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AI Application Engineering
Model development, optimization and packaging into maintainable production applications, not notebooks.
Engineering
The engineering behind systems that work every shift.
Our team works across hardware, algorithms and software. That full-stack depth is what turns an AI model into a reliable industrial system.
Capabilities
Eight disciplines. Integrated by design.
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GPU-Accelerated Computing
Parallel algorithms, accelerated pre/post-processing and inference tuned to the target device.
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Real-Time Data Pipelines
Deterministic streaming architectures with bounded latency, zero-copy data paths and back-pressure.
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Computer Vision
Classical and deep-learning vision: calibration, metrology, detection, segmentation and tracking.
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Sensor Integration
Industrial cameras, depth, IMU, ultrasound, radar and instrument interfaces, with hardware time-sync.
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Python & C++ Development
Python for rapid iteration and tooling; modern C++ where performance and determinism matter.
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Edge Deployment
Embedded and industrial edge targets, containerization, remote updates and fleet observability.
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System Integration
PLCs, robot controllers, SCADA/MES, industrial protocols and enterprise systems.
System architecture
A layered reference architecture.
Every system we build follows the same layered architecture, with clear boundaries, measurable timing at each layer and a path to replace any component without rewriting the whole.
- L6 · InterfacesOperator UIs · dashboards · APIs · alarms
- L5 · IntegrationPLC · robot controllers · SCADA / MES · industrial protocols
- L4 · DecisionRules · state machines · actuation timing · audit logging
- L3 · InferenceOptimized models · multi-model scheduling · accelerated pre/post-processing
- L2 · PipelineStreaming graph · zero-copy buffers · synchronization · back-pressure
- L1 · SensingCameras · depth · imaging · IMU · instruments · time-sync
Performance engineering
Latency is designed, not discovered.
We start with a time budget from sensor exposure to physical action and allocate it across capture, transfer, pre-processing, inference, decision and actuation.
Pipelines are instrumented from the first build, so every stage reports its own timing on the target hardware. Optimization is driven by measurement: memory movement, precision, batching and scheduling.
Performance figures are reported for your configuration only. We do not quote generic benchmarks as promises.
Delivery
How engineering engagements run.
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Discovery
We study the process, the physics and the constraints, and define measurable success criteria and a latency budget.
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Feasibility
We test on your real parts, signals or images to establish what accuracy and speed are achievable before major investment.
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Engineering
We build the system in iterative increments: sensing, models, pipeline, integration and interfaces.
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Validation
We verify against the agreed criteria on target hardware, under realistic conditions, with documented results.
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Deployment
We commission on site, train your team and hand over documentation, source options and operating procedures.
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Evolution
We monitor performance, retrain on new data and extend the system as your operation changes.
Engineering consultation
Have a process that needs to see, decide and act?
Tell us about the parts, signals or images you work with. An engineer, not a salesperson, will reply with an initial assessment of feasibility and next steps.