Automate quality assurance processes
Defect Recognition
At Altamira, we understand that delivering a flawless product is a top priority for business success. We offer cutting-edge technology that scans your product in real time to identify and address defects before they reach your clients.
15+
Years expertise
140+
Satisfied clients
120+
Team members
Defect recognition use cases
Production line monitoring
Implement real-time defect detection on production lines, enabling immediate intervention and corrective action. Reduce wastage, improve yield rates, and enhance overall operational efficiency.
Predictive maintenance
By using defect data and analytics, manufacturers identify patterns and predict potential equipment failures or wear, enabling proactive maintenance and minimising unplanned downtime.
Supply chain quality control
Ensure consistent quality standards across the entire supply chain by implementing our defect recognition solution at various checkpoints, from raw material inspection to final product assembly.
Root cause analysis
Gain valuable insights into the nature and root causes of defects through detailed defect analysis, enabling targeted process improvements and preventing recurring issues.
Regulatory compliance
Meet strict industry regulations and quality standards by demonstrating consistent quality control measures backed by our defect detection capabilities.
Altamira is trusted by
Strive for excellence with Altamira
Altamira has been awarded numerous times for its performance and achievements. Join our community of successful customers, whom we helped to build and grow their businesses through defect recognition solutions.
The main challenges we solve
Overcome all these challenges with our AI software solution.
Cost reduction
Minimise production costs associated with scrapped or reworked products by identifying defects promptly and accurately.
Operational performance
Streamline quality inspection workflows, allowing for faster inspection cycles and reduced downtime.
Consistency in product standards
Maintain consistent product standards across batches by implementing reliable defect recognition systems that provide consistent results.
Customer satisfaction
Improve customer satisfaction by delivering products free from defects, enhancing brand reputation, and reducing returns or complaints.
Data-driven decision making
Collect and analyse defect data over time, identify trends, and implement proactive improvements to enable data-driven decision-making.
Labour intensity reduction
Reduce reliance on manual inspection processes, freeing up skilled labour for more strategic tasks and reducing labour costs.
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Let’s treat the core problems, not symptoms.
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Explore our case studies and find out how we have helped some of our clients from around the world.
Our tech stack
Our tech stack allows us to deliver accurate defect recognition solutions that improve manufacturing processes by monitoring the production line, identifying product flaws in real-time, and analysing the root causes of defects.
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Case studies
People also asked
By analysing images or sensor data, AI systems can detect imperfections that might be missed by the human eye. As a result, the inspection accuracy and speed are significantly improved, allowing for real-time quality control and reducing the need for manual inspections. Contact us to learn more.
AI plays a key role in quality assurance and defect detection by automating the inspection process, ensuring consistency, and reducing human error. It also helps in maintaining high-quality standards, and optimising production efficiency. With AI, businesses can implement predictive maintenance, monitor production lines in real-time, and quickly identify any anomalies or defects.
AI can be used to detect a wide range of issues, including:
- Surface defects such as scratches, dents, and cracks.
- Structural flaws like misalignments or deformations.
- Anomalies in data patterns indicating potential problems.
- Quality deviations in manufacturing processes.
- Faults in mechanical components.
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AI anomaly detection refers to the use of artificial intelligence to identify unusual patterns or behaviours in data that deviate from the norm. This technique helps to spot potential defects, security threats, or system failures. AI models learn from historical data to establish a baseline of normal operations, and any significant deviation from this baseline is flagged as an anomaly.
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