Tollscopic treats tolling reliability like a software and data-quality problem.
Observe the live system, capture edge cases, replay historical evidence, regression-test changes, stage releases, and monitor the result. The improvement loop is the product.
Five stages. The output of each becomes the input of the next.
This is the page where Tollscopic sounds like a serious AI company without using hype: observe production, capture what is hard, replay safely, test rigorously, release in stages.
Device health, image quality, evidence flow, queue depth, latency, model quality.
Edge cases, drift events, anomalies, low-confidence transactions, missing reads.
Re-run historical evidence against candidate model or configuration changes.
Regression-test changes against a held-out replay corpus. Promote only on improvement.
Stage the change to a subset. Monitor outcomes. Keep version traceability.
The system watches the pipeline, not just servers.
A green dashboard that says 'all servers up' is not enough for tolling. The signals that matter are the ones that predict KPI failure before it happens.
What happens before a KPI failure becomes obvious.
A camera slowly loses alignment. Image confidence falls. A classification distribution shifts. A queue grows during WAN degradation. Tollscopic monitors for these signals so they can be investigated early.
Per-camera confidence trending down. Brightness/contrast shifts. Frequent PTZ events.
Class distribution shifting from baseline without a corresponding traffic-pattern change.
A device producing fewer events than expected. Queue growth. Latency creeping up.
Imperfect networks. Imperfect weather. Still publishing cleanly.
The roadside operates in real conditions. The system preserves evidence locally when needed, publishes carefully when connectivity returns, and avoids duplicate or lost records through explicit state handling.
Evidence is persisted at the edge during WAN degradation. The roadside keeps observing.
Idempotency keys and explicit state tracking. Reconnects do not produce duplicate transactions.
Redundant Jetson Orin pair per cabinet. Edge compute survives a single-unit failure.
Multi-AZ cloud, event-driven processing, explicit recovery patterns for transient faults.
Reliability is what stops a tolling system from going sideways at month-end.
If your current vendor's reliability story is about response-time SLAs and maintenance schedules, this page is the contrast.