Camera / sensor
Establish trusted inputs, ownership, and clear access boundaries.
TECHNOLOGY / EMERGING
Computer vision & edge AI
Bring intelligence closer to operational signals.
ARCHITECTURE GUIDE / 10HOW THE TECHNOLOGY CONNECTS
REFERENCE DESIGN01Camera / sensor
02Local inference
03Event detection
04Exception queue
05Operator review
CONTROL PLANEIdentity · governance · observability
THE PRACTICAL VIEW
Understand the pattern.
Then choose the tools.
Visual or sensor workloads have latency, connectivity, or data-volume constraints.
Image inspection, event detection, local inference, model optimization, and device telemetry. The right design depends on data characteristics, operating constraints, risk, and the people who will maintain it.
Collect representative samples; evaluate models; deploy at the edge; monitor exceptions and drift.
ARCHITECTURE RESPONSIBILITIES
Local inference
Make this responsibility explicit, observable, and independently testable within the overall workflow.
Event detection
Make this responsibility explicit, observable, and independently testable within the overall workflow.
Exception queue
Make this responsibility explicit, observable, and independently testable within the overall workflow.
Operator review
Define acceptance checks and ownership of the resulting information or action.
TECHNOLOGY OPTIONS
Architecture first.
Tools with a purpose.
Vision models, edge accelerators, IoT gateways
Examples describe architectural options, not Travinto partnerships, certifications, or confirmed integrations.
Fit
Does the pattern solve a specific workflow problem?
Trust
Can access, quality, and source evidence be verified?
Operate
Can the team observe, recover, and manage the system?
Value
Can usefulness and cost be evaluated against a baseline?
Where this pattern
can come into focus.
START WITH YOUR CONTEXT
Make the next step
a practical one.
Explore the opportunity, assess your current landscape, and agree a focused scope with clear success criteria.
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