THE CHALLENGE
Good AI Needs More Than Raw Data
Raw data rarely arrives ready for model training.
Different formats, unclear boundaries, inconsistent decisions, and difficult edge cases can make annotation harder to manage and harder to keep consistent at scale.
ANNOTATION CAPABILITIES
Annotation Across the Data Your Models Need
From images and video to text, audio, 3D, and multimodal data, we build annotation workflows around what your models need to learn.
Image Annotation
Label and structure visual data using bounding boxes, polygons, keypoints, segmentation, and other annotation methods for computer vision models.
Video Annotation
Track objects, actions, and events across video frames to create consistent training datasets for computer vision and video understanding models.
Text Annotation
Turn unstructured language into labeled datasets for NLP and language models using classification, entity labeling, and sentiment analysis.
Audio Annotation
Transcribe, segment, and label audio data for speech and language applications, supporting training datasets for speech recognition and understanding models.
3D Annotation
Annotate 3D data with labels and structures needed for perception, object detection, spatial understanding, and other computer vision applications.
LiDAR Annotation
Label LiDAR point clouds for perception, object detection, segmentation, and spatial understanding across autonomous systems and computer vision applications.
Multimodal Annotation
Combine data types and annotation methods to create structured datasets for models handling complex inputs and real-world environments.
Custom Annotation Workflows
Build annotation workflows around your data, requirements, models, and quality standards, with processes designed for specific AI development needs.
WORKFLOW
From Raw Data to Training-Ready Data
We start by understanding your requirements, prepare the data, annotate it, check the results, and deliver a dataset ready for the next stage of your AI workflow.
QUALITY
Built Into Every Annotation
Good annotation quality requires more than a final review.
We build quality checks into the workflow so issues can be found and corrected before the dataset moves forward.
Quality Flow :
01 - Annotation Guidelines
Define clear instructions and decision criteria.
02 - Automated Checks
Find inconsistencies and potential issues early.
03 - Human Review
Review cases that require context or judgment.
04 - Issue Detection
Identify gaps, duplicates, and annotation problems.
05 - Correction
Resolve identified issues before delivery.
06 - Validation
Confirm that the required quality standards have been met.
DOMAIN EXPERTISE
Some annotation tasks are straightforward. Others depend on terminology, context, or domain-specific decisions. Our workflows can be shaped around those requirements.
Annotation That Fits Your Data and Its Context
Healthcare
Manufacturing
Automotive
Technology
Retail
Security
Aviation
Transport
More
SECURITY
We control how data is accessed, stored, handled, and delivered throughout the annotation process.
Keep Your Data Controlled Throughout the Workflow
Controlled Access
Access is limited to authorized teams and workflows.
Secure Handling
Data is handled according to defined operational requirements.
Confidential Workflows
Confidentiality remains part of the annotation process.
Controlled Delivery
Prepared datasets are delivered through defined workflows.
TOOLS & PLATFORMS
We work with the tools and platforms your team already uses, or set up new environments when needed. Integration is designed to be smooth, not disruptive.
Works With the Tools Your Team Already Uses
AI WORKFLOW
Fits Into the AI Workflow You Already Use
Annotated data needs to move smoothly into the next stage of your AI workflow. We prepare datasets for model training, evaluation, and other downstream uses.
The goal is simple:
produce annotated data that works within the workflow around your model.
AI + HUMAN
Automation handles repetitive work at scale. People step in when context, ambiguity, or domain knowledge matters.
The Right Balance of Automation and Human Judgment
WHY ANOTAG
Built Around the Way Your Team Works
Every AI project has different requirements. We can support project-based delivery, dedicated teams, managed workflows, or longer-term programs.
Human Expertise
People remain part of the process where context, judgment, and domain understanding matter.
Structured Workflows
Defined processes make complex annotation work easier to manage and repeat across projects.
Flexible Delivery
Choose project-based delivery, dedicated teams, managed workflows, or longer-term programs.
Scalable Operations
Extend annotation capacity as your datasets, projects, and evolving requirements continue to grow.
Long-Term Support
Keep the workflow aligned as your models, datasets, and annotation requirements change.
Security & Trust
Data access, handling, confidentiality, and controlled workflows remain part of the way we work.
FAQ
Questions About Data Annotation?
Find answers about annotation methods, data quality, scalability, costs, and preparing training data for AI models.