Outsource image annotation and labeling to Bogner & Partners. Bounding boxes, polygons, segmentation, and keypoint annotation for computer vision models. Scalable teams in Kenya, GDPR compliant.
Computer vision models are only as good as the training data behind them. Object detection, classification, segmentation — every computer vision model needs large volumes of accurately annotated images. Bogner & Partners provides dedicated image annotation teams from our Nairobi facility, trained on your specific guidelines and quality standards.
Our annotators are proficient across all major image annotation techniques:
Rectangular boxes drawn around objects of interest. The most common annotation method for object detection tasks. Used in autonomous driving (vehicles, pedestrians), retail (product detection), security (person/object identification), and manufacturing (defect detection).
Precise outlines traced around irregularly shaped objects. Delivers higher accuracy than bounding boxes for objects with non-rectangular shapes. Common in medical imaging, agriculture (crop and weed identification), and geospatial analysis.
Pixel-level classification where every pixel in an image is assigned to a category. Essential for scene understanding applications including autonomous navigation, medical image analysis, and land use classification from satellite imagery.
Combining pixel-level accuracy with individual object distinction. Each object instance is separately identified and segmented, enabling models to distinguish between overlapping objects of the same class. Used in robotics, warehouse automation, and advanced manufacturing.
Placing specific landmark points on objects — facial features, body joints, hand positions, or structural features. Critical for pose estimation, gesture recognition, facial analysis, and motion tracking applications.
Assigning category labels to entire images. Used for content moderation, product categorization, quality inspection, and scene classification.
Our image annotation teams support clients across diverse sectors:
Image annotation quality directly affects model performance. Our QA process includes:
We work with your preferred annotation platform, including Labelbox, CVAT, V7, Supervisely, Label Studio, Scale AI, and custom tools.
No need to change your processes. We become a seamless extension of your team.
Contact UsNo guessing games. No overhead. Just one flat, all-inclusive monthly fee.
Your team is operational in 30 days. Your dedicated account manager keeps you informed at every step.
We uncover your specific needs, goals, and benchmarks in a 60-minute strategy call.
We deliver a strategic document combining your process analysis, KPIs, and custom SOPs.
We hand-pick your team from our pre-vetted pool of university-educated talent.
We conduct rigorous training on your specific workflows, culture, and software stack.
We run mock scenarios and live operational tests to ensure zero friction at launch.
Your fully managed team is operational. We monitor closely and optimize continuously.
Enjoy the cost benefits of offshoring with the legal protection of a German partner
The cost benefits of outsourcing, backed by the safety of a German-registered entity and GDPR compliance. You sign a standard B2B contract under German law. Zero legal risk.
Enterprise-grade data security protocols. Your data is handled with the same rigor expected by European regulators.
We are not a faceless agency. Our European leadership team is physically present in Nairobi to ensure punctuality and quality.
It depends on annotation type and complexity. Simple bounding boxes on clear objects cost less than pixel-level semantic segmentation on medical images. Our annotators start at EUR 4.55 per hour, and throughput varies --- a trained annotator can label 200--500 bounding boxes per hour but only 15--30 polygon annotations per hour on complex shapes. We quote per-image or per-hour rates after reviewing a sample of your data.
We target 95--98% annotation accuracy depending on task complexity, measured through inter-annotator agreement and QA sampling. Every annotation passes a multi-level review: annotator, senior annotator, then QA lead. We also run automated validation checks for format compliance and spatial consistency. If accuracy drops below the agreed threshold on any batch, we rework it at no extra cost.
Yes. We work directly within your preferred annotation platform, including Labelbox, CVAT, V7, Label Studio, Supervisely, and Scale AI. Our annotators log into your workspace, follow your existing taxonomy and label definitions, and deliver annotations in your native format. No data migration or tool switching required.
We deploy a trained annotation team within 30 days. That includes annotator recruitment, qualification testing on your specific data type, and onboarding to your guidelines and tools. For urgent projects, we can assign pre-qualified annotators from our existing pool within 1--2 weeks. Teams scale from 5 annotators for pilot batches to 50+ for production volumes of millions of images.
We train annotators specifically on your medical imaging guidelines --- whether that is radiology (X-rays, CT, MRI), pathology slides, or retinal scans. Annotators complete domain-specific qualification tests using your reference data before touching production images. For clinical-grade datasets, we implement double-blind annotation where two annotators label independently and a QA lead resolves disagreements. We are GDPR compliant and ISO 27001 certified for handling sensitive patient data.
