Engineering
intelligence
for industry.
We design automation, machine vision and edge AI systems that connect sensing, decision-making and machine control.
The right technology.
A complete system.
From field signals to operator software, each layer needs a clear role, a reliable interface and a way to verify its behavior.
Industrial Automation
Connect machine sequencing, operator interfaces and production data in one coherent control architecture.
Machine Vision
Build inspection around repeatable images: optics, lighting, calibration, processing and a traceable decision.
Edge AI
Place inference near the machine, with resource limits, model versions and predictable failure behavior.
Industrial Software
Create operator-facing tools, inspection records and dashboards around the way production actually works.
From sensor to decision.
Follow the architecture from a physical signal to a useful production action.
Sensors / Cameras
Trigger, optics and lighting
Edge Computing
Acquire and buffer images
AI / Vision
Inspect against defined criteria
PLC / Control
Validate result and interlocks
Industrial Software
Record result and image reference
Production Decision
Route, hold or request review
Reference architecture. Missing images, low confidence or late results must produce a defined hold or review state. Machine control and safety remain in the control system.
Inspect what matters.
Keep the evidence.
A useful inspection system begins with the part, the defect and the imaging conditions. The algorithm is one component of the decision path.
- Surface defects, presence and object classification
- Detection, segmentation and anomaly analysis
- OCR, code reading and calibrated measurement
- OK / NG / review decisions linked to control and records
Built around your
production constraints.
AI Visual Inspection
Evaluate detection, segmentation and anomaly models against the defects and uncertainty that matter to your process.
Industrial IoT
Turn machine signals into usable records with timestamps, quality flags and a defined data contract.
Embedded Systems
Integrate sensing, firmware and communication within practical electrical and timing constraints.
Robotics Integration
Coordinate vision, robot programs and machine sequencing through explicit handshakes and cell states.
Different lines.
Shared engineering needs.
Repeatable sensing, controlled decisions and usable production data. The architecture changes with the product and the process.
Explore industriesDefine. Evaluate.
Integrate. Verify.
Resolve the difficult assumptions early. Agree on evidence before calling a system ready.
Understand the process
Document the operating conditions, sample parts, interfaces and measurable acceptance criteria.
Prove the difficult part
Test acquisition, algorithms and timing on representative inputs before expanding scope.
Connect the complete system
Align software contracts, machine states and fault handling across sensors, compute and control.
Make handover repeatable
Validate against agreed criteria and prepare configuration, operating guidance and maintenance records.
Interfaces before isolated tools.
Technology selection follows the required timing, environment, data and maintainability. These are engineering options, not vendor partnerships or deployment claims.
Architectures made concrete.
AI Visual Quality Inspection System
A reference inspection station that connects controlled image acquisition, defect analysis and traceable quality decisions.
Machine Vision Sorting System
A camera-guided sorting architecture with job tracking, controller handshakes and confirmation sensing.
Edge AI Inspection Node
A local inference node that reports an inspection result together with device health and model identity.
Clear decisions.
Connected responsibilities.
Engineering first
Start with process requirements, data quality and system constraints. Choose technology after the problem is clear.
One connected architecture
Define how sensing, inference, control and software exchange information, including faults and recovery.
Evidence before claims
Use representative samples and agreed acceptance criteria. Distinguish concepts, prototypes and validated deployments.
Practical system thinking.
Reliable inspection starts with the image
Why lighting, optics, triggering and calibration belong in the acceptance plan before model selection.
Designing the vision-to-PLC handshake
A decision becomes useful only when the controller can associate it with the correct part and operating state.
What to validate before moving AI to the edge
A practical evaluation checklist for model behavior, device constraints and recovery on the target hardware.
What does your process
need to do better?
Share the part, the machine, the constraint and the result you need. That is the starting point for a useful technical conversation.