This collaboration marks a significant milestone in the solar energy industry, providing a sophisticated solution for accurately predicting and mitigating soiling losses in solar installations.

App Orchid, a leader in making data actionable with AI, and Fracsun, an innovator in solar performance monitoring technology, are proud to announce the latest advancement in their strategic partnership with the launch of an AI-powered soiling loss modeling tool, CLEO AI. This collaboration marks a significant milestone in the solar energy industry, providing a sophisticated solution for accurately predicting and mitigating soiling losses in solar installations.

Utilizing Fracsun's proprietary data and App Orchid's AI platform, CLEO AI is a predictive machine learning model that considers additional factors that specifically impact soiling loss, fine particulate matter (PM2.5 and PM10) concentrations, nitrate and sulfate levels, seasonal trends, and the impacts of wind and precipitation. The result is the ability for users to generate a prediction model for a Typical Meteorological Year (TMY) for a specific location of interest to support their ability to granularly forecast operational and maintenance costs.

"Using the large database that we have built to generate typical soiling loss data for a given site was the goal, App Orchid was crucial in helping us build the tool using their expertise in AI decision engines," said Catlin Mattheis, CEO of Fracsun. "They were able to quickly build out this model which will help refine every production model run and take a lot of risks out of the project development pipeline."

Fracsun is excited to offer the first 5 CLEO AI simulations free for clients who sign up as "early adopters". Users will be able to conduct up to 5 simulations to analyze the effects of soiling on their solar energy system by signing up here:
https://www.fracsun.com/cleo-early-access.

A Partnership for the Future of Clean Energy
This partnership between App Orchid and Fracsun signifies a major step forward in the integration of AI technology within the renewable energy sector. Both companies are committed to driving innovation and sustainability, and CLEO AI represents their shared vision of a cleaner, more efficient energy future.

"We are thrilled to partner with Fracsun to bring CLEO AI to the market. Our combined expertise in AI and solar technology will provide a significant way to drive accurate, cost-efficient planning solutions for the industry, helping to maximize solar energy production and drive the transition to sustainable energy," said Shyam Chodapunedi, CEO of App Orchid.

About App Orchid:
App Orchid is an AI platform company democratizing AI-powered Decision Intelligence. Our flagship product Easy Answers™ enables users to engage with data in plain English and get Advanced Analytics (dashboards and analytics created by AI), Quick Insights from native, patented AI models, and Generative Actions (AI-generated recommendations and applications) to support faster time-to-decision. The App Orchid platform enables a powerful, predictive data fabric that can integrate diverse multi-model data sources as well as embed advanced AI in everyday processes and strategic decisions across the enterprise visit. Interested in learning more about how we can assist you in leveraging your enterprise data to its fullest extent? We'd love to hear from you! Please visit www.apporchid.com.

About Fracsun:
Fracsun has developed the industry's most accurate and reliable soiling loss monitoring station as well as the software needed to support it. This portal allows users to monitor solar assets for soiling loss and drive operational efficiency through actionable data and forecasting. Through its years of experience collecting and analyzing soiling loss data in over 22 countries, the team is now setting its sights on utilizing this data set to aid project development teams and banks to de-risk solar projects at the very earliest stages of conception. Learn more about Fracsun's hardware and software solutions at www.fracsun.com.