What is an AI Worker?

An AI Worker is an autonomous AI agent that works like a real teammate: it has a personality, specialized skills and memory, runs tasks end to end and integrates with your tools (Slack, Teams, Gmail, Notion…).

How many AI Workers are available?

15 specialized AI Workers are available, covering marketing, development, design, data, human resources, finance and customer support.

Can I try it for free?

Yes, a free trial is included with no commitment. You can add or remove AI Workers at any time and cancel whenever you want.

Yes. Your data is encrypted end to end, hosted in isolated environments, and never used to train models. Workers Delos is GDPR-compliant and SOC 2-certified.

Controle Qualite par IA : Detecter les Defauts 99% Plus Rapidement

Par Delos Intelligence — 2026-08-11

Manual quality inspection catches only 65% of defects. AI-powered quality control using computer vision detects 99% of defects in real time, cutting scrap costs by 50%.

The Limits of Manual Quality Inspection

Manufacturing defects cost enterprises $50 billion annually in scrap, rework, and warranty claims. Human inspectors miss 35% of defects on average (Deloitte, 2025). Inspection accuracy drops 20% after 2 hours of continuous work. Manual inspection is the bottleneck in high-speed production lines. Inconsistent standards across shifts and facilities compound the problem.

How AI Quality Control Works

Computer Vision for Defect Detection

AI models trained on thousands of defect images identify surface defects, dimensional deviations, assembly errors, and color mismatches at production line speeds. Cameras capture every unit; the model classifies each as pass, fail, or review in under 100 milliseconds.

!Defect Detection Rate Comparison

Real-Time Process Monitoring

Beyond visual inspection, AI monitors process parameters (temperature, pressure, vibration, speed) to predict defects before they occur. This shifts quality control from reactive detection to proactive prevention.

Continuous Learning and Adaptation

Modern AI quality systems learn from every inspection. When a new defect type appears, the model retrains within hours using production data.

!AI Quality Control Process

Quantified Business Impact

99% defect detection rate vs 65% manual (Aberdeen Group, 2025)
50% reduction in scrap and rework costs
40% reduction in warranty claims
30% reduction in inspection labor costs
10-15% improvement in OEE
ROI typically achieved within 6-9 months

Implementation Roadmap

Phase 1: Assessment and Pilot (Weeks 1-6)

Identify the highest-defect product line. Install cameras and lighting at the inspection point. Collect 5,000+ labeled images.

Phase 2: Model Training and Deployment (Weeks 7-12)

Train the computer vision model. Deploy alongside manual inspection for parallel validation. Measure accuracy and false positive rates.

Phase 3: Full Automation (Weeks 13-18)

Once model accuracy exceeds 95%, transition to AI-first inspection with human review only for flagged exceptions.

Overcoming Implementation Challenges

Data quality: start with well-lit, consistent imaging conditions
False positives: set confidence thresholds above 90% to avoid unnecessary rejects
Change management: train operators to work alongside AI, not against it

Conclusion

AI-powered quality control delivers measurable ROI within the first year. With defect detection rates approaching 99% and scrap cost reductions of 50%, the question for manufacturers is no longer whether to adopt AI inspection, but how quickly they can scale it.