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September 2, 2022

How to choose your outsourcing data annotation company for your AI projects?

September 2, 2022

The best approach to choosing your data annotation specialist

In recent years, the field of AI has advanced in leaps and bounds. To stay ahead of the curve, companies need access to the best technology has to offer: the best equipment, the best tools, and crucially, the best specialists. In data annotation especially, where expertise and skill count above all, the success of your project can hinge on whether or not you have the right expert on the job. How do you find the perfect specialist for your needs? Well, first you need to know exactly what data annotation is, and what a data annotation specialist does.

What is data annotation?

Data annotation is the backbone of machine learning models. By providing large quantities of tagged and labelled datasets, AI models can ‘learn’ to detect, recognise and understand various inputs (such as text, images or video) and come up with appropriate responses to those inputs. The accuracy of the AIs decisions increases over time as it is fed with more and more data. Clearly, large quantities of data are needed, and that's where your data annotation specialist comes in.

What does a data annotation specialist do?

Your data annotation specialist will, essentially, provide your system with the dataset it learns from. It takes a team of people to label, tag, categorise and validate the data the machines learn from. To have high quality data then, you need a data annotation specialist that is accurate, able to handle large volumes of data and is trustworthy security-wise, among many other characteristics. The advancements in the AI, machine learning and deep learning field means you will be spoilt for choice; to narrow your choice down, it will be prudent to first select one of the three main types of specialist services available.

In House Team

Hiring and forming your own in-house team of data annotation specialists may be a good option, especially if you require large amounts of control over the project. 


-Security is assured, since your own company will be producing and reviewing the data protection protocols.

-Better understanding of the needs of the project

-Consistency in annotation, since you rely on the same team every time


-More resources have to be devoted to human resources and team compensation

-Managing large scale projects requires more specialised skill than your company may have internally without third party support.


Outsourcing to data annotation experts relieves much of the burden from the company, allowing the professional to handle the heavy lifting. It is the best solution for speedy, cost effective results.


-Cost effective

-Time effective

-Pre existing infrastructure (facilities and services) 

-The benefit of professional expertise


-Unscrupulous companies may undermine the security of data

-Quality may not be assured

Data annotation platform

This type uses end-to-end labelling technology, combining the best of human effort and Ai, while allowing data scientists to use their time for other matters


-Task annotation is delegated to users of the platform


Cost effective


-Platforms are usually specific to certain industries

 What to look for in a Data Annotation Specialist

Now that you’ve narrowed exactly what type of specialist you need, there are many characteristics to consider before you settle on a final choice.

  1. Expertise- 

You must consider whether the company has the ability to do the job right to the highest degree of quality possible. Looking at examples of their past work, unbiased reviews from customers and the QA processes they have can give you the right idea about their data annotation expertise.

  1. Security-  

Important projects usually come with sensitive data. Most foward thinking companies are hesistant to entrust this data to outsiders without assurance of safe keeping, and rightly so. Compliance with international security standards can be easily verified. If your specialist has the requisite documents, such as an ISO 2700 certificate, you can proceed knowing your data is in secure hands.

  1. Speed- 

Each project has its specific timelines and generally,  the earlier the data annotation can be done the better. However high turnover rate may mean decreased quality, so you want to be able to discuss the full scale of the project and get a realistic timeline.

  1. Scalability-

The dynamic nature of the industry may mean the project may have to be scaled up or down in the midst of ongoing work. Your specialist should give you the flexibility to make these changes.

  1. Free trial-

 Sometimes, after checking all these factors it is still difficult to tell if a specialist service meets your needs. A good quality data annotation specialist will offer you a free trial to have a better idea of how they run projects.

Why choose Isahit as your data annotation specialist?

Isahit is an ethical data labelling company that uses crowdsourcing, giving a strong guarantee of quality and control. Since 2015, Isahit has worked with numerous teams, companies and data scientists, building up expertise for your specific project needs.

 Each team receives specialised training for each project, and quality control is integrated in the very fabric of the project. The technical infrastructure is state-of-the-art- the platform has the ability to adapt to the unique needs of your project. In terms of security, Isahit has the latest ISO certifications. As a customer, you will also be able to follow the progress of each project in real-time, retaining control of your own data.

Beyond all these qualifications, Isahit is about impact sourcing- socially responsible impact creation. Each contractor in their crowdsourced program is a young woman from a developing country being aided to become financially and digitally emancipated. If your company cares about their impact on the social world, Isahit is the data annotation specialist for you.

Around the world, the field of data annotation and labeling is becoming more and more significant. By 2027, it is anticipated that the global market for data annotation tools would amount to $2.57 billion.

Why is there a growing need for data annotation companies?

The advancement of technology has had a favorable impact on human life quality by improving the efficiency of our daily activities. Technology developments like artificial intelligence (AI) and machine learning (ML) are more important than ever, even in the corporate world. This is because they make tasks, no matter how difficult or routine they may be, simple to do. As a result, we may observe businesses gradually dipping their toes into the world of AI. As businesses employ AI to accelerate digital transformation and support business goals, more advancements are inevitable and will push the boundaries of what is technically possible.

Examples of data annotation companies

1. gives computer vision systems access to annotated image data, enabling robots to recognize images and classify objects into various categories. They provide machine learning picture annotation that renders each image easily recognizable for machines or computer vision using cutting-edge technology and human-powered talents.

2. Appen:

Appen recently acquired Figure Eight, a dispersed network of human annotators that provides high-quality data annotation services. Keeping all annotators under one roof generally makes sense since it fosters better interaction and ensures everyone is on the same page.

3. Amazon Mechanical Turk (MTurk):

Owned by Amazon, it should come as no surprise that MTurk enables businesses to access a sizable distributed workforce around-the-clock. Businesses can utilize MTurk to recruit lone workers to assist them with finishing particular tasks for their machine learning projects. Typically, they are straightforward tasks like text transcription or image labeling. MTurk is perfect for small-scale initiatives that don't need for annotating enormous amounts of data but rather call for jobs to be finished swiftly and affordably.

4. Playment:

Although it appears to be solely focused on the automotive sector, Playment provides a variety of data annotation services. Even so, a lot of significant corporations have faith in the business, which offers a thorough outline of the many data annotation tasks it is capable of handling. This is unusual in that not many businesses go into great detail about the kinds of data annotations they specialize in.

5. Hive:

Hive provides end-to-end data annotation solutions, but its use cases imply that it only caters a few industries.

6. Scale:

Because it offers managed labeling services through an application programming interface, Scale is a fascinating business. While Scale depends more on computers annotating the data, many other businesses place a greater emphasis on the human element. Additionally, it features a quality-control mechanism, which you should be aware of if you want to hire human data annotators.

7. Isahit:

Isahit is a platform for AI and data processing that ethically labels data. Isahit is an expert in data processing, natural language processing, and computer vision. You can always be guaranteed of receiving top quality results for your various annotation projects thanks to Isahit's team of properly qualified data annotators. You can check our site for examples of projects we've worked on.

Precautions when choosing a data annotation vendor

Before selecting a provider or partner for data annotation, there are a few questions you should ask.

1. How much information do you need them to process?

How varied of a data set do you require?

Is the information sensitive?

4. What precautions should the team take if the data is sensitive?

5. How soon do you require the annotations to be completed?

How significant is accuracy?

Do the annotators require any specialized knowledge?

When you have appropriate answers to these inquiries, you can begin the process of selecting a provider who will meet your needs.


For the development of autonomous vehicles, computer vision for aerial drones, and many other AI and robotics applications, properly annotated data is crucial. The intelligence of the AI-based model depends on the data it is fed; otherwise, it is useless. The secret lies in "proper training data," which consistently benefits computer vision and NLP models alike. By providing high-quality results, reputable data annotation companies can assist enterprises in exploring new business opportunities.

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