Bert Labs develops IoT devices and AI-driven operational digital twins to enhance energy efficiency and production quality in various industries. Their technology enables real-time data capture and analysis, resulting in significant reductions in power consumption and operational costs.
Funding
$4.2M 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
Many industrial facilities and commercial buildings lack comprehensive, real-time insights into their energy consumption and operational efficiency, leading to wasted resources and suboptimal performance. Existing monitoring systems often provide fragmented data, making it difficult to identify areas for improvement and implement effective optimization strategies. This results in increased operational costs, higher energy bills, and a larger carbon footprint.
Solution
Bert Labs offers an AI-powered IoT platform that provides real-time monitoring, analysis, and optimization of energy consumption and production processes. The platform utilizes a network of proprietary IoT devices to capture granular data related to machine health, environmental conditions, and material information. This data is then transmitted through a wireless sensor network to a big data lake and distributed computing infrastructure, where AI algorithms generate operational digital twins of manufacturing process lines, process equipment, and utility equipment. These digital twins provide predictive insights and enable reinforcement learning agents to optimize energy efficiency, improve production quality, and enhance asset integrity.
Target Audience
The primary target audience includes manufacturing plants, production facilities, R&D facilities, and commercial buildings across industries such as FMCG, pharmaceuticals, cement, and automotive.
Features
- Patented IoT devices for real-time sensing and control of machine parameters, environmental conditions, and material information
- Proprietary wireless sensor network for secure and reliable data transmission
- Big data lake and distributed computing infrastructure for data storage, processing, and AI-powered analytics
- Operational digital twins that provide a 99.99% accurate representation of the current and predicted state of manufacturing processes
- Reinforcement learning agents that optimize energy efficiency by up to 30% and improve production efficiency by up to 3%
- Integration with existing infrastructure through versatile I/O points and field instrument actuation
- Cloud-based platform accessible through web and mobile interfaces