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Inspect Everything. Miss Nothing.
Deep learning defect detection that runs at 99%+ accuracy — every shift, every hour, without fatigue. Every defect classified by type and severity, trended over time, and routed into corrective action before it leaves your plant.
The Inspection Ceiling
Your best inspectors catch 80-85% of defects on a good shift. Under fatigue — late in a rotation, during high-volume runs — that drops to 60-70%. The gap is not effort. It is biology.
The defects that pass through become customer complaints, warranty claims, and in some industries, recalls. Manufacturers spend an average of 20% of revenue on cost of poor quality. Globally, quality failures cost $1.7 trillion annually.
Meanwhile, defect data sits in paper logs or disconnected spreadsheets. You know something failed. You rarely know why it keeps failing. Without structured defect classification and trend data, quality teams react to escapes instead of preventing them. The ceiling on human inspection is well-documented. The question is what replaces it.
Why This Matters
Human inspectors catch 80-85% on a good shift; 60-70% under fatigue (MIT Human Factors). At production speed, the undetected 15-20% reaches your customer — as warranty claims, returns, or recalls. The global cost of quality failures is $1.7 trillion annually (ASQ/NIST). For a single mid-market manufacturer, cost of poor quality typically represents 15 to 25% of revenue — a figure that includes scrap, rework, warranty, and the hidden costs of customer-driven corrective actions. Process capability metrics like Cpk tell you whether your process is capable. Inspection tells you whether the output meets spec. But when inspection itself has a ceiling, even a capable process generates escapes. The inspection gap is the final barrier between your quality system and your customer.
How It Works
What Quality Vision Does
Measurable Outcomes
Eliminate Inspection Fatigue
99%+ detection accuracy sustained across every shift. No performance degradation at hour ten that did not exist at hour one.
Reduce Quality Escapes
60-90% reduction in defects reaching the customer. 100% inspection coverage replaces statistical sampling. The defect escape rate — the percentage of non-conforming units that reach the customer — becomes the primary quality KPI, and it drops dramatically when every unit is inspected.
Lower Cost of Poor Quality
Fewer warranty claims, fewer returns, fewer customer-driven corrective actions. COPQ reduction begins at the inspection point, not after the complaint. For manufacturers spending 15-25% of revenue on cost of poor quality, a meaningful reduction in escape rate translates directly to margin improvement.
Structured Quality Intelligence
Defect data classified, trended, and connected to corrective action. Quality decisions informed by data, not anecdote. FMEA reviews are grounded in actual defect frequency data rather than estimated risk priority numbers.
Expected Outcomes:
In early deployments, customer quality escapes have decreased by 60-90% through 100% inline inspection at 99%+ accuracy.
Cost of poor quality has decreased measurably as catch rates improve and rework replaces customer-side failure costs.
Defect trend data has enabled proactive FMEA updates and prevention control improvements, reducing the recurrence rate of top failure modes by 30-50%.
Related Modules & Pages
See Quality Vision in Your Environment
Book a consultation. We will walk through how Quality Vision applies to your specific products, defect types, and inspection challenges — and what 99%+ accuracy looks like on your line. Bring your defect samples. We will show you what the model sees.
