High-Precision Data Annotation Services for Production AI & Computer Vision
Computer vision models in production require pixel-accurate ground truth, especially on edge cases and complex sensor feeds. Obraz’s data annotation services cover polygon segmentation and 3D LiDAR point clouds to video multi-object tracking and sensor fusion, our in-house specialists deliver verified datasets with zero outsourced crowdsourcing.
Discover our Custom Computer Vision Solutions to see how we engineer, train, and deploy models from these datasets directly to edge devices and cloud infrastructure.
NO-Third party, secure data annotation:
From Model From Model something to something
Over 80% of computer vision models fail in production not because of model architecture, but due to inconsistent, noisy ground-truth training data. Our in-house data annotation services eliminate data pipeline fragmentation at the root.
100% In-House Dedicated Workforce
Every bounding box, polygon contour, and point cloud is labeled by trained, background-verified specialists under strict NDAs. We never crowdsource to anonymous gig workers, guaranteeing consistent labeling taxonomy and absolute data integrity.
Sub-Pixel Engineering Precision
Production computer vision demands extreme accuracy on difficult edge cases, occlusion, low-contrast imagery, and micro-defects. Our specialists follow rigorous standard operating procedures calibrated for complex real-world environments.
Enterprise Security & Data Custody
Work occurs on managed, internet-restricted workstations where external downloads, USB drives, screenshots, and clipboard copying are blocked. We maintain complete cryptographic audit logs and provide certified data destruction—the level of control enterprise data annotation services demand for sensitive, regulated projects.
Core Annotation Modalities &
Sensor Streams Expertise
Supporting diverse visual data types and sensor streams through our Computer Vision Data Annotation Services, backed by tailored tooling and sub-pixel accuracy.
2D & 3D Bounding Boxes (Cuboids)
High-throughput 2D bounding boxes and 3D spatial cuboids capturing object position, orientation, depth, and spatial dimensions. Essential for autonomous vehicles, inventory tracking, traffic monitoring, and retail analytics.
Polygon & Semantic Segmentation
Sub-pixel contour tracing for irregular shapes, biological samples, aerial vegetation, and industrial defect detection. Full semantic segmentation (class-level) and instance segmentation (individual entity separation).
Video Annotation & Multi-Object Tracking
Persistent object ID tracking across complex multi-frame video sequences with occlusion handling, camera motion compensation, event timestamping, and keyframe interpolation.
Keypoint & Landmark Annotation
Precise skeletal joint tracking, facial landmark mapping, human pose estimation, gesture recognition, and ergonomic movement analysis for sports, healthcare, and human-machine interaction.
3D LiDAR & Point Cloud Annotation
High-density 3D LiDAR point cloud semantic classification, ground plane segmentation, and 3D bounding box placement for robotics, autonomous navigation, and digital twin reconstruction.
Sensor Fusion & Multimodal GIS
Synchronized multi-sensor labeling combining RGB camera frames, Thermal/Infrared (IR), LiDAR, Radar, and IMU/GPS telemetry. Includes geospatial orthomosaics and satellite SAR imagery mapped to Coordinate Reference Systems (WGS84, UTM).
Why Companies Choose OBRAZ
for Data annotation service
Your training data may contain proprietary products, facilities, infrastructure, customer environments, medical imagery, operational footage, or defense-related visual information.
For sensitive projects, where data handling matters as much as annotation quality, Obraz’s in-house data annotation services provide greater control over the annotation environment than a typical data annotation outsourcing company can offer.
SECURE IN-HOUSE ANNOTATION
Obraz performs data annotation through LabelOps, our proprietary in-house annotation platform, rather than depending on external annotation SaaS platforms for core workflows. This gives us greater control over how project data is accessed, processed, reviewed, and managed throughout the annotation lifecycle.
NO UNNECESSARY THIRD-PARTY DEPENDENCIES
Sensitive Computer Vision datasets can contain proprietary products, facilities, infrastructure, surveillance footage, medical imagery, and other confidential information. Our in-house annotation workflow is designed to minimize unnecessary third-party involvement, keeping annotation operations within the Obraz environment and reducing additional points of exposure.
QUALITY-CONTROLLED DATASET DEVELOPMENT
A Computer Vision model is only as reliable as the data used to train it. As a provider of data annotation services for machine learning, Our workflows incorporate project-specific labeling guidelines, defined classes, review processes, and quality checks to improve consistency across datasets and ensure the resulting training data is suitable for the intended Computer Vision application.
Domain-Specific Solutions
Different Computer Vision applications require different annotation strategies. Whether the dataset involves defense imagery, manufacturing defects, medical images, drone footage, surveillance video, logistics environments, or infrastructure, we structure annotation workflows around the objects, classes, attributes, edge cases, and visual conditions relevant to the project
ANNOTATION TO MODEL DEVELOPMENT
Annotation doesn't operate as an isolated production task at Obraz. Our data annotation services connect with the broader Computer Vision development pipeline, allowing annotation requirements, dataset issues, and model performance observations to inform subsequent iterations of the training data. This creates a continuous loop between annotation, model development, validation, and improvement.
Frequently
asked
questions
01What are data annotation services?
Data annotation services involve labeling images, videos, and other datasets so that Machine Learning and Computer Vision models can learn to identify objects, patterns, categories, regions, or events. Obraz provides secure data annotation services for Computer Vision applications, including image annotation, video annotation, object detection, segmentation, classification, tracking, and OCR-related labeling.
02What types of data annotation does Obraz provide?
Depending on project requirements, Obraz can support annotation workflows such as bounding box annotation, polygon annotation, image classification, semantic segmentation, instance segmentation, keypoint annotation, object tracking, video annotation, and OCR annotation.
Only list modalities and annotation types that your actual delivery team supports.
03How does Obraz keep annotated data secure?
Obraz uses controlled internal annotation workflows and its proprietary LabelOps annotation platform to manage annotation operations. Access to project data can be controlled according to defined project requirements, helping reduce unnecessary exposure of sensitive visual datasets during the annotation process.
04Does Obraz use third-party data annotation platforms?
Obraz uses LabelOps, its proprietary in-house annotation platform, for its annotation workflows rather than depending on external annotation SaaS platforms for core annotation operations. This gives Obraz greater control over the annotation environment and how project data is handled.
05Does Obraz outsource data annotation to third-party vendors?
Obraz’s annotation model is built around its in-house annotation workflow and internal team, minimizing unnecessary third-party involvement. For sensitive projects, the applicable personnel, infrastructure, access controls, and data-handling requirements should be defined during project scoping.
This wording is safer than making an absolute “we never use any third party under any circumstance” claim unless your company has formally committed to that.
06What is LabelOps?
LabelOps is Obraz’s proprietary in-house data annotation platform used to support Computer Vision annotation workflows. It provides the working environment for annotation operations and helps connect data labeling with the broader Computer Vision development lifecycle.
07How does data annotation quality affect Computer Vision model performance?
Training data quality directly affects the ability of a Computer Vision model to learn the intended visual patterns. Inconsistent labels, missing objects, ambiguous classes, and annotation errors can introduce noise into a dataset. Obraz uses project-specific annotation guidelines and quality-control workflows to improve consistency and prepare datasets for model development.
08Can Obraz annotate sensitive defense, industrial, healthcare, or surveillance data?
Obraz can support sensitive Computer Vision datasets subject to project requirements, applicable regulations, contractual obligations, and security controls. Potential applications include defense imagery, industrial inspection data, surveillance video, medical imagery, drone footage, infrastructure imagery, and logistics datasets.
09Can Obraz handle large-scale Computer Vision datasets?
Annotation capacity and workflow design can be scaled according to project volume, annotation complexity, required turnaround time, quality requirements, and available resources. Large datasets can be organized into defined annotation, review, quality-control, and dataset-delivery stages.
10Can annotation be integrated directly into Computer Vision model development?
Yes. Obraz connects data annotation, dataset preparation, and Computer Vision model development within its broader engineering workflow. Annotation results can be used to create training datasets, while model evaluation can identify additional data or labeling requirements for subsequent iterations.
11What industries use secure data annotation services?
Secure data annotation can support Computer Vision projects across defense, aerospace, manufacturing, healthcare, logistics, retail, security and surveillance, maritime, automotive, agriculture, energy, construction, mining, and public infrastructure.
12How can professional data annotation improve AI project ROI?
High-quality annotation can reduce the time spent correcting training-data problems, improve dataset consistency, reduce rework during model development, and help teams reach usable model performance more efficiently. The business impact depends on the model, dataset, quality requirements, and operational use case.
Ready To Build Your Next
Computer Vision System?
Whether you’re solving operational challenges, automating visual workflows, or deploying mission-critical AI solutions,
Obraz’s enterprise data annotation services and computer vision expertise take you from concept to production with confidence.