance decision-making.
Boost productivity while minimizing errors and reducing resource waste.
Users collect field data with their devices, which is subsequently verified and incorporated into the record system via the platform. The user-friendly interface with a real-time dashboard offers high-level insights for decision-making.
CFF guarantees seamless data transfer, real-time monitoring, and effective data management. This solution has greatly optimized operations, resulting in increased productivity and better customer satisfaction. With its scalable framework and adaptable integration, CFF establishes the groundwork for future growth and advancements in field data management.
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