AI needs many examples
They're often made by real humans

Do better data labeling

and  treat workers more fairly

Use Cases

Small video of the step where a data labeler selects an object of interest and the labeling tool automatically makes the delimitation around the object, even when the object has a complex form
Point & click

Start with image auto annotation

Precise labeling tools that works on even complicated objects. Negligible prior training needed. Same model with tuning works on different objects. Slash by 6x the time to annotate even when you include time to correct the delimitation

Small video of the step where a data labeler selects an object of interest and the labeling tool does not automatically make the correct delimitation around the object. The image labeler has then to click to correct the selection. The AI model then makes the correct delimitation. This is often semi-automatic labeling. The data labelers most of the time just click and validate. In a few cases, the data labeler has to correct the selection.
Delimit only when needed

Correct selection when needed

Human labelers correct only when it's needed. It's quick, intuitive and various tools are available such as polygons, brush... They can add directional vectors, tags on the whole image or attributes on specific selections,...

Another GIF that shows how annotators can quickly annotate text. In this case we show someone annotating entities and relationships
Text at speed

Better text labeling

So many use cases: Tag receipts with financial classification, categorize customer reviews, moderate hate speech... Assigning attributes and defining relationships between those attributes has never been so easy and fast for the workers. You'll understand why Stanford University, Google, Twitch and Twitter use this tool.

Tools & Platforms

Compare Quickly 150+ of them

Choose quickly and wisely

Take advantage of our list of partner tools and platforms built thanks to our experience. Quickly identify the ones best suited to your needs, with comprehensive pricing and client reference insight

Another Lottie file showing that there's many tools and platforms for data labeling and data annotation. With some tools the work goes up to 120% faster. With some platform, it's possible to find 20 data labelers in 2 days
Working with
Logo of datacamp, a partner with a great platform to learn about data. They're awesome to teach about data science, data cleaning, data preparation machine learning and artificial intelligence. They also have - courses on SQL, PowerBI, and Pandas
Logo of khan academy, another partner with a fantastic platform to learn about math and some Tech and IT skills such as JS, SQL.

Why us?

We're the only ones combining the following:
icon showing how scalable dataprep can be. Scalability is really at out core.
Scalable workforce

We help finding, onboarding
and coordinating workers.
20 workers in 2 days? No problem!

icon showcasing the efficient way dataprep handles things
Efficiency

New tools and approaches let you label the data often an order of magnitude faster and cheaper

icon explaining the great quality dataprep can have
Outstanding data quality

Work only with workers passing custom tests. Monitor quality with pre-answered tasks

icon illustrating that the pricing is transparent
Transparent pricing

Simple pricing per consulting day.
No hidden fees or lock-in

icon showing workers can be happy. A special part of dataprep is to avoid child labor and forced labor. But also empowering users with free trainings and sometimes even paid trainings
Helping workers

Option to work locally with deaf or mute workers. If offshore labor, we do real checks against forced or child labor

icon showing that dataprep only works with proven tools and platforms for data labeling and data annotation.
Proven platforms

Be guided to choose among the hundreds of existing platforms. Chose the proven one best answering your needs

Logo of MIT or M.I.T. Sloan. Next to it a quote of Andrew Ng, former Google Brain Head about data centric AI
" We should shift effort from AI models to the data in most cases "
MIT Sloan & Andrew Ng

Work experience

Also via past employers

Scaling workforces

For one of the largest IT consultancy and a startup

Data Science

for Insurance, Tourism, Beverage industry as well as the Belgium Federal Police

Data Viz and Business Analyst

for a large pension fund and a large energy multinational
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