Malaysia's semiconductor and E&E sector is expanding faster than its workforce can follow — a 10x gap between engineer supply and industry demand, per SEMICON Southeast Asia 2026, with 12,000–15,000 unfilled technical roles expected by year-end. At the same time, revised Employment Pass salary floors (effective 1 June 2026) have raised the cost of technical headcount. QC and inspection lines are where this pressure shows up first: hard to staff, harder to staff consistently, and the first place a labor shortage becomes a yield problem.
Teh Tarik Digital builds computer vision and workflow automation that takes pressure off that line — not a hardware appliance you bolt on, but systems that integrate into what you already run.
Not a generic "innovation" pitch — this is scoped for plant and ops managers evaluating QC automation against a shrinking technical labor pool. If your evaluation criteria is "does this sound impressive in a demo," a lot of vendors clear that bar. If it's "does this hold up on a real line with real defect variance," the field narrows fast — and that's the bar we're building to.
Four capabilities, each one built to integrate rather than replace — the goal is never a rip-and-replace project. A vendor selling you new hardware is selling you a longer, riskier deployment than one building on cameras and systems you already have running. We picked the harder path on purpose, because it's the one that actually ships on a real production timeline.
YOLO-based real-time visual QC, calibrated to your defect classes — not a generic off-the-shelf model trained on defects that don't match what your line actually produces.
Auto-generated from CV output the moment inspection happens, reducing manual QC logging — the report is a byproduct of the system working, not a separate task someone has to remember at shift-end.
Plugs into existing camera/line hardware and ERP/MES systems; Teh Tarik Digital is the integrator layer, not another vendor portal — the deployment risk lives in the integration, and that's where most of our actual work happens.
Where people-facing monitoring is involved — PPE compliance, safety zones — built with facial recognition disabled by default, written-authorization gated per s.40, so the compliance question is answered before a camera goes live, not after.
A workforce gap doesn't fix itself with better hiring — it needs the line to run without depending on headcount you can't find. The numbers below aren't projections; they're measured on SmartPole OS, a platform already running at scales from a 50-pole deployment to a 2,000-pole network, which is the actual test of whether an architecture holds up or just looks good in a pilot.
Four stages, scoped to your actual defect taxonomy — not a generic deployment checklist. Skipping the assess phase is the single most common way a computer vision project fails: a model trained on someone else's defect classes will miss the ones that actually matter on your line.
Line audit, defect taxonomy, existing camera/hardware inventory — nothing gets proposed until we've actually seen what's running on your floor.
CV model trained on your actual defect classes, not generic datasets — coverage that matches your specific production line, not a one-size-fits-all defect library.
Connects to line hardware and MES/ERP without replacing what's already there — this is where a project actually succeeds or fails, and where we spend most of our time.
Live QC dashboard, auto shift reports, escalation on flagged defects — with the same team that scoped the audit staying on for ongoing support.
The same architectural discipline behind our computer-vision QC builds — documented from a different system.
The state-schema-first discipline behind IRIS v2's supervisor-specialist architecture — the same approach we apply to every build, including QC pipelines. Full case study →
The questions plant and ops managers ask before they'll commit to a line audit. Most of these come down to one underlying concern: will this actually work on my line, or does it just work in a controlled demo environment.