Computer vision services are end-to-end data services that train AI models to interpret images and video — including image annotation, video annotation, semantic segmentation, 3D point cloud labelling, and dataset collection. Shaip delivers these services through a managed workforce of 500,000+ trained contributors across 60+ countries, with HIPAA, SOC 2, and ISO 27001 controls.
The recent developments in Computer Vision technologies have overcome some of the limitations that humans face in accurately detecting and labeling objects from the vast amounts of data generated today from disparate systems. The computer effectively solves these 3 tasks:
Training ML models to interpret & comprehend the visual world requires large volumes of accurately labeled image and video data.
From bounding boxes, semantic segmentation, polygons, polylines to keypoint annotation we can help you with any image/video annotation technique.
We also offer a skilled resource that becomes an extension of your team to support you with your data annotation tasks, through tools that you prefer while maintaining the desired consistency and quality. Our skilled and experienced workforce apply the best practices learned by labeling millions of images & videos to deliver world-class data labeling for computer vision solutions.
From image/video collection to annotation object recognition and tracking to semantic segmentation and 3-D point cloud annotations, we bring a greater understanding of the visual world with detailed, accurately labeled images and videos to improve the performance of your computer vision models.
450k images of driver faces with car setup in different poses and variations covering 20,000 unique participants from 10+ ethnicities
80k+ images of landmarks from over 40 countries, collected based on custom requirement.
84.5k drone videos of areas like College/School campus, Factory site, Playground, Street, Vegetable Market with GPS details.
55k images in 50+ variations (w.r.t. food type, lighting, indoor vs outdoor, background, camera distance etc.) with annotated images
Annotation of X-rays, CT, MRI, ultrasound, pathology slides, and dental imagery — with HIPAA-controlled workflows and clinician-led review.
Face recognition data, weapon and threat detection, crowd analytics, and licence-plate datasets with documented consent and ethics review.
Satellite, aerial, and drone imagery annotation — land use, infrastructure, agriculture monitoring, and disaster response.
Egocentric video, hand-object interaction, manipulation, and warehouse / factory perception data for VLA and robotics foundation models.
Multiple cameras capture videos from a different angle to identify the boundaries of traffic signals, roads, cars, objects, and pedestrians nearby to train the self-driving cars to auto steer the vehicle and avoid hitting obstacles while driving the passenger safely.
Product attribute tagging, shelf detection, visual search, and try-on imagery for personalisation and inventory automation.
As experts in training and managing teams, we ensure projects are delivered within the defined budget.
The team analyzes data from multiple sources & is capable of producing AI-training data efficiently and in volumes across all industries.
The wide gamut of image data provides AI with copious amounts of information needed to train faster.
Our pool of experts who are proficient in image/video annotation and labeling can procure accurate and effectively annotated datasets.
Our team helps you prepare image/video data for training AI engines, saving valuable time & resources.
Our team of collaborators can accommodate additional volume while maintaining the quality of data output.
Computer vision is all about making sense of the visual world to train computer vision applications. Its success completely boils down to what we call image annotation – the fundamental process behind the technology that makes machines make intelligent decisions and this is exactly what we are about to discuss and explore.
Today, we are at the dawn of the next-generation mechanism, where our faces are our pass codes. Through the recognition of unique facial features, machines can detect if the person trying to access a device is authorized, match CCTV footage with actual images to track felons & defaulters, reduce crime in retail stores, and more.
Human beings have the innate ability to distinguish and precisely identify objects, people, animals, and places from photographs. However, computers don’t come with the capability to classify images. Yet, they can be trained to interpret visual information using computer vision applications and image recognition technology.
Dedicated and trained teams:
Highest process efficiency is assured with:
The patented platform offers benefits:
Empowering teams to build world-leading AI products.
Computer vision services help AI teams train models to interpret images, videos, and sensor-based visual data. These services typically include image annotation, video annotation, object detection, semantic segmentation, 3D point cloud labeling, dataset collection, and quality-managed annotation workflows. Shaip provides computer vision services to help enterprises build high-quality training datasets for production AI models.
Computer vision is a branch of artificial intelligence that enables machines to understand and analyze visual data such as images, videos, medical scans, satellite imagery, retail photos, or autonomous driving footage. It allows AI models to detect objects, classify scenes, recognize patterns, track movement, and make decisions based on visual inputs.
Computer vision works by training machine learning and deep learning models on labeled visual datasets. Human annotators label objects, regions, attributes, keypoints, or pixels in images and videos so the model can learn visual patterns. Once trained, the model can identify, classify, segment, or track objects in new visual data.
Shaip offers image annotation services including bounding boxes, polygons, polylines, keypoints, semantic segmentation, instance segmentation, panoptic segmentation, 3D cuboids, image classification, and 3D point cloud annotation. These annotation types support use cases such as object detection, facial landmark annotation, autonomous driving, medical imaging, retail visual search, and robotics AI.
Common computer vision annotation techniques include bounding boxes for object detection, polygons for irregular object boundaries, semantic segmentation for pixel-level labeling, instance segmentation for separating individual objects, keypoints for pose or landmark detection, 3D cuboids for spatial object labeling, and polylines for lanes, roads, tracks, or boundaries.
Yes. Shaip can customize computer vision datasets based on project requirements such as geography, environment, camera angle, lighting condition, object class, demographic mix, annotation taxonomy, image format, video frame rate, metadata fields, and delivery schema. Custom dataset design helps improve model relevance, accuracy, and real-world performance.
The amount of labeled data needed depends on the model type, use case, object complexity, number of classes, and performance target. A baseline model may start with thousands of labeled images per class, while production-grade computer vision models often require tens of thousands or more examples across varied lighting, angles, backgrounds, and edge cases.
Shaip supports computer vision projects across healthcare and medical imaging, autonomous vehicles and ADAS, robotics and physical AI, retail and e-commerce, geospatial and UAV imaging, agriculture, security and surveillance, insurance, smart cities, and industrial AI. Each industry requires domain-specific annotation guidelines, QA workflows, and expert review.
Computer vision is used in autonomous vehicles for obstacle detection, healthcare for medical image analysis, retail for visual search and product tagging, manufacturing for defect detection, agriculture for crop monitoring, security for surveillance analytics, insurance for damage assessment, and robotics for object recognition, navigation, and task execution.
Shaip uses structured quality workflows, reviewer calibration, project-specific guidelines, quality checks, and human-in-the-loop review to maintain annotation accuracy. Projects typically begin with a pilot batch to validate taxonomy, edge-case rules, acceptance criteria, and reviewer alignment before scaling to full production annotation.
Shaip supports secure handling of sensitive data through privacy, compliance, and access-control workflows. For regulated projects, Shaip can support de-identification, NDA-bound teams, controlled access, auditability, secure cloud delivery, and compliance-aligned processes for standards such as HIPAA, GDPR, ISO 27001, ISO 9001, and SOC 2.
Computer vision project timelines depend on data volume, annotation complexity, number of object classes, QA depth, tool setup, and review cycles. Pilot batches often help define throughput and quality benchmarks before full production. Large enterprise projects are commonly delivered in phased batches with continuous feedback and quality reporting.
The cost of computer vision services depends on the data type, annotation method, project volume, object complexity, number of classes, QA requirements, domain expertise, security needs, and turnaround time. Shaip scopes pricing based on the required workflow, pilot results, delivery format, and production scale.
Shaip helps enterprises build production-ready computer vision datasets through scalable data collection, image and video annotation, 3D annotation, human-in-the-loop quality review, and compliance-focused delivery. With experience across healthcare, autonomous systems, retail, robotics, and other AI use cases, Shaip supports complex visual AI projects from pilot to production.
