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In-House or Integrator? Choosing the right path for your machine vision project

You ususally handle your machine vision tasks yourself, but have your ever asked yourself if cooperating with a system integrator could make things easier, faster, or more reliable?

When planning a machine vision system, many organizations face the same question: “Do we build it in-house, or partner with a system integrator?”

There’s no one-size-fits-all answer, but four factors matter most: Expertise, time, budget and complexity.

You may have the in-house skills in imaging, lighting, optics, algorithm selection, integration and maintenance, but the journey from prototype to full production often takes much longer than expected.

various machine vision applications

If you’re unsure, it’s worth asking: Why not take advantage of a system integrator’s expertise?

They can help:

Engineer a complete solution for your automation or quality challenge

Physically realize and install the system on the shopfloor

 What a system integrator brings to the table

  • Broad hands-on experience with hardware, software and production environments across multiple industries.
  • Deep familiarity with all the surprises real-world production can deliver.
  • Day-today expertise in selecting the right components and optimizing development
  • Faster time-to-market, reduced risk, and fewer hidden costs.
  • Scalable solutions designed for future upgrades

How to choose the right partner

Evaluate potential integrators based on:

  • Relevant experience: Have they solved similar challenges before?
  • Technology independence: Do they work with multiple vendors and platforms?
  • Solution mindset: Do they simply reuse a solution they already have, or tend to overengineer the task instead of finding just the right solution for your specific problem?
  • Transparency: Are costs, timelines and deliverables clearly defined?
  • Flexibility: Can your team maintain or evolve the system later on?
  • Production readiness: Is the solution designed for real-world conditions, not just lab demos?

From prototype to production

In machine vision, what looks simple in a demo can become complex in production. Variations in lighting, motion, parts, or environment can quickly affect performance.

Early success ≠ long-term reliability

Before deciding, ask (and be honest to) yourself

  • How complex is the task?
  • Do you have the right people and resources?
  • What’s your plan for scale-up and ongoing support?

If you answered “yes” to two or more questions, partnering with an experienced integrator might be the clearest path to production-ready performance.

What's been your experience with machine vision implementation?
Have you built systems in-house or worked with integrators? 

I'd love to hear your insights!