Train your

with better data

We provide speech, image and video data collection, data annotation and labelling services tailored to your specific needs.
Data labeling and annotation services
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Trusted by leading generative AI teams, public companies, and startups

Our services

Whether you're building your own models or fine-tuning foundation models, trust Twine Al to get quality image data, audio datasets, video datasets, text data collected, aggregated, annotated, and delivered.
Data annotation and labeling services

Fine-tune your AI with better data

Your AI model, your way. Twine AI’s custom data collection and RLHF techniques allows you to adapt foundational models to your needs.
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Voice recording software audio datasets to train machine learning

Speed + scale of labeling + collection

Training data is often the source of bias, so leverage Twine AI’s crowd of 500,000 global experts to scale your datasets quickly and reduce model bias.
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Build the best foundation model

Twine AI partners with leading AI companies, to help them build the best AI models with unique data and we help companies meet the evolving AI regulations.
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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.
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Benefits of

How we can help you
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Humans in the Loop

Have model raters / annotators to give feedback directly to your model.

Say goodbye to spreadsheets, data leaks and quality control issues. We help to automate your workflows while ensuring the right task is assigned to the right person.
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Tagging + Labelling

Hire 100s of professional data labelers who care about accuracy. We manage the QA for you and ensure consensus on annotations.

Industries: automotive, e-commerce / retail, FMCG, healthcare, security, energy & more. Learn more.

Security & Payments

We take data security seriously: Your data will adhere to ISO 27001 standards and be GDPR compliant.

Grant permissions: Individual stages of your data flow, or restricted access to groups of labelers or collectors.

Global payments: Paying 1000s of data collectors and annotators  globally can be challenging. We solve this. 

Dataset Management

Build smarter: Consolidate your ML training data.

Version control: Create versions of all exports for data-driven model experiments. Sync dataset versions with models—iterate and optimize as you scale.

Audit trail and compliance: Track what data has been used on what model to ensure compliance with legislation.

Skilled + Scale

The Twine network consists 500,000 expert freelancers and consultants. Twine AI leverages this for data collection, annotation and consulting.

Skills include: data scientists, engineers, voiceover artists, videographers, actors, and 200+ more expert roles.

Diverse Global Network

With 500,000+ experts:

Professional on-demand annotators and data collectors paid ethically.

Domain experts for your collector or annotation.

Fully managed projects by the Twine  Projects team.
AI and ML

Feedback Loop

Your Project Manager will host weekly meetings, or at a schedule that works for you. They will get feedback on the collected / annotated data so we can optimise and improve the workflow.

With our monthly billing, if you’re unhappy, you can cancel anytime. We want this to be a success project!

Project Managed

Have your data collection or annotation project run by an experienced  Project Manager who can ensure all participants are following instructions and work with you to improve the collection process.

Off-the-shelf datasets

Audio-visual speech with multiple speakers
Large-scale audio-visual dataset comprising speech clips with no interfering background signals.
Audio-visual emotion recognition
These expressions are produced at two levels of emotional intensities (regular and strong) except for the neutral emotion that only contains regular intensity.
Instructional cooking videos
Each video contains some number of procedure steps to fulfill a recipe. All the procedure segments are temporal localized in the video with starting time and ending time. The distributions of 1) video duration, 2) number of recipe steps per video, 3) recipe segment duration and 4) number of words per sentence are shown below.

Why should I get ethical data?

Ethical data collection is become more important to data scientists


Bias in data collection is a distortion that results in information not being truly representative of the model you are attempting to build. Engineer your dataset to minimise bias.
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Consent means ensuring all participants agreed to provide the data and understand the implications. We will ensure all participants have given their consent.

Data Projection

Knowing where the data has come from is essential to ensure if all the checks and balances have taken place, and you could risk tainting your algorithm.

Customer case studies

We've worked with some of the world's leading AI startups and corporations.

Frequently asked questions

Still feeling unsure? More questions? These might help!
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.

What do you mean by customised data?

We mean that your dataset will be produced by participants according to your specific specifications, there are no generalised datasets here at Twine AI, unless you specifically want one.

What is ethical data collection?

By securing clear and informed consent from participants for use of their data, and giving insight into how this data might be used, we ensure ethical data collection.

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.

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.

How is the data delivered?

You determine the mode and speed of delivery. We will provide instructions to each participant regarding the type or recording you require and what metadata needs to be provided, if it needs to be provided. We instruct contributors based on your specification.

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.

How much does it cost and how do we pay?

The cost is based on the number of unique participants you require and how much work they need need to do.

We work with clients on a rolling monthly subscription, so you cancel or pause at any point. Payments can be made by credit card or invoice depending on the size of the project.

Do you have examples?

We can create samples if you contact us. We also have our collation of over 100 voice and visual open datasets.

Contact us

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Other Twine AI Services

Audio datasets:
We create speech datasets across demographics including gender, language, location, dialect, accent, and age. We can also use professional voice actors with professional recording studios. Learn more.

Video datasets:
We can build long-range biometric video datasets or close range facial or emotion datasets. We help you reduce bias by recruiting demographics to reduce bias in your data such as gender, ethnicity, age, and facial distinctions (eye colour, glasses, etc). Learn more.

Data Processing:
Outsource tasks at scale using Twine AI. If you’d like to use our transcription services, we can provide that too.
We’ve worked with some the leading AI companies in the world and seen what works and what doesn’t.

We can hand you over to our Twine team to help recruit consultants and experts in engineering, marketing and creative aspects,

Other AI resources:
Our Twine Blog has its own AI category. We have an article on 100+ Open Audio and Video Datasets, 100+ Speech Dataset. Also follow our Twine AI LinkedIn page for the latest news in the AI/ML space.