Kulim Hi-Tech Park is doubling to 12,000 acres. Infineon's world's-largest 200mm silicon carbide power fab here — a RM30.1 billion investment — has been in production since 2024. Ferrotec's second Kulim plant broke ground in April 2025. The corridor was scaling before any of this made headlines, and it's still outpacing the workforce meant to run it.
At SEMICON Southeast Asia 2026, the industry named the real bottleneck: a tenfold gap between engineer supply and demand, 12,000–15,000 additional technical roles needed by year-end, current pipelines meeting barely 60–65% of it. On 1 June 2026, Employment Pass salary floors rose across the board — manufacturing technical roles now carry a RM 7,000 floor. Headcount for QC just got harder to find and more expensive to keep. We're deployed on-site in Sungai Petani, inside the corridor, not visiting it — and we build the systems that let plants stop competing for engineers who don't exist.
Kedah doesn't get the AI-conference coverage Penang does — this isn't AI-scene news, it's industrial-investment news, and that's exactly why the labor-gap story above is real and not a talking point.
We focus on the Alor Setar–Sungai Petani stretch of the corridor, closest to Kulim Hi-Tech Park itself — not a generic Malaysia-wide pitch, a build scoped to what's actually running here. Four capabilities, and none of them are speculative: computer vision QC is the direct answer to a line that can't find inspectors; shift and yield reporting exists because someone on your floor is still compiling that by hand at the end of every shift; predictive maintenance and plant workflow automation extend the same discipline to the facility infrastructure and the paperwork around it, not just the production line itself.
Defect detection trained on your actual production line — not a generic pretrained model that misses the specific flaws your line produces. This is the direct answer to a floor that can't hire enough inspectors to hold a consistent standard shift after shift.
Auto-generated from CV output the moment a shift ends, not compiled by hand at 11pm by whoever's still on the floor. The report exists because the inspection already happened — it's a byproduct of the system working, not a separate task someone has to remember to do.
SmartPole OS-style health scoring, where facility infrastructure is in scope — the same 85%+ fault-prediction approach that already runs on live poles, applied to plant equipment so a failure gets flagged days before it becomes downtime.
Approvals, maintenance dispatch, and compliance logging — the paperwork layer around the production line that usually gets deprioritised until an audit forces someone to reconstruct it from memory.
Why Teh Tarik Digital, not a vision-hardware vendor: point-solution vision systems solve one line. We integrate into what you already run — existing cameras, existing MES/ERP, existing reporting — so you're not adding another vendor portal to your stack.
The labor shortage isn't a future risk here — it's already priced into your headcount budget. The question isn't whether automation helps; it's whether the numbers you're being shown are real or a projection dressed up as a result. The left column is what actually changes for the people on the floor. The right column is where those changes have already been measured on live products — SmartPole OS's fault-prediction numbers, IRIS's audit logs — not modeled for a pitch deck.
Four stages, starting with your actual line, not a generic deployment checklist. The order matters: we won't propose a CV model before we've audited your defect taxonomy, and we won't integrate into MES/ERP before the model's already trained on your real defect classes. Skipping ahead is how vendors end up selling you a system that works in the demo and breaks on your actual production floor.
Line audit, defect taxonomy, existing camera and hardware inventory — we're not proposing anything until we've seen what's actually running on your floor.
CV model trained on your actual defect classes, not generic datasets — coverage that matches the flaws your line actually produces, not a stock library of defect types that mostly don't apply.
Connects to line hardware and MES/ERP without replacing what's already there — the deployment risk is in the integration, not the model, so this is where most of the real work happens.
Live QC dashboard, auto shift reports, escalation on flagged defects — and the same team that scoped the project stays on for support, not a handoff to a different account manager.
How we actually build multi-agent systems — the architecture decisions, not the highlight reel.
The supervisor-specialist rebuild behind IRIS v2 — the state-schema-first approach that replaced the original single-agent design. Full case study →
Straight answers for plant managers evaluating whether this actually fits a line that's already running lean.