Discover & Scope
Align on problems, data reality, and success metrics. Opportunity brief, KPI model, phased roadmap, effort/cost ranges.
Computer Vision &
Edge AI
Real-time inspection, safety and detection systems built for the factory floor — running on edge GPUs, synced to the conveyor, calibrated by our engineers on site.
AI that works where the work actually happens
Computer vision in a lab is easy. Computer vision on a factory line with shifting glare, dust on the lens, vibrating cameras and a 120-millisecond frame budget is an engineering problem. We build edge vision systems designed from the camera bracket up for real industrial conditions. We select and position the optics, calibrate the lighting, write the capture and inferencing pipeline in C++ or Rust over TensorRT, and deploy on Jetson or edge-PC hardware wired directly into your PLC over Industrial Ethernet.
The result is sub-50ms deterministic latency, zero cloud dependency for the core loop, and fail-safe hardware triggering that stops the line when a defect is found. When the system is uncertain, it captures the raw frame and context, queues it for asynchronous cloud re-training, and alerts the operator without stopping the line — so the model gets smarter every week without ever taking a line down.
What We Do?
We engineer end-to-end vision and edge systems: defect detection, dimensioning, safety zone monitoring, OCR on challenging surfaces, and worker-assist overlays. We handle the full stack — optical design, lighting specification, dataset annotation, model training, TensorRT quantization, edge runtime deployment, and PLC logic integration.
We deploy engineers to site to calibrate under real production conditions, test under edge cases, and train your maintenance operators to keep the system clean and calibrated.
Key Deliverables
A turn-key edge vision deployment running on your production line, integrated with your PLCs and operating at sub-50ms latency — with on-site engineering calibration included.
- Edge GPU inferencing pipeline running TensorRT models at deterministic frame rates
- PLC and Industrial Ethernet (Profinet/EtherNet/IP) hardware triggering and stop logic
- On-site optical calibration, lighting rig design and environment tuning
- Continuous data capture pipeline that routes edge-case frames for cloud model re-training
What's included service?
- + Optical, lighting and edge hardware specification
- + Model training, quantization and TensorRT optimization
- + PLC logic integration and hardware I/O wiring
- + On-site commissioning, calibration and operator handover
Process
From Idea
to Production
to Production
FAQs
Frequently
asked questions
asked questions
A focused pilot reaches a working, testable v1 in four to six weeks. A production system - with on-site deployment and integration-typically runs eight to twelve weeks depending on hardware, data access and how many systems it has to talk to. We tell you which one you are in during discovery, not after.
A clear problem statement, a definition of success, access to sample data, and one stakeholder who can make decisions. That is genuinely it. We run a kickoff workshop to pin down scope and the KPI model before anyone writes code.
Whichever ones wins on accuracy, latency and cost for your problem. In practice: Claude and GPT for reasoning and generation; Llama and Mistral when it has to be self-hosted; YOLO, OpenCV and InsightFace for vision; PyTorch, ONNX and TensorRT for anything on the edge. We are not loyal to a vendor. We are loyal to the benchmark
Yes, and several of our systems do. We have shipped fully on-premise vision analytics that runs a single executable with no internet connection, and self-hosted voice assistants where every inference endpoint stays inside the customer VM. If compliance rules out third-party APIs, we design for that from the start.
Development is included in the project price. Model and API usage is billed at cost, based on your actual volume. We estimate it up front and then work to bring it down - compression, caching and smaller models where a smaller model is enough.
Monitoring, tuning and one support loop that runs from the engineers who built it. AI system drift-data changes, storefronts change, We watch for it and we fix it.