How It Works
BiaSense evaluates one defined workflow using approved data, engineering physics, and operating limits. Customer experts validate the reasoning before Bia presents practical guidance. The operating team decides.
One operating loop
BiaSense is designed to work with customer-approved inputs from compatible existing sensors, systems, and computers. When a workflow requires new functionality, compatible BiaTech edge devices or robotic data-capture systems can add sensing, local computing, mobility, or inspection capability.
BiaSense organizes the evidence around the asset and applies Physics-Informed Machine Learning (PIML) to evaluate behavior against customer-approved limits. For defined workflows, PIML can accelerate repeated model evaluations while retaining selected physical constraints.
The accountable subject-matter expert reviews the evidence, assumptions, limits, and results against the agreed baseline in advisory or shadow mode.
Through Ask Bia, Bia answers questions in plain English, presents the evidence, and offers a practical next action for the operating team to consider. When low latency matters, accelerated edge computing runs selected physics and AI workloads near the operation, adding timely engineering guidance alongside traditional operational and control systems.
A representative six-week path
Choose one rig, asset, site, or workflow with an accountable owner, accessible data, and an agreed baseline. The sequence below shows a representative six-week path toward a measured go/no-go subscription decision; timing depends on scope, data readiness, integration access, and customer review cycles.
Confirm the operating decision, owner, data path, KPI, and desired result.
Connect approved data and establish how the current decision performs.
Map the asset context, engineering physics, operating limits, and decision logic.
Compare the results with baseline evidence and the customer expert’s judgment.
Let operators question the evidence and review the guidance against current operating decisions in shadow mode.
Measure the agreed KPI against the baseline and decide whether to proceed with an annual subscription and wider rollout.
Human control throughout
Next step
Tell us the rig, asset, site, or process; who owns the result; what data is available; and how success is measured. We will help determine whether it fits a focused paid pilot.
The first step is a short qualification call and an initial data-path review.
