Accurate data labeling for AI + machine learning.

We provide high quality, fully flexible data annotation services designed around the needs of your project.
Data labeling and annotation services
Trusted by leading generative AI teams, public companies, and startups

Benefits of

Brand designers


Highest-quality annotation of text, images, audio and video data for complex models. Ideal for computer vision, sentiment analysis, entity linking, text categorization, and syntactic parsing and tagging models.


Twine specializes in data annotation of all types of documents & formats for any industry, no matter how complex or large the task. From complex documents to dense images, our experts precisely tag the data you need to train your models.
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Data Security

We’re equipped to handle the most sensitive and highly regulated data.
Video and animation

All Media

Wide range of data types, including images, videos, facial recognition, satellite photos and drones.

Here's what our customers say

"Working with Twine enabled us to scale projects quicker than before."
-Josh Bolland
CEO, J B Cole
"Working with Twine AI has been an exceptional experience. Their ability to consistently deliver data and the level of service, professionalism, and dedication to understanding our needs set them apart."
-Ian Sherwin
Head of Data, Hypersurfaces
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5 star rating
108 reviews

How we work


Technical Meeting

Scope your full data collection or data annotation project.

Proof of Concept

Delivery of initial proof of concept to prove feasibility.

Full Project Delivery

Dedicated Twine Projects team will manage delivery with flexible monthly billing and QA services.
Book a meeting

Frequently asked questions

Still feeling unsure? More questions? These might help!
What is data labeling?

Data labeling refers to the annotation process of adding tags or labels to raw data such as images, videos, text, and audio.

These tags form a representation of what class of objects the data belongs to and helps a machine learning model learn to identify that particular class of objects when encountered in data without a tag.

What is “training data” in machine learning?

Training data refers to data that has been collected to be fed to a machine learning model to help the model learn more about the data.

Training data can be of various forms, including images, voice, text, or features depending on the machine learning model being used and the task at hand to be solved.It can be annotated or unannotated.

When training data is annotated, the corresponding label is referred to as ground truth.

Why is data labeling important?

Before an AI system can identify images or analyze text on its own, it must be “trained” with hand-labeled examples. In the case of self-driving cars, that means manually labeling millions of images and videos.

Let’s imagine you want to train a sentiment analysis model. You’ll need to feed the AI model labeled examples (or “training data”) of positive, negative, and neutral sentiment. And beyond that, you’ll need to include sometimes ambiguous phrases that demonstrate human language at its most complex level, like sarcasm and irony – some of the most difficult sentiments for a machine, or even humans, to detect.

Good quality training data is key to determining the success of AI tools. It must be relevant, free from noise (like errors, duplicates, and irrelevant data) and it must be labeled correctly. Get your training data and labels in order and you’ll be able to rely on this information to improve your products, services, and everyday processes.

What can I expect from my Project Manager?

Our experienced team takes your project specifications and creates custom procedures designed to maximise success. Your Project Manager is responsible for running the project: writing out the labeling instructions, ensuring the labeling quality is consistent and sourcing expert labelers.

They will be your point person for updates and the achievement of milestones.

What types of labeling can you provide?

Highest-quality annotation of text, images, audio and video data for complex models. Ideal for computer vision, sentiment analysis, entity linking, text categorization, and syntactic parsing and tagging models.

Images | Videos | Object Recognition | Facial Recognition | Satellite Photos | Drone | Vehicle and Traffic | Driving

Do you have labeling examples?

Not yet but watch this space for more soon! We do have our collation of over 100 voice and visual open datasets.

Headshot photos of example portfolios
Example of an active campaign
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Need to build datasets?

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Datasets for speech recognition
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Audio scene analysis
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Single person or multi-person conversation content
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Multi-language capabilities
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Datasets for object tracking or detection
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Human action recognition and biometics
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Human facial recognition
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Drone video datasets
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