Discover the advantages and disadvantages of developing your own AI-based solution for visual quality inspection in our white paper – and learn when it becomes strategically relevant.
CEO and Co-founder of Maddox AI
We often hear from customers that an internal solution already exists – but in many cases, these are not fully implemented or fail to meet expectations. With AI being a hype topic, many decision-makers allocate significant budgets to develop their own in-house AI solutions. However, even AI projects must deliver a positive return on investment. This white paper helps decision-makers assess when developing an in-house AI solution makes strategic and economic sense.
Training an AI model has never been easier or more accessible – provided there is a sufficiently high-quality data foundation. However, a successful pilot project does not automatically translate into a fully operational vision software. To solve AI-based image recognition challenges in a scalable and sustainable way, a powerful algorithm alone is not enough. The effort required to build an in-house AI solution is often underestimated – in many cases, the actual AI programming accounts for less than 5% of the overall solution.
Automated image processing has been a core component of industrial quality control for decades – typically based on traditional, rule-based systems and externally sourced software solutions. However, the increasing adoption of artificial intelligence is changing how many companies approach this area: internal development projects are receiving significant budgets to build proprietary AI solutions. Whether this approach leads to lasting competitive advantages or inefficient use of resources depends on a range of strategic and economic factors.
Our white paper provides a practical framework to support companies in making the right decision: develop AI in-house – or buy an existing solution?
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