2D & 3D Data Annotation Service for Computer Vision

In our computer vision projects, we place central importance on the quality and structure of data, as a well-designed dataset directly influences model performance. We understand that computer vision requires a high degree of visual diversity to enable an algorithm to accurately recognize, segment, and interpret complex scenes, even in changing environments.

To enhance the robustness of computer vision systems, we systematically enrich our datasets with variations in scenes, lighting, camera positions, distances, and even occlusions. We also ensure a balanced representation across different classes to prevent biases that could weaken a visual analysis model based on computer vision.

Like any computer vision pipeline, our process relies on meticulous annotation control. We review labels, correct inconsistencies, remove unusable images, and verify the accuracy of boundaries (bounding boxes, polygons, 3D cuboids). Reliable annotation is essential because computer vision models are extremely sensitive to noisy data.

Our experience in computer vision has also taught us that a dataset must evolve continuously. We therefore implement an improvement cycle based on regular data audits, model error analysis, targeted re-annotation, and the addition of new edge cases. This allows us to maintain a dataset that is always aligned with the real needs of computer vision applications.

Finally, we structure our datasets according to strict standards: clear train/validation/test splits, usable metadata, versioning, and change tracking. Thanks to this approach, we ensure that our computer vision pipeline remains stable, reliable, and scalable. This is how we sustainably achieve computer vision models that are more accurate, robust, and better prepared to handle real-world scenarios. Through these proven methodologies, we optimize every dataset to achieve the highest possible performance in computer vision.

To improve a computer vision model, we adopt a methodical approach focused on data quality and continuous evolution. We know that a high-performing computer vision system depends directly on the relevance and diversity of the dataset used during training. That’s why we regularly enrich our datasets to expose the model to more visual variations, thereby enhancing the robustness of our computer vision solutions.

We then analyze the errors produced by the computer vision model to identify false positives, false negatives, or areas of uncertainty. This step allows us to adjust the dataset, correct ambiguous annotations, and add edge cases, which are essential for better generalization in computer vision.

At the same time, we apply targeted data augmentation to simulate complex conditions that the computer vision system must handle: lighting variations, extreme angles, fast motion, or partially occluded objects. These transformations strengthen the model’s ability to interpret varied and realistic scenes.

Finally, we implement an iterative training cycle. With each new version of the dataset, we retrain and evaluate the computer vision model to measure improvements in accuracy. Thanks to this continuous strategy, we sustainably enhance the performance, stability, and reliability of our computer vision solutions.

Choosing our expertise for your computer vision projects means partnering with a team capable of transforming your visual data into operational, cost-effective solutions that are truly aligned with your business objectives. We offer a unique combination: technical excellence, industrial-scale capability, strategic guidance, and deep mastery of 2D/3D annotation workflows — an essential mix for success in modern computer vision.

Comprehensive expertise across the entire value chain

We work across the full computer vision cycle, from defining needs to deploying models. Our value lies in our ability to:

• Analyze your operational constraints
• Structure an effective dataset
• Produce precise and consistent annotations
• Optimize your AI models and pipelines
• Ensure performance under real-world conditions

This integrated expertise helps avoid common mistakes, accelerate timelines, and deliver a fast return on investment.

Professional 2D/3D annotation production

We guarantee exceptional data quality, a central element in any computer vision project. Our strengths include:

• Annotators trained for complex tasks (2D, 3D, video)
• Rigorous quality control methodology
• Capability to handle very large volumes
• Custom guidelines tailored to your needs

Result: your models receive clean, consistent, and ready-to-train data.

Enhanced performance through continuous improvement

Computer vision projects require constant evolution, and we understand this. We implement:

• Regular dataset audits
• Model error analysis
• Targeted re-annotation
• Addition of edge cases
• Iterative improvement cycles

This approach progressively increases the accuracy, robustness, and reliability of your models.

A business-first approach

Our focus is not only technical — we think in terms of results. We help you:

• Reduce costs related to annotation and training
• Accelerate time-to-market
• Secure your technology choices
• Improve the productivity of your data and AI teams
• Fully leverage the opportunities offered by computer vision

Our value-oriented approach ensures that every dollar invested in computer vision delivers measurable impact.

Guaranteed security, privacy, and compliance

We provide a work environment compliant with the strictest standards for your computer vision projects, including:

• European hosting
• GDPR compliance
• Restricted access and traceability
• Advanced security protocols

Your data is protected and processed under strict conditions.

A long-term partner for your AI projects

By choosing us, you gain a partner capable of supporting you over time, adapting to your evolving ambitions. Whether your goal is to:

• Automate an industrial process
• Analyze video streams
• Recognize products
• Detect anomalies

Integrate computer vision into a commercial product

…we provide the expertise, tools, and teams needed to ensure sustainable success.

With our deep expertise, industrial capacity, mastery of 2D/3D annotation, and ROI-focused vision, we are the ideal partner to deploy high-performance, scalable, production-ready computer vision solutions.

We ensure the success of your computer vision projects by structuring high-quality datasets designed to cover the full range of visual variations your models will encounter. We implement highly precise 2D/3D annotation workflows based on detailed guidelines, professional tools, and multi-level quality control capable of detecting even the smallest discrepancies or label noise. To optimize performance, we integrate advanced training pipelines that use expert metrics such as IoU, mAP, F1-score, and class-wise recall to accurately evaluate model behavior in every scenario.

We also apply targeted data augmentation techniques that simulate real-world conditions encountered in computer vision: complex geometric transformations, photometric variations, occlusion generation, controlled noise, and specific manipulations for 3D point clouds. In each iteration cycle, we rigorously analyze false positives, false negatives, and areas of uncertainty to adjust the data and strengthen model robustness.

Thanks to our MLOps architecture, we ensure dataset versioning, modification traceability, automated testing, production monitoring, and continuous model redeployment. This approach guarantees the stability, scalability, and reliability of your computer vision solutions, even in demanding industrial environments. By combining technical expertise, operational capacity, and continuous improvement, we provide a comprehensive and sustainable solution to achieve the highest possible performance in computer vision