A few questions about your current production, a live estimate.
Adjusts the estimate to your industry. The result covers a single production line.
Default assumptions: 1x8 and 2x8 ≈ 250 days/yr, daytime · 3x8 ≈ 250 days/yr, 24h/24 · 5x8 ≈ 365 days/yr, 24h/24, 7 days/week. 3x8 and 5x8 include night hours (and 5x8, weekend hours too), which are paid at a premium: the loaded hourly cost will be adjusted automatically further below.
to pay back your investment
3-year ROI
Labor savings / year
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Cost avoided / year
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Net savings / year
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This estimate covers direct savings. It does not quantify certain indirect costs that are often heavier over time: brand reputation damage, loss of trust and future contracts, higher insurance premiums after an incident, team time spent on crisis management, or risk of losing a quality certification. The result above is therefore a floor estimate.
Illustrative estimate only, not a quote or a guarantee: every project is unique, and system cost also depends on camera count and integration complexity. Replace default values with your own figures, and contact us for a precise quote.
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Some costs of a defect that reaches a customer are real but don't fit into a formula, but they matter just as much, if not more, in the decision.
A defect that reaches an end customer damages trust well beyond the cost of rework: a product recall has a measurable impact on the stock value of listed companies, and on reputation for others.
A customer who has experienced a quality incident renegotiates, reduces volumes, or switches suppliers, a cost that never shows up in the quarter the incident happened.
After an incident (particularly a fire risk), industrial insurance premiums increase significantly, sometimes for several years.
Every incident pulls quality, production, and management teams into crisis management instead of continuous improvement or innovation.
Repeated non-conformity can jeopardize a quality certification (IATF 16949, etc.), a condition for access to certain markets or customers.
Fine-grained traceability enables a targeted recall (batch, lot) rather than a mass recall: a difference in cost scale that no average reflects.
Our vision engineers can qualify your case and provide you with a precise quote, with no commitment.
Replacing or supplementing manual quality control with an AI-powered machine vision system is a significant investment whose payback needs to be demonstrated before any decision is made. Unlike many other industrial investments, the return on investment of an automated quality inspection system isn't limited to a single cost line: it combines labor savings, a reduction in the number of defects that escape inspection, and in some cases the avoidance of a major quality incident such as a product recall. Our calculator estimates these three levers within a few minutes, based on data specific to your production line.
An AI-powered machine vision system uses one or more cameras positioned along the production line to capture an image of every part, product, or component. A deep learning model, trained on hundreds of examples of compliant and/or non-compliant parts, then analyzes each image within milliseconds to detect visual defects: scratches, deformations, assembly errors, contamination, or any anomaly defined by the product's quality specifications. Unlike a rule-based inspection system, an AI model generalizes from the examples it has seen and can detect defect variations it has never encountered in exactly the same form before, which makes it particularly well suited to production environments where defects take varied shapes.
One of the main advantages of this approach over human inspection is consistency: even an experienced operator's attention drops after several hours of repetitive inspection, especially at high throughput or during night shifts. An AI vision system maintains the same detection level at 3am as at 9am, regardless of line speed.
The ROI calculation depends on several parameters specific to each production site. The first is volume: the higher the number of parts produced per day, the higher the absolute number of defects, and the greater the savings potential. The shift pattern also plays a role: a line running in 3x8 or continuous 5x8 involves night and weekend hours, which are typically paid at a premium, increasing the real cost of the equivalent manual inspection and improving the ROI of an automated solution accordingly.
The current non-conformity rate, the proportion of parts with a defect, is another determining factor, as is the inspection method currently in place. Sampling inevitably lets more defects through than exhaustive 100% inspection, which means a significant share of the value an AI system adds lies precisely in the parts that aren't inspected today.
Finally, the average cost of a defect that reaches the customer (return, warranty, rework) and, for the most exposed sectors, the risk of a major quality incident (product recall, contamination) weigh heavily in the calculation. An AI vision system is never perfect either: it also has a false-alarm rate, meaning a share of compliant parts wrongly rejected. This cost, often overlooked in simplified ROI calculations, needs to be included to get an honest estimate.
AI-based quality inspection applies to a wide range of industrial sectors, but some find particularly strong value in it. In electronics, the miniaturization of components makes human visual inspection increasingly difficult at high throughput. In automotive, traceability requirements and quality certifications (IATF 16949) require rigorous inspection across large volumes. Food and beverage combines food-safety and throughput concerns, where a missed defect can affect consumer health. The recycling and waste-processing sector faces specific risks, notably fire risk from improperly sorted lithium batteries: a use case where automated detection reduces both a human and a financial risk. Finally, glass and optics require fine-grained detection of defects that are often invisible to the naked eye at normal line speed.
The result produced by this calculator is an estimate based on the data you enter and on adjustable default assumptions. It aims to give a quick order of magnitude, useful for starting an internal discussion or justifying a more thorough study, but it does not replace a precise quote built with our vision engineers based on your actual case. Indirect costs (reputational impact, lost contracts, insurance premiums, crisis-management time) are intentionally not quantified here, since they are specific to each company and hard to generalize, but they should be kept in mind in any investment decision.
This depends heavily on production volume, shift pattern, and current non-conformity rate, but most AI quality inspection projects pay for themselves within a few months to a little over a year when volumes are sufficient.
Not necessarily. Many customers combine automated inspection with a reduced level of human oversight, particularly to handle ambiguous cases or perform second-level quality checks.
It's the proportion of compliant parts that an inspection system, human or automated, wrongly rejects. A false-alarm rate that's too high generates unnecessary scrap costs and can slow down production; it's a metric worth watching just as closely as the detection rate.