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FictiveBox
AI & ML EngineeringSolution

Computer Vision QA

Edge-deployed defect detection and visual inspection wired into MES with measured drift and latency.

Problem

What we are solving

Manual QA misses defects and slows the line. Cloud-only vision is too slow and too expensive for high-throughput production environments.

Solution

How it works

Vision models distilled to run on NVIDIA Jetson and Triton at the edge, with sub-50ms inference, drift monitoring and a labelling feedback loop. Bad units are auto-flagged in MES and rerouted before they leave the cell.

Use Cases

Where it ships

Manufacturing

Surface defect classification on stamped, painted and welded parts with full traceability to the work order.

Logistics

Parcel damage and label verification at sortation throughput speeds.

Pharma & Food

Fill-level, seal and date-code verification with audit-grade evidence retention.

Stack

Tech stack

PyTorchTritonNVIDIA JetsonONNXRoboflowKafkaGrafana
What you get

Engagement deliverables

Architecture and reference implementation tailored to your environment
Integration into your existing systems of record and action
Production hardening: observability, SLOs, runbooks and on-call
Knowledge transfer and optional managed operations

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