Industrial AI.
Smarter performance, grounded in physics, and aligned with business value
Take the EPIQ-M approach
Predict, optimize, and improve operations with AI that understands your processes and physics.
Our unique approach to industrial AI applications combines engineering and manufacturing expertise with digital twin technology, generative AI, deep learning, and machine learning to deliver measurable gains in energy (E), productivity (P), inventory/supply chain and costing (I), quality (Q), and predictive maintenance (M).
It’s what we call EPIQ-M.
Understand the physics behind your data
It’s true that the industrial world runs on data. But not many people understand the physics behind their “dirty” industrial data.
Do you?
Sensors, programmable logic controllers (PLCs), and historians are everywhere, but when conditions shift, materials change, seasonal effects set in, or processes scale, AI models often fail.
Why?
Because AI models that are exclusively data-driven are usually partially physics-blind.
Bridge the data-reality gap
Maya HTT’s EPIQ-M approach and combined expertise in engineering and manufacturing environments bridges the gap between your industrial data and your physical reality to help you gain a competitive advantage.
Here, we understand the laws of thermodynamics, fluid flow, material stress, component fatigue process and control. This is why we can deliver better AI models: we turn partially physics-blind operational data into reliable, physics-aware intelligence leading to actionable decisions.
Move from the unstable predictions of black-box AI to actionable, physics-informed intelligence.
