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

Benefits of
Twine
AI

Six benefits of working with Twine AI

Quality

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.

Scalability

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.

Data Security

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

All Media

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

Here’s the steps to start working together

1

Project Planning

Establish priorities and define deliverables with your Twine Project Manager. They'll work to develop a custom solution to meet your project objectives and timeline.
2

Production

We vet and onboard collectors of your preferred demographics, set them the collection task, and pay them securely once you've approved.
3

Delivery

Training data is packaged and formatted to your specification, then shared for your final approval.

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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Contact Us