About CrossnoKaye

CrossnoKaye has developed the ATLAS platform, an industrial operating system that utilizes physics-based machine learning to optimize energy consumption and operational efficiency in heavy industrial refrigeration systems. By transforming traditional facilities into smart, responsive assets, ATLAS enables up to 48% energy savings while reducing carbon emissions in the food and beverage sector.

```xml <problem> Heavy industrial refrigeration systems in the food and beverage sector often suffer from inefficient energy consumption due to outdated control systems and a lack of real-time optimization. Traditional facilities struggle to adapt to changing conditions, leading to increased energy costs and higher carbon emissions. Furthermore, reliance on manual adjustments and a shrinking talent pool exacerbates these inefficiencies. </problem> <solution> CrossnoKaye's ATLAS platform is an industrial operating system that leverages physics-based machine learning to optimize energy consumption and operational efficiency in heavy industrial refrigeration systems. ATLAS transforms traditional facilities into smart, responsive assets by connecting them to an integrated suite of cloud applications. The platform uses real-time data and external factors like weather forecasts and utility rates to model power consumption and automatically implement ideal energy strategies. This approach enables facilities to reduce energy consumption by up to 48% while also tracking and reducing Scope 2 and Scope 3 carbon emissions. </solution> <features> - Enterprise cloud platform with integrated suite of cloud applications - Physics-based machine learning for real-time optimization of refrigeration systems - Integration with external data sources (weather, utility rates, etc.) for predictive control - Automated energy strategy implementation based on facility conditions - Centralized industrial operating system extendable across entire organization - Unified control narratives, process alarms, and ammonia response logic - Standardized safety and compliance protocols for every site - Real-time responses to physical onsite and external conditions - Cloud-based infrastructure for enterprise governance and data visibility - Continuous updates and improvements deployed across all sites - SOC2-compliant control system with SSO, 2-Factor Authentication, live security patches, granular access controls, and data encryption </features> <target_audience> The primary target audience includes companies in the food and beverage industry with heavy industrial refrigeration systems, particularly cold storage and food processing facilities, seeking to reduce energy consumption, lower carbon emissions, and improve operational efficiency. </target_audience> ```

What does CrossnoKaye do?

Where is CrossnoKaye located?

CrossnoKaye is based in East New York, United States.

When was CrossnoKaye founded?

CrossnoKaye was founded in 2017.

Location

East New York, United States

Founded

2017

Employees

80 employees

Major Investors

Cleantech Open

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CrossnoKaye

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Executive Summary

crossnokaye.com 1K+

Crunchbase

Founded 2017 – East New York, United States

Funding

Major Investors

Cleantech Open

Team (75+)

No team information available.

Company Description

Problem

Heavy industrial refrigeration systems in the food and beverage sector often suffer from inefficient energy consumption due to outdated control systems and a lack of real-time optimization. Traditional facilities struggle to adapt to changing conditions, leading to increased energy costs and higher carbon emissions. Furthermore, reliance on manual adjustments and a shrinking talent pool exacerbates these inefficiencies.

Solution

CrossnoKaye's ATLAS platform is an industrial operating system that leverages physics-based machine learning to optimize energy consumption and operational efficiency in heavy industrial refrigeration systems. ATLAS transforms traditional facilities into smart, responsive assets by connecting them to an integrated suite of cloud applications. The platform uses real-time data and external factors like weather forecasts and utility rates to model power consumption and automatically implement ideal energy strategies. This approach enables facilities to reduce energy consumption by up to 48% while also tracking and reducing Scope 2 and Scope 3 carbon emissions.

Features

Enterprise cloud platform with integrated suite of cloud applications

Physics-based machine learning for real-time optimization of refrigeration systems

Integration with external data sources (weather, utility rates, etc.) for predictive control

Automated energy strategy implementation based on facility conditions

Centralized industrial operating system extendable across entire organization

Unified control narratives, process alarms, and ammonia response logic

Standardized safety and compliance protocols for every site

Real-time responses to physical onsite and external conditions

Cloud-based infrastructure for enterprise governance and data visibility

Continuous updates and improvements deployed across all sites

SOC2-compliant control system with SSO, 2-Factor Authentication, live security patches, granular access controls, and data encryption

Target Audience

The primary target audience includes companies in the food and beverage industry with heavy industrial refrigeration systems, particularly cold storage and food processing facilities, seeking to reduce energy consumption, lower carbon emissions, and improve operational efficiency.