Production systems across industries - each with real performance metrics from live deployments.
The right choice depends on latency requirements, connectivity, and data privacy constraints.
By the numbers
The old way
The StartxLabs way
Common questions
How much labelled data do we need to start?
For a focused defect detection task, 500–2,000 annotated images is often sufficient to beat rule-based baselines. We run active learning loops to prioritise which additional images to label - so you get the most accuracy per annotation dollar.
Can models run entirely on-premise without internet?
Yes. Our edge deployment stack runs on Jetson Orin, Intel NUC, or NVIDIA RTX workstations with no cloud dependency. OTA updates are optional and can be routed through your internal network.
What if our defect types change over time?
We build continual learning pipelines with a human-in-the-loop review step for novel examples. New defect classes can typically be incorporated in 1–2 weeks with as few as 50 confirmed examples.
How do you handle model performance degradation in production?
Monitoring dashboards track confidence score distributions and flag drift before accuracy drops below your SLA threshold. We include automated retraining triggers and canary deployment patterns in every production system.
Do you work with existing camera infrastructure?
We integrate with RTSP streams, GigE Vision cameras, USB3 Vision, and standard IP cameras. We can also advise on camera selection, lens, and lighting configuration during the discovery phase.
Client result
"Replaced 12 manual QC inspectors with a single Jetson-based vision system - zero false passes in the first 90 days of production."
Contact Us
Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.
Production systems across industries - each with real performance metrics from live deployments.
The right choice depends on latency requirements, connectivity, and data privacy constraints.
By the numbers
The old way
The StartxLabs way
Common questions
How much labelled data do we need to start?
For a focused defect detection task, 500–2,000 annotated images is often sufficient to beat rule-based baselines. We run active learning loops to prioritise which additional images to label - so you get the most accuracy per annotation dollar.
Can models run entirely on-premise without internet?
Yes. Our edge deployment stack runs on Jetson Orin, Intel NUC, or NVIDIA RTX workstations with no cloud dependency. OTA updates are optional and can be routed through your internal network.
What if our defect types change over time?
We build continual learning pipelines with a human-in-the-loop review step for novel examples. New defect classes can typically be incorporated in 1–2 weeks with as few as 50 confirmed examples.
How do you handle model performance degradation in production?
Monitoring dashboards track confidence score distributions and flag drift before accuracy drops below your SLA threshold. We include automated retraining triggers and canary deployment patterns in every production system.
Do you work with existing camera infrastructure?
We integrate with RTSP streams, GigE Vision cameras, USB3 Vision, and standard IP cameras. We can also advise on camera selection, lens, and lighting configuration during the discovery phase.
Client result
"Replaced 12 manual QC inspectors with a single Jetson-based vision system - zero false passes in the first 90 days of production."
Contact Us
Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.