How much data do we need to train a computer vision model for our specific use case?

It depends on the complexity of the task, but it's often less than people assume. Thanks to transfer learning, a well-scoped defect detection or classification model can reach production accuracy with a few thousand labeled images per class. More complex tasks like medical image segmentation or autonomous driving require significantly larger datasets. A practical first step is a data audit: looking at what visual data already exists in the organization, how it's labeled, and where the gaps are. That audit usually clarifies the scope of the data effort within a few weeks.

Do we need specialized hardware to run computer vision in our facility?

Not always. Many computer vision applications run perfectly well on standard cameras, off-the-shelf GPUs, and existing on-premise servers. Edge deployments do benefit from specialized AI accelerators, but the hardware market has matured, and the cost has dropped substantially. The right hardware setup is a function of latency requirements, image volume, and where the data needs to live for compliance reasons. A proper architecture review at the start of the project answers this question concretely.

Can the computer vision technology integrate with our existing production line, ERP, or security infrastructure?

Yes, and integration is usually the part of the project that takes the most thought. Modern computer vision systems are designed to expose their outputs (detections, classifications, alerts) through APIs, message queues, and standard protocols, so they can feed ERP systems, MES platforms, video management systems, or custom dashboards. The work is in mapping the model's output to the data structures and workflows that the existing systems expect. This is where having an implementation partner with both vision and systems integration experience pays off.

When should we build a custom computer vision solution versus using an off-the-shelf platform?

Off-the-shelf platforms are the right choice when the use case is generic and when the volume and accuracy requirements fall within what the platform supports. A custom-built solution becomes the better path when the use case is specific to the business (a particular product, a proprietary process, a regulated environment), when accuracy needs to be higher than a general model can deliver, when the data is too sensitive to send to a third-party cloud, or when latency and edge deployment matter. A short discovery phase, often two to four weeks, is usually enough to determine which path makes sense and what the implementation effort looks like.