Developing Quality Control Software In-House: the (Hidden) Costs of Reinventing the Wheel
- Produce Quality Control
- Product
- Quality Control App
The hype cycle for AI and ML is probably over. We’ve entered the mass adoption phase in the evolution of these tools, and with it, the democratization of the underlying technology. These technologies are more widely available and accessible than they ever have been, lowering the barriers to entry for building and deploying AI applications.
Understandably, then, more and more businesses are mulling over the idea of building their own quality control platforms and applications. But while the barriers to entry may be lower, it’s by no means clear that they’re low enough to justify the cost and time expenditure of developing an AI-powered quality control software in-house.
Here, we’re examining the costs and time commitment required to develop such a system, from the point of view of fresh produce businesses (whose core operations typically do not include building advanced IT systems or software).
Minimal Viable Product (MVP): In for a Penny, in for a Pound
If an organization has the resources to contemplate a project like this, they need to aim high. That’s because, while it may be possible to reach something approximating level 1 quality control: automated inspection, manual data entry, with the app structuring the data to make it accessible and manipulable.
But as a business scales, it will need to address more than this low hanging fruit. To reach an MVP, it’s necessary to invest in infrastructure, licensing of development tools and integrations. Beyond that, testing, optimization and continual maintenance will be necessary to ensure that what’s been spent so far.
And that’s all before you start adding new features like computer vision and advanced defect analysis. Before we get to those, let’s take a closer look at these basic costs:
Infrastructure Costs
- Servers and Cloud Storage: Costs for the hardware or cloud infrastructure needed to run and store the software and data.
- Database Management: Expenses related to setting up and maintaining a database system to handle large volumes of data.
- Network and Security: Costs for ensuring the software is secure from cyber threats, including firewalls, encryption, and regular security audits.
In the best case scenario, an organization establishes this infrastructure and lays the foundation for the development of a quality control platform. Once that’s achieved, there are ongoing costs to consider:
Maintenance and Support
- Ongoing Maintenance: Continuous updates, bug fixes, and improvements to the software post-launch.
- Technical Support Team: Salaries for a team to provide support and troubleshoot issues as they arise.
- Documentation and Training: Costs for creating documentation and training your team to use and maintain the software.
These costs are variable and hard to quantify. But the step between them – actual software development – is easier to visualize, which we’ll turn to next.
Software Development Costs: Salaries for AI Development and UX/UI Design
According to research by ZipRecruiter, the average salary for AI developers in 2024 sits somewhere around $129,000, around $62 per hour. These are the specialized developers needed to build AI and ML-based solutions for quality control software.
Next, UX/UI designers need to work on creating a usable interface. This is especially important for fresh produce businesses, because their operations involve a wide range of people with varying degrees of technical training. For an AI-powered quality control app to be useful at all, it needs to be easy to use, from the field to the inspection point to the boardroom.
All told, a fresh produce business could be looking at anything between $30.000 and $150.000 to develop an app – an activity that does not form part of their core business operations, with questionable returns.
Clarifresh vs. DIY: A Better Solution for Your Business
All of these costs, time commitments and technical challenges would of course be justifiable if AI-powered quality control solutions did not exist. But thankfully, they do. Clarifresh packages advanced AI, ML and computer vision capabilities in a platform tailor built specifically for fresh produce quality control. Fresh produce growers, distributors and retailers can access these powerful capabilities without the need to invest in complex infrastructure, high-cost AI expertise, or ongoing maintenance.
In other words: the heavy lifting has already been done. Clarifresh is the scalable, easy-to-use solution that organizations need to digitize quality processes and reallocate resources to their core activities: growing their business and delivering top-quality produce.
By choosing Clarifresh, you not only reduce costs and eliminate hidden risks, but you also ensure that your quality control processes are backed by cutting-edge technology and industry-leading expertise.
Don’t let the complexities of in-house development slow you down. Opt for a trusted partner that delivers results from day one.