A three-layer system works alongside the AOI equipment already installed on your line, cuts false-call review labor, and never sends a board image off-premises.
Rule-based AOI systems routinely flag 25 percent or more of inspected boards as defective — the substantial majority false calls. Every new product family restarts the tuning effort, so the review burden compounds with product diversity rather than diminishing with experience.
The platform re-evaluates every AOI flag through three coordinated layers, entirely on the customer's premises. No AOI hardware is replaced, no production process is modified, and no data leaves the building.
A trained vision model runs on the appliance and clears high-confidence cases in under 100 milliseconds — sorted into clear, defect, and uncertain against thresholds quality engineers configure and own.
Runs entirely on the on-site applianceUncertain cases escalate to a locally hosted vision-language model that evaluates each board against IPC-A-610 acceptance criteria and explains its call in plain language — handling new product families on day one.
Locally hosted · on-site GPU nodeGenuinely ambiguous cases route to an asynchronous queue. Every human decision is recorded and feeds retraining datasets that never leave the facility.
Every decision recorded on-site for retrainingADVISORY ONLY — THE PLATFORM NEVER ENTERS THE PLC REAL-TIME CONTROL LOOP.
Nobody is asked to trust a black box. Thresholds are yours to set, evidence is yours to inspect, and model versions are yours to approve — or roll back.
Both verticals share the same structural condition: regulation keeps cloud AI off the production floor, and no purpose-built on-premises alternative exists. One platform serves both — and market entry is sequenced by design, not by accident.
Validated processes, documented change control, and complete traceability are contractual requirements in medical device production — not preferences. The platform's decision audit trail, model version history, escape ledger, and one-click rollback map directly onto that validation culture, with no product modification.
The air-gap-native architecture and clean software bill of materials are designed for precisely this environment. ITAR-controlled technical data cannot be processed on commercial cloud infrastructure — which is why this vertical has a proven category of solution and no compliant vendor.
This product cannot be delivered by a machine learning specialist, a web developer, or a controls engineer working in isolation. It demands simultaneous, production-grade fluency across every discipline the product requires — assembled here in one person, with zero communication loss between disciplines.
For quality engineering leaders at high-mix, compliance-constrained EMS providers — defense and aerospace under ITAR, and medical device under ISO 13485 and FDA design controls — who cannot use cloud AI, the system adds an on-premises, air-gapped layer on top of existing AOI equipment: compliant by design, transparent in every decision, and supported in person, locally, the same day.
The US Southwest is becoming the nation's semiconductor and electronics manufacturing center: TSMC's Phoenix campus is operational and expanding, Intel is investing $20 billion in Chandler, Amkor is constructing a $2 billion advanced packaging facility in Peoria, and more than 35 ecosystem companies have announced Arizona expansions — over $100 billion in combined regional investment. That build-out feeds the regulated EMS supply chain this platform serves, at exactly the moment those shops are competing for scarce skilled labor and facing rising quality-scorecard pressure.
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