Manufacturing AI works when it survives the factory: dust, vibration, intermittent connectivity, shift changes, and 5-year hardware refresh cycles. NKKTech ships manufacturing AI that holds up — computer vision QA on edge cameras, predictive maintenance models, OEE dashboards, multilingual support agents for global field teams. Used by mid-market manufacturers in the US, Japan, and Southeast Asia.
Six capability patterns we ship most often. All designed for industrial reliability + edge deployment when needed.
Surface-defect detection, dimensional verification, label-correctness checks. Edge-deployed on NVIDIA Jetson or Intel OpenVINO. Continuous learning on flagged defects.
Sensor-stream models that predict equipment failure 2–14 days out. Reduces unplanned downtime 30–50%. Integrates with CMMS (Maximo, Fiix, UpKeep).
Real-time OEE (Overall Equipment Effectiveness) calculation, bottleneck detection, shift-comparison dashboards. Production-data warehouse + dbt models + Grafana / PowerBI surface.
Real-time inventory + supplier-lead-time models, disruption alerting, scenario planning. Integrates with SAP, Oracle ERP, NetSuite.
Multilingual AI agents that answer equipment-manual questions, troubleshoot from a photo + description, schedule technician dispatch. EN/JA/ZH/VI/KO out of the box.
Quality-control report drafting, certificate-of-analysis extraction, customs-form OCR, multilingual technical-document translation.
1–2 weeks. On-site or video walkthrough; identify the production constraints (hardware, network, shifts) that will shape the AI architecture.
6–10 weeks. Narrow pilot scope; ship to one production line; measure against baseline; iterate.
8–14 weeks. Roll out to additional lines or sites; harden edge deployment; train operator teams.
Monthly retainer for model refresh, new defect-class addition, continuous-learning loop.
Weld-quality CV inspection, paint-defect detection, torque-data anomaly detection, supplier-quality dashboards.
PCB defect detection, wafer-map anomaly classification, yield analysis, equipment-fault prediction.
Foreign-object detection, label-correctness verification, package-integrity inspection, traceability automation.
Batch-record automation, vial-fill verification, GMP-compliant deviation tracking, multilingual SOP search.
Fabric-defect detection, cut-and-sew quality control, color-matching automation, factory-floor productivity tracking.
Predictive maintenance on hydraulics, vibration-based fault detection, operator-behavior analytics, remote diagnostics.
Yes — three patterns work for limited-data cases. (1) Anomaly detection (no labels needed, just "normal" examples). (2) Synthetic data generation using NVIDIA Omniverse or domain randomization in Blender. (3) Active learning: start with a tiny labeled set, deploy the imperfect model, accumulate hard cases for labeling, weekly model refresh. Most projects reach acceptable accuracy in 6–12 weeks even with limited initial data.
Inference runs entirely on the edge device; only aggregated results (defect counts, alerts) go upstream. Model updates can be air-gapped via USB if needed. We've shipped systems for sites with no internet that resync nightly via cellular at the operator's break.
Yes — OPC UA, MQTT, MTConnect, Modbus are first-class. We have integrations with SAP, Oracle ERP, NetSuite at the warehouse layer, and with major MES platforms (Wonderware, Ignition, Tulip) at the shop-floor layer.
We have native-Japanese senior engineers and a 3+ year track record with Japanese manufacturers (PrismLab AI/AR work in particular). Contracts under Japan-law are available via partner counsel; daily work happens in Japanese with English-fluent senior consultants as needed.
USD 60K–150K for a pilot on one line / one machine. Multi-line or multi-site scaled deployments: USD 200K–800K over 6–12 months. Recurring costs are mostly hardware-amortized (edge devices last 3–5 years); cloud LLM cost is typically $300–$2K/month at mid-market scale.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Tony's team understood our legacy PHP system faster than our internal team. Zero downtime migration, exactly as promised. The bilingual PM made communication seamless.
We went from 15 hours/week of manual prospecting to fully automated lead gen in 8 weeks. ROI in 60 days as Tony promised.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
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