Outsourced Data Annotation | A Way To Cut Costs & Boost Accuracy

By Shah Nur
Published in Blog
January 09, 2024
6 min read
Outsourced Data Annotation | A Way To Cut Costs & Boost Accuracy

Data annotation is an essential aspect of machine learning. Why? Because the precision and efficiency of the models rely on how good and consistent the annotated data is.

But, you know, data annotation can take up a lot of time and resources. It’s something people can only do if you have some serious skills and expertise. This is where we come in. Co-one is a gateway for data based on AI and crowdsourcing that facilitates the growth of AI teams.

If you are looking for a way to minimize costs, we can help. We specialize in delivering accurate data efficiently, making us the missing piece between your business and success.

Read on and discover how we can help you transform the drawbacks of in-house data labeling into significant benefits.

What is Data Annotation? The Cornerstone of Artificial Intelligence

According to the McKinsey Institute, AI has the potential to generate an additional $13 trillion in global economic activity by 2030. This represents a 16% increase in cumulative GDP compared to current levels.

To achieve this, AI & ML models need to be fed plenty of data. Data is what makes these two systems work effectively. For a computer to interpret data and make decisions, it needs to be given instructions. It may also require some background information to process and analyze the data effectively. This is where data annotation comes into play.

Annotating data means putting labels or tags on it. This makes it easier for computers to understand and learn from. Now, let’s discuss why annotating data is the most significant aspect of teaching artificial intelligence models.

  • Accuracy: To develop and enhance accurate AI models, you need reliable information. This makes the process smooth and effective.

  • Efficacy: The greatest benefit of automated data labeling is the time and resources it saves. If you have some easy tasks, you can allocate them to AI models. This helps you focus on other tasks.

In-house vs. Outsourcing Data Labeling: A Comparative Analysis

What is the difference between in-house and outsourcing data labeling?

Well, in-house labeling is when a business labels its data. It achieves this by assigning tasks to its data scientists and employees. In contrast, outsourcing labeling involves enlisting the services of a third-party platform to do the tasks for you.

Businesses have a strong desire to enhance their machine-learning algorithms. This is primarily because AI models require a substantial amount of labeled data before they can be deployed successfully. But first, they have to decide. Should they build an in-house team? Or should they work with a renowned contract partner? Which option is considered good for business? Let’s find out.

Time Requirements

Labeling data in-house can be time-consuming. This is because you have a team that needs a lot of training in the methods, tools, and procedures used. Businesses can bypass the training process and save time through outsourcing.


It can cost a lot to label data in-house. This is because you need to get the right tools and train your team. Most of the time, outsourcing costs less. This is because you don’t have to buy as many tools or hire as many data annotators to do labeling work.


Comparing the quality of in-house and outsourced options can be challenging. This is because data labeling service providers may specialize in distinct areas.

Well, the good news is that Co-one can be your tailor-made data service provider with its crowdsourced team of data labelers specialized in more than 10 industries.

Scalability and Flexibility

For simple jobs that don’t need much, having an in-house data labeling team might be enough. But if your project has precise and difficult needs, it’s best to find providers to label your data so that you can be able to adapt.


Labeling data in-house has the advantage of preventing it from being shared with outsiders. This makes it the safest method available. However, outsourcing to platforms with the right qualifications or licensing can provide you with high security. In the end, this can keep your data safe.

As Co-one, we pledge to protect the security of your data, protect the privacy of your personal information, and be open and honest about our business operations.

Our company undertakes to keep confidential information strictly private and confidential, to ensure and maintain confidentiality, to take all necessary measures, and to show the necessary care to prevent unauthorized use of all or any part of confidential information or disclosure to a third party.

Here are the vital tests and certificates we strictly follow to keep your data safe:

  • GDPR Compliant

  • ISO 27001 & 9001

  • Data Encryption

  • Penetration Safe

Please read more about the privacy, security, terms, and GDPR.

Turning Disadvantages into Advantages with Co-one

Artificial intelligence consists of a crucial element that is giving data names. It is challenging, takes time, and can be quite expensive to do in-house. Using Co-one’s services allows you to turn your problems into big opportunities. With that said, let’s look at how you can turn your fears about labeling into real strengths:

User-friendly dashboard and Dedicated Project Managers

Working with an in-house data tagging team may waste resources and time. Since the need for information changes over time, the outcome may change, creating inequality in production. The end goal for these damages the business in the long run.

So how can Co-one help your business? To keep track of your projects, our easy-to-use dashboard makes your management easy and checks the state of every stage in the procedure for annotation.

Exceptionally High Data Accuracy Rates

Annotating data is fundamental, but you shouldn’t just add it to the long list of things your workers need to do. A lot is at stake. It can take a long time to label a lot of data, or even worse, this important job might not be done at all. Individual labeling may differ in their approach or make mistakes due to fatigue or differing viewpoints when there are tight deadlines.

How can Co-one help in such a situation? They work with purpose-built data annotation tools, which are accustomed to processing high levels of data accuracy. People worldwide know that Co-one’s data tagging services are very accurate. Our team of highly trained, knowledgeable, and tested annotators ensures your data is labeled correctly and regularly.

For AI projects to run smoothly, accuracy is essential. When you outsource data labeling to Co-one, you can expect high-quality data since multiple quality checks and controls are built as part of the process. This reduces the risk of costly mistakes.

Vast Network of Crowdsource Users

When a company is dealing with in-house annotators, it takes more time to ensure that each staff member is well-trained for the job. Experienced trainers and annotators do these kinds of training.

For a training program to be planned and to start running, more time and resources need to be allocated. For this reason, the sole purpose of resources and duties of employees changes to suit the goal of training new annotators.

Working with us is easy because we have done the hard work. Our vast network of over 8000 registered crowdsource users empowers us to generate the precise data you need to drive your projects forward.

Our experts are skilled and can help you with your projects whenever needed. Using our network, you can get precise and detailed data that you can manipulate to move your AI projects forward.

Why Choose Co-one?

Co-one has proven to be the best partner for AI teams for these reasons;

Fast Data Services

In the fast-paced world of AI, speed is one of the most important aspects. For this reason, Co-one has ensured that their data services are quick, and there is no need for you to keep on labeling data.

Our large network of annotations and effective methods ensures that all AI projects that you entrust to us move swiftly, putting you at the top of the game in the AI field.

High Accuracy Data Sets

Since we are dealing with data most of the time, getting things right is the biggest step to achieving success in all our projects. Our annotators at Co-one ensure that the data used is regularly labeled correctly. In this case, you are assured of using the least time possible to get maximum results, ensuring your AI models do the best work.

Transparent Data Processes

Being open and honest at all times is one of Co-one’s guiding principles. The way our platform is set up is for you to access info as quickly as possible and keep constant track of your projects. This way you can see how we work and the entire process in which your project goes through. There are open loads of contacts for you to stay in touch any time you feel like.

Secure Data & Information Processing Environment

Co-one offers you a safe place to store your data and work. Your info is safe and there is no need to worry if you entrust Co-one with your data. There are constant measures in place to protect data and a safe infrastructure that is constantly updated.

We want to achieve reduced to zero mistakes on AI projects since you will have more control over your project. Since we know how important it is to achieve the best goals as soon as possible, we are ready and capable of partnering with you in this fast-paced field of artificial intelligence.


When you have a group of highly skilled professionals working only on your data labeling project, you can rest assured that it will be completed accurately and on time.

Our expertise in handling a wide variety of data sets, combined with our data labeling abilities, allows us to safely provide improved data labeling for ML and AI projects.

If you have the chance to change the way you annotate data for your AI projects, make sure you take it. If you are ready, visit the Co-one Data Annotation Management Platform. Fuel your AI with Co-one. Talk to our experts about your data labeling needs today!


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Shah Nur

Shah Nur

Product Owner

Table Of Contents

What is Data Annotation? The Cornerstone of Artificial Intelligence
In-house vs. Outsourcing Data Labeling: A Comparative Analysis
Turning Disadvantages into Advantages with Co-one
Why Choose Co-one?

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