Olion Data is an experienced data annotation and AI data services provider delivering high quality training datasets for computer vision, autonomous systems, and large language models. We support startups, research teams, and enterprise AI programs with scalable, accurate, and production ready annotation pipelines.
Our mission is to accelerate AI development by providing reliable, human curated data through a skilled global workforce and proven quality assurance frameworks.
We specialize in 2D and 3D annotation, including image and video bounding boxes, polygon segmentation, keypoints, LiDAR point cloud labeling, BEV annotation, 3D cuboids, semantic segmentation, and multimodal NLP and LLM data preparation. Our team also supports transcription, content moderation, data validation, and conversational AI training.
Olion Data operates with a remote first model, led from Kenya and supported by a trained workforce of over 500 annotators and reviewers. Our annotation leads bring hands on experience from platforms and tools such as CVAT, Labelbox, Dataloop, V7, X Annotate, Basic AI, SegmAI, Deepen AI, Ango AI, Uber AI tooling, Avride tools, and custom client environments. We follow structured QA workflows including multi layer review, consensus validation, and sampling audits to ensure consistent accuracy across large scale projects.
We primarily serve industries including autonomous driving, robotics, geospatial mapping, retail analytics, healthcare imaging, and generative AI.
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Security
Security Overview
Olion Data maintains strict security and privacy standards to protect client data across all projects, particularly within sensitive industries such as autonomous driving, robotics, geospatial mapping, healthcare imaging, retail analytics, logistics, and generative AI.
We operate under signed NDAs and enforce role based access controls to ensure only authorized personnel handle client datasets. All annotators are trained on data confidentiality, secure handling procedures, and client specific compliance requirements. Access to platforms and tools is managed through individual credentials, and project environments follow least privilege principles.
Our workforce follows secure device policies, including password protected systems, controlled data downloads, and restricted external storage usage. Internal quality assurance workflows apply multi layer reviews and audit sampling while maintaining data isolation between projects.
We support projects across autonomous systems, robotics, smart cities, healthcare imaging, retail analytics, logistics, agriculture technology, security and surveillance, and large language model development, ensuring data integrity from ingestion through delivery.
Olion Data is committed to maintaining confidentiality, operational transparency, and high quality standards, positioning us as a dependable long term data partner for AI driven organizations.
Labeling Experience
Retail Image and Video Annotation for SKU Recognition, Price Tagging, and Shelf Analytics
Internal Proprietary ToolingImageBounding Box
This project focused on retail image and video annotation to support AI models for shelf analytics, product recognition, and pricing intelligence. The scope included bounding box and polygon labeling of SKUs, price tags, promotional labels, and shelf structures, along with classification of products by brand, size, and category.
Additional tasks included detecting missing products, identifying out of stock items, planogram compliance checks, and relationship labeling between products and price tags. The team annotated thousands of retail images and video frames captured in supermarkets and convenience stores.
This project focused on retail image and video annotation to support AI models for shelf analytics, product recognition, and pricing intelligence. The scope included bounding box and polygon labeling of SKUs, price tags, promotional labels, and shelf structures, along with classification of products by brand, size, and category.
Additional tasks included detecting missing products, identifying out of stock items, planogram compliance checks, and relationship labeling between products and price tags. The team annotated thousands of retail images and video frames captured in supermarkets and convenience stores.
2025
Vehicle Image and Video Annotation for Traffic Intelligence and Computer Vision Models
DataloopImageBounding Box
This project focused on image and video annotation for vehicle perception systems supporting autonomous driving and traffic analytics. The scope included bounding box and polygon labeling of cars, buses, trucks, motorcycles, cyclists, and pedestrians, along with multi object tracking, lane segmentation, and attribute classification across urban and highway environments.
The team processed tens of thousands of images and video frames, ensuring temporal consistency and accurate object identities. Quality assurance followed multi layer workflows including peer reviews, supervisor audits, inter annotator agreement checks, and random sampling to maintain high precision and guideline compliance.
Project Team Size
This project focused on image and video annotation for vehicle perception systems supporting autonomous driving and traffic analytics. The scope included bounding box and polygon labeling of cars, buses, trucks, motorcycles, cyclists, and pedestrians, along with multi object tracking, lane segmentation, and attribute classification across urban and highway environments.
The team processed tens of thousands of images and video frames, ensuring temporal consistency and accurate object identities. Quality assurance followed multi layer workflows including peer reviews, supervisor audits, inter annotator agreement checks, and random sampling to maintain high precision and guideline compliance.
Project Team Size
2025 - 2025
Agriculture Image and Video Annotation for Crop Monitoring and Land Analysis
V7 LabsImageBounding BoxPolygon
This project focused on agriculture focused image and video annotation to support AI models for crop monitoring, land cover mapping, and yield prediction. The scope included polygon segmentation of crop fields, bounding box labeling of plants and farm equipment, vegetation classification, and temporal tracking across drone and satellite imagery.
Datasets covered diverse agricultural environments including row crops, orchards, and mixed farmland. The team labeled crop types, growth stages, weed presence, irrigation patterns, and soil boundaries while maintaining spatial accuracy and temporal consistency.
The project processed tens of thousands of images and video frames. Quality assurance followed structured workflows including peer reviews, supervisor audits, inter annotator agreement checks, and random sampling to ensure high accuracy and guideline compliance.
This project focused on agriculture focused image and video annotation to support AI models for crop monitoring, land cover mapping, and yield prediction. The scope included polygon segmentation of crop fields, bounding box labeling of plants and farm equipment, vegetation classification, and temporal tracking across drone and satellite imagery.
Datasets covered diverse agricultural environments including row crops, orchards, and mixed farmland. The team labeled crop types, growth stages, weed presence, irrigation patterns, and soil boundaries while maintaining spatial accuracy and temporal consistency.
The project processed tens of thousands of images and video frames. Quality assurance followed structured workflows including peer reviews, supervisor audits, inter annotator agreement checks, and random sampling to ensure high accuracy and guideline compliance.
2025 - 2025
Large Scale 2D Computer Vision and Multimodal AI Data Annotation Project
CVATImageBounding BoxPolygon
This project involved large scale 2D image and video annotation combined with multimodal AI training data preparation for computer vision and generative AI applications. The scope included bounding boxes, polygon segmentation, keypoints, object tracking, action recognition, and attribute classification across diverse datasets such as retail imagery, robotics environments, and real world scenes.
In parallel, the team supported LLM workflows including text classification, prompt response writing (SFT), evaluation and rating, and conversational dataset creation.
The project processed millions of assets across multiple client programs. Quality was maintained through structured onboarding, clear annotation guidelines, peer review cycles, lead level audits, and random sampling. Continuous feedback loops and performance dashboards ensured consistency, accuracy, and delivery at scale.
This project involved large scale 2D image and video annotation combined with multimodal AI training data preparation for computer vision and generative AI applications. The scope included bounding boxes, polygon segmentation, keypoints, object tracking, action recognition, and attribute classification across diverse datasets such as retail imagery, robotics environments, and real world scenes.
In parallel, the team supported LLM workflows including text classification, prompt response writing (SFT), evaluation and rating, and conversational dataset creation.
The project processed millions of assets across multiple client programs. Quality was maintained through structured onboarding, clear annotation guidelines, peer review cycles, lead level audits, and random sampling. Continuous feedback loops and performance dashboards ensured consistency, accuracy, and delivery at scale.
2024 - 2025
Autonomous Driving LiDAR 3D Annotation and Object Detection Project
Scale AI3D SensorSegmentationCuboid
This project focused on large scale LiDAR and multi sensor data annotation for autonomous driving and ADAS model development. The scope included 3D point cloud labeling, BEV annotation, cuboid creation, semantic segmentation, and frame to frame object tracking across complex urban and highway environments.
Our team annotated vehicles, pedestrians, cyclists, traffic infrastructure, and static obstacles using Scale AI tooling, ensuring precise spatial alignment and attribute consistency. Tasks also involved point cloud fusion, occlusion handling, lane and drivable area segmentation, and relationship labeling between dynamic objects.
The project processed hundreds of thousands of frames across multiple scenarios, supporting perception, prediction, and planning pipelines. Quality assurance followed multi layer review workflows, including peer validation, lead audits, and random sampling to maintain high annotation accuracy and guideline compliance.
Project Team Size
This project focused on large scale LiDAR and multi sensor data annotation for autonomous driving and ADAS model development. The scope included 3D point cloud labeling, BEV annotation, cuboid creation, semantic segmentation, and frame to frame object tracking across complex urban and highway environments.
Our team annotated vehicles, pedestrians, cyclists, traffic infrastructure, and static obstacles using Scale AI tooling, ensuring precise spatial alignment and attribute consistency. Tasks also involved point cloud fusion, occlusion handling, lane and drivable area segmentation, and relationship labeling between dynamic objects.
The project processed hundreds of thousands of frames across multiple scenarios, supporting perception, prediction, and planning pipelines. Quality assurance followed multi layer review workflows, including peer validation, lead audits, and random sampling to maintain high annotation accuracy and guideline compliance.
Project Team Size
