AI Prediction Engine
Physics-aware models forecast thermal hotspots and failure risk before a single layer is printed.
From Guesswork to Predictive Intelligence in Manufacturing.
OptiFab uses AI-driven solutions to eliminate repetitive tasks, reduce human error and connect design, process and execution — so critical decisions are made with full context, not assumptions.
Design, process and execution live in separate silos. By the time a defect appears, the context that caused it is already gone. The result: scrapped parts, wasted material and slow, expensive iteration.
OptiScan connects design → process → execution into a single predictive workflow. Critical decisions are made with full context, so problems are caught before they cost you a part.
AI analyzes CAD geometry, material properties and process parameters to predict thermal hotspots and high-risk areas before production starts — fast, design-level thermal simulation.
Connects to machine sensors and controllers, streaming thermal, acoustic and pressure data in real time — adaptively adjusting parameters as the build runs.
Aggregates data into quality reports and digital traces for aerospace and medical certification and compliance — full lifecycle accountability.
Physics-aware models forecast thermal hotspots and failure risk before a single layer is printed.
Streams thermal, acoustic and pressure signals straight from machine controllers as the build happens.
Closed-loop adjustments tune parameters mid-process to keep every part inside spec.
A growing library of validated material and process data that gets smarter with every build.
Insights compound across teams and sites, so a lesson learned once is never re-learned the hard way.
A single view of quality, throughput and risk — with proactive alerts before issues escalate.
Techstars ’25
If I tried to develop something like OptiFab’s solution in-house, it would cost me 10 times more.
— OptiFab customer
Talk to our team about a free trial of OptiScan, or send us a note — we’ll get back to you within one business day.