Computer vision is mature but only the production pipelines are. Most CV projects ship a notebook that scores 95% on a clean test set and degrades to 60% on real cameras in real lighting. NKKTech ships CV systems that hold their accuracy through dataset drift, lighting changes, hardware swaps, and a 24-month operating window. Object detection, OCR, video analytics, defect detection, AR/VR overlays. Deployed on edge (NVIDIA Jetson, Coral), cloud (AWS Rekognition + custom), or hybrid.
End-to-end CV systems, not Kaggle notebooks. Six capabilities every engagement includes.
Curation, annotation pipelines (Label Studio, CVAT, in-house), augmentation strategy, train/val/test splits with realistic distribution match.
YOLOv8 / RT-DETR for detection, DINOv2 + linear probes for classification, SAM2 for segmentation, PaddleOCR / Tesseract for OCR. Picked against your latency and accuracy targets.
Edge: ONNX → TensorRT on Jetson, OpenVINO on Intel, Core ML on iOS. Cloud: SageMaker / Vertex / Bedrock or custom Triton. Pick by data egress cost and latency budget.
When real data is scarce or sensitive (medical imaging, defect detection), we generate synthetic training data with NVIDIA Omniverse or Blender + domain randomization.
Production mAP, false-positive-rate-by-class, drift detection on input distribution, alerting when accuracy drops. CI gates on regression.
Active learning loop: misclassified frames flagged for human review, added to training set, weekly model refresh. Models that get better in production, not worse.
1–2 weeks. Define the task, agree on accuracy and latency targets, capture realistic test set from production conditions.
3–5 weeks. Build annotation pipeline, label initial dataset, train first model, eval against test set.
3–5 weeks. Augmentation iteration, quantization (INT8 / INT4), edge deployment, real-world testing.
2–3 weeks. Production deployment, monitoring stack, continuous-learning pipeline, handoff to your team.
Surface-defect detection on production lines, anomaly detection without labeled defect examples, predictive QA. Edge-deployed on factory cameras.
Shelf-stock detection, customer-flow analytics, planogram compliance, self-checkout loss prevention. Privacy-preserving (no face IDs).
Radiology decision-support, dental X-ray analysis, pathology slide screening. HIPAA-compliant deployment; FDA-pathway-aware engineering.
Crop disease detection from drone footage, livestock counting, growth-stage classification. Edge deployment for low-connectivity environments.
Shipping-label OCR, damage detection, parcel-dimension measurement from camera, hazmat-symbol detection.
PPE compliance (hardhats, vests), site-progress photo analysis, equipment-utilization tracking. Edge cameras with privacy redaction.
Depends on the workload. Detection at <1 frame/sec on small images: CPU + quantization is fine. Real-time video analytics or high-resolution imagery: GPU on edge (Jetson) or in cloud. We'll project the inference cost in the discovery phase.
For a new object class: 200–2,000 labeled examples typically. For fine-tuning a pretrained model: often <500. We use active learning to minimize the labeling budget — initial model trained on small set, hardest examples sent for labeling next.
Yes. We integrate with RTSP, ONVIF, USB cameras, MIPI-CSI on edge devices, AWS Kinesis Video Streams, and most popular CCTV stacks. Bring your hardware; we adapt.
We do not develop facial recognition, license-plate recognition without explicit legitimate-interest documentation, or any surveillance-adjacent CV. For PPE/safety/retail use cases that capture faces incidentally, we add automatic face/license-plate blurring at the edge before frames leave the camera.
USD 50K–120K for a standard 10–16 week project covering dataset, model, edge optimization, and production deployment. Recurring compute cost varies (edge: hardware once + ~$0/month; cloud GPU: $200–$2K/month depending on volume).
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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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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30-minute free discovery call with a senior NKKTech engineer (not a sales rep). We'll review your requirements, scope an engagement, and tell you honestly whether we're the right fit.
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