It's Monday morning. The ERP shows a new High-Mix job scheduled for Line 3. The pick-and-place files are loaded, the reflow profile is set, and the printer is ready.
But the line isn't moving.
Why? Because your AOI machine is still being "taught."
Right now, your most senior process engineer is hunched over the terminal, manually drawing inspection windows and tweaking RGB thresholds, trying to teach the machine the difference between a capacitor and a shadow.
This is what we call the NPI Paradox. As manufacturing shifts toward High-Mix, Low-Volume (HMLV) models, the time spent programming inspection equipment often exceeds the time spent actually running the batch.
When it takes 3–5 hours to program a meaningful inspection for a run that only lasts four hours, your equipment utilization isn't just low; it's leaking profit.
At APEX EXPO 2026, DaoAI is demonstrating how to change this equation entirely.
The Problem with "Golden Boards" in a High-Mix World
Traditional AOI systems rely on a "Golden Board"—a single, perfect PCBA—as the absolute standard for pixel-by-pixel comparison. In theory, this works. In practice, especially during NPI, it's a trap.
Supply chains are messy. If your component supplier changes and the 0402 resistor body color shifts from black to dark blue, rigid pattern matching screams "Wrong Part". To fix this, engineers widen the acceptance thresholds, which inadvertently opens the door for real defects to escape.
The struggle is particularly brutal with polarity detection. On challenging surfaces like black components on black PCBs, traditional rule-based algorithms often struggle to reach 60–70% accuracy. This forces operators to manually double-check thousands of parts, defeating the purpose of automation.
Moving From "Rules" to "Reasoning"
DaoAI takes a different approach. Instead of rigid pixel matching, we use Visual AI Models trained on over 100 million real production samples.
Think of it this way: The AI doesn't just memorize what one specific resistor looks like; it understands the concept of a resistor. It identifies the component body, the leads, the polarity marks, and the text automatically.
This shift from "rules" to "visual reasoning" allows the system to:
- Ignore harmless variations: It tolerates color shifts and supplier differences that trip up traditional machines.
- Nail the hard stuff: Polarity detection accuracy jumps from roughly 70% to over 98%, even on low-contrast black-on-black assemblies.
- Eliminate the library: You don’t need to maintain a massive central component library. The AI handles it.
The 5-Minute Setup Challenge
The most tangible benefit, however, is speed.
Because the AI auto-frames components and generates its own defect thresholds, the programming process collapses from hours to minutes. We are talking about going from a 3–5 hour setup down to 5 minutes.
You don't need CAD data (though you can use it), and you don't need a master programmer.
The Business Case
Let's look at the numbers. For a typical EMS provider handling 10 NPIs a week, the labor cost alone for programming is substantial. But the real cost is opportunity—the downtime where the line produces nothing.
By switching to AI-driven inspection, manufacturers can cut annual labor costs by an estimated $28,000–$42,000 per line , while reducing false alarms by 1/10th compared to traditional algorithms.
See It to Believe It
We know "AI" is a buzzword that gets thrown around a lot. That’s why we invite you to test it.
Bring your most complex High-Mix NPI board to Booth #1234 at APEX EXPO. Bring the one with the black-on-black components. Hand it to our team.
If we can't get it ready for inspection in 5 minutes, dinner is on us.
Stop Troubleshooting. Start Producing.
Visit DaoAI at APEX EXPO 2026 | Booth #2747