Evari Harnesses AI for Swift and Precise Data Collection

In the realm of data collection, Evari stands as a beacon of innovation, utilizing cutting-edge artificial intelligence to swiftly and accurately gather critical information. The paramount importance of these two facets cannot be overstated, particularly when our clients often hinge multimillion-dollar decisions on the data we provide. Furthermore, the future energy-saving commitments made by our clients necessitate an expedited process; any delay, be it a single day or an entire month, directly impacts the efficacy of energy conservation measures (ECM), thereby extending the payback period. Evari has harnessed AI in a distinctive manner to fulfill both these crucial requirements.

One instance of this approach can be seen in our collaboration with Ameresco on a project for the City of Philadelphia and the Philadelphia Energy Authority. Evari was entrusted with the task of gathering data on approximately 132,000 streetlights, essential for supporting an intricate process encompassing intergovernmental agreements, design, procurement, and installation.

The solution lay in deploying a custom-built AI system capable of processing field photographs to identify key attributes, such as pole material, wattage, or pole numbers. This technological leap led to a dramatic increase in speed, as the audit process for each light now involved capturing just a few photos. Additionally, the training period for locally hired auditors was significantly reduced, allowing them to work closer to their homes. This translated to more time spent on actual work rather than training and minimized the time spent at each light. The photographs not only simplified the auditing process but also reduced the need for revisits, as we could easily reference the images.

Crucially, this AI-driven methodology substantially enhanced accuracy through independent verification. Evari never relied solely on the AI; every piece of data underwent confirmation either through the initial data provided or by human inspection of the photographs. To allow erroneous data to pass through, two separate decisions would have to fail in the same manner, a highly improbable scenario. In cases where there was a discrepancy, such as wattage data differing between the initial data and the AI output, the project manager made the final decision after reviewing the photos and consulting other available resources.

The benefits of this approach extended beyond speed and accuracy. Evari's utilization of AI enabled the hiring of more local staff, thereby keeping project funds within the Philadelphia community. Moreover, over 800,000 photos amassed during the project proved invaluable in identifying and addressing non-standard circumstances prior to ordering and installation, allowing Ameresco to make well-informed decisions and optimize outcomes.