Image captioning
Create descriptive captions for images to support visual understanding, multimodal AI training, and image-to-text applications.
Image annotation
Precise visual labels for computer vision systems.
Turn raw imagery into structured training and evaluation data through classification, detection, segmentation, keypoints, captions and quality review.
What we annotate
Visual structure your models can recognize.
We align annotation methods with target objects, edge cases, model requirements and measurable quality criteria.
Keypoint annotation
Mark specific points on objects, faces, bodies, or products to support pose estimation, facial recognition, gesture tracking, and object structure analysis.
Object detection
Identify and label multiple objects within an image so AI models can recognize, locate, and distinguish different visual elements.
Semantic segmentation
Label each pixel or region in an image by category to help models understand object boundaries, scene structure, and visual context.
Polygon annotation
Outline objects with precise polygon shapes to capture irregular boundaries and improve pixel-level object detection accuracy.
Image evaluation
Review image quality, label accuracy, visual relevance, and annotation consistency to ensure datasets meet project requirements.
Image classification
Assign category labels to entire images to help machine learning models recognize image types, scenes, objects, or visual patterns.
Bounding boxes
Draw rectangular labels around objects in images to help computer vision models detect and locate target items accurately.
Built for your use case
Annotation methods matched to the vision task.
We select the right label geometry, granularity and review workflow for the model behavior you need.
01Detection & localization
02Segmentation & scene understanding
03Pose & keypoint models
04Multimodal vision-language AI
How we deliver
From ontology to validated visual labels.
A calibrated workflow protects boundary accuracy, class consistency and coverage across every batch.
Define the ontology
Align classes, geometry, examples, edge cases, volume and acceptance criteria.
Train & calibrate
Qualify reviewers through practice batches and visual guideline feedback.
Annotate & review
Run controlled production with consensus, audits and senior review.
Deliver & report
Provide structured annotations with quality findings and issue documentation.
Start focused
Plan an image annotation pilot.
Share your target classes, sample images and quality targets. We’ll propose a focused annotation plan with clear review criteria.
Discuss your image annotation project ↗