What we do:
EdgeSite Analytics is a data intelligence company focused on bringing real-time, interpretable analytics to the edge of energy operations. We develop custom solutions that help operators and equipment manufacturers make faster, safer, and more informed decisions without relying on heavy cloud-only systems. Our team combines deep expertise in physics, machine learning, and industrial systems to create lightweight, domain-specific models that adapt to each customer’s environment. By partnering directly with operators and OEMs, we bridge the gap between advanced data science and practical field performance, delivering measurable results today while building the foundation for tomorrow’s connected and intelligent infrastructure.
How we do it:
EdgeSite Analytics delivers real-time intelligence where it matters most, combining structured compression, physics-informed models, and adaptive AI to make edge analytics fast, reliable, and explainable.
Our Approach
At EdgeSite Analytics, we design analytics that work where the data lives, at the edge. Our methods are built to deliver high-fidelity insight without the burden of heavy cloud infrastructure or excessive computing requirements. By tailoring our algorithms to the structure of each system, we make advanced analytics practical, interpretable, and efficient for real-world energy applications.
Structured Compression
Our technology uses structured compression to process complex, high-dimensional data with a fraction of the computational load required by traditional AI models. This approach captures the essential relationships within the data while minimizing redundancy, allowing our algorithms to run on standard CPUs instead of costly, power-intensive hardware. The result is faster analysis, lower latency, and reduced energy consumption, which are critical advantages for field-deployed systems.
Physics-Informed Intelligence
We combine data-driven AI with analytical and physics-informed models to understand systems that are inherently multichannel and highly correlated. By embedding physical principles into machine learning, our models learn faster, generalize better, and remain explainable to engineers and operators. This hybrid approach makes it possible to detect anomalies, forecast performance, and optimize operations even in complex industrial environments where variables interact dynamically over time.
Edge-First, Cloud-Ready
Our systems are built to perform at the edge, close to where data is generated, which reduces latency and keeps critical operations running even without constant cloud connectivity. When broader coordination or fleet-wide learning is needed, our architecture connects seamlessly to secure cloud environments. This hybrid design provides the best of both worlds: real-time, local intelligence with the scalability, reliability, and collaboration benefits of the cloud.