AI Data Services
Your Models Need Data They Can Trust.
CapeStart prepares, labels, annotates, and validates the data your AI and machine learning models depend on. Our integrated team of analysts and subject-matter experts delivers accurate, quality-checked datasets across text, image, audio, and video, at the scale your project needs.
AI Models Need the Right Data to Work
Machine learning models learn from the data you give them. When that data is incomplete, inconsistent, or poorly labeled, accuracy suffers, and no amount of tuning fully makes up for it. Preparing and annotating large, high-quality datasets is slow, specialized work, and it is rarely the best use of your team's time.
CapeStart is the annotation partner that turns your raw data into clean, accurately labeled datasets your models can learn from.
Two Ways We Prepare Data for Your Models
CapeStart supports both general AI data annotation and specialized medical image annotation, with workflows built around your data type, model requirements, and quality standards.
Data Annotation Services
Image, text, document, video, and audio annotation across the modalities your models need, from bounding boxes and semantic segmentation to named entity recognition, transcription, and document fields.
Medical Image Annotation
Specialized clinical annotation by a team that includes radiologists and radiologic technologists, across CT, MRI, X-ray, mammography, ultrasound, and ophthalmic images, including OCT and Fundus images.
Structured Data Workflows, Built Around Your Model Requirements
CapeStart does more than label data. We build the annotation workflow around your model objective, data type, quality requirements, and review process. From the first sample to final delivery, our teams define clear labeling rules, calibrate reviewers, run quality checks, and keep feedback built into the workflow.
01
Data and Requirement Review
We review your sample data, model objective, annotation scope, labeling requirements, validation needs, and preferred delivery format.
02
Guideline and Workflow Setup
We define annotation rules, reviewer instructions, quality criteria, escalation paths, and project documentation so the team works from a consistent standard.
03
Sample Annotation and Calibration
We annotate a sample dataset for your review, then use your feedback to refine labeling parameters, instructions, and quality expectations before scaling.
04
Scaled Annotation and Quality Control
Our trained analysts and subject-matter experts scale the annotation workflow with multi-level quality checks for accuracy, consistency, and completeness.
05
Delivery and Sign-Off
We deliver quality-checked datasets in the required format, support review and sign-off, and adjust the workflow if additional batches, edge cases, or model feedback require refinement.
Why Capestart
Annotation Built for Model Performance
Structured Workflows for AI-Ready Data
Our annotation and validation workflows are designed to deliver quality-checked datasets in formats that fit your machine learning, AI training, and evaluation pipelines.
Workflow
In-house annotation and validation workflows built around project-specific guidelines, reviewer instructions, and escalation paths
Modalities
Quality
Multi-level review, validation, calibration, and sign-off processes
Exports
JSON, CSV, and project-specific formats ready for downstream ML workflows
Security
Controlled access, confidentiality practices, and secure handling of sensitive data
Don’t See Your Workflow?
We align annotation, review, delivery, and export formats to the way your model team works.
Questions Teams Ask Before They Start
How do you make sure annotation quality is consistent across large datasets?
We start with clear labeling guidelines, reviewer training, and sample calibration. As the project scales, multi-level quality checks help maintain consistency, accuracy, and reliability across the dataset.
Can you work with complex or domain-specific data?
Yes. CapeStart works with trained analysts and subject-matter experts for projects that require domain understanding, including healthcare, financial services, telecom, retail, legal, and medical imaging use cases.
Can we review quality before starting a full project?
Yes. You can share a sample dataset and your requirements. We annotate a sample at no cost, so your team can review the quality, provide feedback, and confirm the approach before scaling.
What types of data can CapeStart annotate?
We annotate text, documents, images, audio, and video. This includes bounding boxes, polygons, segmentation, named entity recognition, document fields, transcription, speaker labels, event tags, and timestamps.
Do you support medical image annotation?
Yes. Our medical image annotation work is supported by a team that includes radiologists and radiologic technologists across CT, MRI, X-ray, mammography, ultrasound, OCT, and Fundus images.
How do you protect sensitive data during annotation?
We use controlled workflows, access management, confidentiality practices, and security protocols to help protect sensitive data throughout the project.
What formats do you deliver the final datasets in?
We deliver quality-checked datasets in the formats your models and workflows require, including JSON, CSV, and project-specific formats.
Can you scale from a small pilot to a large annotation program?
Yes. Many projects begin with a sample or pilot dataset. Once the labeling rules and quality expectations are confirmed, we scale the team and workflow to handle larger volumes while maintaining review and quality-control steps.
Ready to Train Your Models on Better Data?
Share your sample data and requirements with us. We will prepare a small sample annotation, so you can review the quality before moving forward.
Talk to Our Experts
Tell us what you’re trying to achieve, and we’ll get back to you.