The startup operates a data analytics platform that utilizes machine learning and AI techniques to optimize enterprise operations and enhance decision-making. By providing actionable insights, the platform enables businesses to achieve significant productivity improvements and cost efficiencies.
Funding
$3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Building managers and asset owners face challenges in achieving sustainability goals, optimizing resource utilization, and enhancing operational efficiency while minimizing costs and reducing their carbon footprint. Traditional methods often lack the real-time insights and automated controls needed to address these complex challenges effectively.
Solution
Azendian Solutions offers the EVOLV AI suite, a set of AI-driven solutions designed to optimize building operations, reduce energy consumption, and promote sustainability in the built environment. The platform leverages machine learning, data science, and cloud computing to provide real-time data analysis and automated control of building systems. By integrating with existing Operations Technology (OT) infrastructure, EVOLV AI enables building managers to make data-informed decisions, improve process and workforce productivity, and achieve significant cost savings. The solution facilitates proactive maintenance, reduces false alarms, and supports the transfer of knowledge between generations of engineers, ensuring operational continuity and long-term sustainability.
Target Audience
The primary target audience includes building managers, asset owners, and facility management companies seeking to enhance sustainability, reduce operational costs, and improve the efficiency of their built environment assets.
Features
- Real-time data extraction and conversion from diverse equipment brands and types into a standardized Haystack format
- AI-driven HVAC energy optimization that reduces energy use by automatically adjusting set points based on real-time data and simulations
- Fault detection and predictive maintenance algorithms that identify anomalies and predict equipment failures, minimizing downtime and repair costs
- Computerized Maintenance Management System (CMMS) that centralizes maintenance information and streamlines maintenance activities
- Unified Platform that enables automated control and operations with or without human intervention
- Mathematical digital twins that accurately simulate component relationships and optimize energy usage
- Cloud-enabled architecture that supports integration of building data and operations across multiple geographic locations
- Integration with Green Mark certification by the Building and Construction Authority, contributing up to 11 points without major retrofitting