BE AI Energy provides AI-powered platforms for the energy and industrial sectors, automating critical processes and improving decision-making. Their solutions, including CorrosionAI, InverterAI, and AI Portal, focus on predictive maintenance, corrosion prediction, and enterprise AI deployment. The company reports efficiency improvements of 20-40% and a 20% reduction in operational risk across more than 20 projects.
Funding
Funding not disclosed
Founders
Product
Problem
Energy and industrial organizations rely on manual processes, reactive management, and limited predictive insight, leading to operational inefficiency, high costs, and late detection of failures. These traditional methods lack the capability to anticipate future risks, resulting in unplanned downtime and suboptimal quality standards.
Solution
BE AI Energy provides customized AI platforms that automate critical processes, accelerate decision-making, and minimize operational risks for the energy and industrial sectors. Their solutions include CorrosionAI for predicting corrosion rates, InverterAI for predictive maintenance of power electronics, and AI Portal for enterprise AI deployment. These platforms use physics-informed neural networks and degradation models to deliver actionable insights, such as remaining useful life and risk-based inspection plans, rather than simple anomaly alerts. The company emphasizes measurable outcomes, reporting 20-40% efficiency improvements and a 20% reduction in operational risk across more than 20 projects.
Target Audience
Primary customers are energy and industrial organizations, including integrity departments, maintenance planners, and enterprise AI teams, that need predictive capabilities for corrosion management, power electronics maintenance, and secure AI deployment.
Features
- CorrosionAI uses physics-informed neural networks (PINNs) that incorporate electron transfer kinetics and thermodynamic driving forces to predict corrosion rates and remaining wall thickness at a granular level (e.g., weld, elbow, low point)
- InverterAI models individual components (power modules, DC link capacitors, cooling fans, contactors) using degradation mechanisms like Coffin-Manson for thermal cycling and Arrhenius for electrolyte ageing to produce remaining useful life estimates with uncertainty intervals
- AI Portal provides enterprise AI deployment with RAG-based retrieval quality measured separately from generation, identity and permissions enforcement during retrieval, and full portability of documents, indexes, prompts, and evaluation sets
- All platforms include audit trails for single predictions, enabling reconstruction of inputs, model version, and mechanism for regulatory or insurance review
- Solutions integrate with existing data sources (historian, SCADA, laboratory systems) and output directly to work orders or risk-based inspection plans