Computer Vision Development Services

Frequently Asked Questions

• Q:

  What computer vision solutions does Azumo build?

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  Azumo builds production-grade computer vision systems for object detection, image classification, quality inspection, facial recognition, OCR, and real-time video analysis. We have delivered computer vision solutions including ID verification systems using super-resolution imaging, visual quality control for manufacturing lines, and automated document processing pipelines. Our computer vision stack includes PyTorch, TensorFlow, OpenCV, and pre-trained architectures like YOLO, ResNet, and Mask R-CNN. We deploy on AWS, Azure, and Google Cloud with edge computing options for low-latency applications. Every project ships under SOC 2 compliance from our nearshore engineering teams across Latin America.

• Q:

  Why should companies invest in computer vision development?

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  Companies invest in computer vision to automate visual inspection tasks that are slow, inconsistent, or expensive when performed manually. Computer vision systems process thousands of images per minute with sub-millimeter precision, operate 24/7 without fatigue, and deliver consistent results regardless of volume. Common ROI drivers include reduced quality control costs, faster processing throughput, fewer defects reaching customers, and automated compliance documentation. Azumo clients typically see ROI within 12-18 months of deployment. We have built computer vision systems for ID verification, content analysis, and visual data processing across healthcare, manufacturing, media, and financial services.

• Q:

  What are the main steps in a computer vision project?

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  A computer vision project follows eight phases: requirements definition, data collection and annotation, data preprocessing and augmentation, model architecture selection, model training and optimization, evaluation and validation, production deployment, and continuous monitoring. Azumo starts every engagement with a discovery session to define business objectives, success criteria, and technical constraints. Data annotation quality directly determines model performance, so we invest heavily in precise labeling workflows. We select model architectures based on your latency requirements, accuracy targets, and deployment environment: cloud, edge, or on-premises. Post-deployment, we monitor model drift, collect feedback, and retrain with new data. Typical timeline from discovery to production: 4-9 months depending on data readiness and integration complexity.

• Q:

  What data do we need for a computer vision project?

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  Successful computer vision requires high-quality, labeled images that represent real-world conditions your system will encounter in production. This includes diverse lighting conditions, varied object appearances, multiple angles, and edge cases. Initial proof-of-concept models can work with 50-100 labeled samples per class. Production systems typically need thousands of curated samples for reliable performance. Azumo provides end-to-end data strategy including collection planning, annotation workflows with quality assurance, data augmentation using rotation, scaling, and synthetic generation, and balanced sampling to prevent model bias. For regulated industries like healthcare, we handle de-identification and compliance requirements. We also implement ongoing data collection pipelines to continuously improve model accuracy after deployment.

• Q:

  What computer vision tasks can be automated?

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  Common automated computer vision tasks include image classification, object detection and localization, instance segmentation, facial recognition, optical character recognition, pose estimation, anomaly detection, and real-time video analysis. Specific business applications include quality control on manufacturing lines, automated document processing, product categorization for e-commerce, vehicle and license plate recognition, medical image analysis, security monitoring, and content moderation. Azumo built an ID verification solution using computer vision with super-resolution imaging, and has delivered automated visual analysis systems for clients in media, healthcare, and financial services. We also build custom pipelines combining multiple vision tasks: for example, detecting objects, classifying them, reading text on them, and routing results to downstream systems.

• Q:

  What technologies does Azumo use for computer vision?

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  Azumo's computer vision stack includes PyTorch and TensorFlow for deep learning, OpenCV for image processing, and architectures including YOLO for real-time object detection, ResNet for classification, Mask R-CNN for instance segmentation, and Vision Transformers for advanced visual tasks. We use transfer learning with pre-trained models from ImageNet, COCO, and domain-specific datasets to reduce training time and data requirements. Cloud deployment uses AWS SageMaker, Azure Machine Learning, and Google Vertex AI. For edge deployment, we optimize models for GPUs, TPUs, and specialized hardware like Intel Neural Compute Sticks. MLOps infrastructure uses Docker, Kubernetes, MLflow, and CI/CD pipelines for scalable, maintainable production systems.

• Q:

  How does Azumo approach computer vision development?

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  Azumo provides end-to-end computer vision development: strategic consulting, data strategy and annotation, model development and training, evaluation, deployment, and ongoing optimization. We start with a discovery session to align on business objectives and technical constraints. Our data team creates high-quality training datasets with rigorous annotation quality assurance. We use systematic hyperparameter optimization, data augmentation, and ensemble methods to maximize model performance. Deployment options include cloud-based solutions on AWS, Azure, or Google Cloud, edge computing for low-latency applications, and on-premises deployment for security-sensitive environments. Post-deployment, we provide monitoring dashboards, automated alerting, and regular performance reviews. SOC 2 certified with nearshore teams across Latin America working in your time zone.

• Q:

  What security and compliance standards does Azumo follow for computer vision?

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  Azumo is SOC 2 certified and implements end-to-end encryption, secure key management, role-based access controls, and comprehensive audit logging for all computer vision projects. For healthcare applications, we maintain HIPAA compliance with data de-identification and access controls. Financial services projects follow PCI-DSS and SOX requirements. GDPR compliance includes data minimization, consent management, and right-to-deletion implementation. We offer on-premises and air-gapped deployment for organizations requiring complete data control. Our bias mitigation framework includes training data analysis, fairness metrics during evaluation, and ongoing monitoring of model outputs. We do not develop computer vision systems for inappropriate surveillance or content that violates ethical standards.