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Vietnam is amongst the top destinations for AI data processing services, providing top-notch data labeling, data collecting and data annotation work. With many favorable traits that can help businesses reduce costs as much as possible, we now have a whole ecosystem of the top Data Labeling companies in Vietnam.

If you are looking for a reliable AI data processing service provider in Vietnam, you can consider our list of top 10 data annotation companies.

You might want to know: Why is Auto Data Labeling the future?

 

Overview of data labeling companies in Vietnam

The demands for AI data processing services hit a record-high number as the world’s technology is revolving around AI-related technologies. To operate an AI model, one business might need thousands of training datasets. The increasing need for AI development and training data leads to the increasing needs for data collection, data annotation and data validation.

Since the dawn of AI and ML, there have been hundreds of companies founded just to handle data processing services (because the number needed is very high). The most mature market in this particular field is the US and China. However, as these countries move further towards AI development, the cost for operating an AI data processing hub gets higher and higher. In these countries, the workforce once dedicated to AI processing services now switch to other AI-related technologies.

To maintain a reliable and stable source of training datasets, AI development companies have to come to other countries for a better cost, and Vietnam is one of the most reasonably-priced destinations.

In Vietnam, the price for hiring and retaining talents is lower than that of China or the US. We also have a young and abundant workforce that can cover your needs for training data.

Our AI data processing services started to boom 6 years ago. And in only 6 years, a whole new ecosystem of the most prestigious and renowned AI data annotation companies are founded and still operating with great prospects:

• Lotus Quality Assurance
• DIGI-TEXX VIETNAM
• Sibai
• SANEI HYTECHS VIETNAM Co., Ltd.
• BEETSOFT Co., Ltd
• MP.BPO
• Vietnam Smart BPO (VSBPO)
• Kotwel
• OkLabel
• Vie-Partner

 

 

Details about top data labeling companies in Vietnam

Top data labeling companies in Vietnam can provide you with an array of different services to fulfill your needs in AI development and AI data processing.

 

Lotus Quality Assurance

Lotus Quality Assurance, as part of Lotus Group, was founded in 2016 with the start of a Testing and Quality Assurance company. As the company moves towards the newest technologies there are in the market, our BOD has come to the realization that AI data processing service holds great potential and prospects for further development. Indeed, since its foundation, Lotus QA has continuously worked with international clients in different data annotation, data annotation and data validation projects. Besides project-based work, Lotus QA has been a long-term partner of multiple clients, mostly in the automotive sector.

 

 

Especially, our annotators and QA engineers assure high-quality training data and annotated data with an average error rate of only 0.02%, which is very ideal for any annotation project.

Since the foundation of Lotus QA, data annotation has always been the key service offering for our clients. As we thrive in this area, we have been working with many kinds of data, ranging from image, text, voice from different sectors. These sectors are automotive, agriculture, construction, fashion, finance, etc.

 

DIGI-TEXX VIETNAM

DIGI-TEXX is a German IT- BPO company headquartered in Ho Chi Minh City, Vietnam since 2002, with 3 branches in Ho Chi Minh City and one office in Fukuoka, Japan. With 100% FDI from Germany, DIGI- TEXX is one of the pioneers in the Business Processing Outsourcing (BPO) industry in Vietnam. As a digital solution provider with a solid BPO background, we empower clients around the world from various industries to achieve business transformation and gain competitive advantages.

With more than 1000 employees, providing round-the-clock services, they guarantee service delivery excellence while ensuring compliance with industry-followed quality and security standards.

They have been consistently providing Outsourced Services and Digital Solutions for more than 19 years to international clients in various industries, that require:

• Document processing to save time and optimize cost.
• Digital solutions to replace paperwork with automation processes, such as Banking, Insurance, and Healthcare.

Besides, they also provide Customer Helpdesk services in fluent Vietnamese, Chinese, Japanese, and English for many E-commerce and trading platforms.

 

SIBAI VIETNAM

SIBAI VIETNAM was founded in 2020 with a dedicated team of more than 200 experienced annotators who can handle your most unstructured datasets. With competent staff who have worked on multiple projects, SIBAI VIETNAM can now carry out your data annotation project on multiple platforms with different data annotation tool, across all content types.

With the combination of human talents and AI, SIBAI VIETNAM thrives as one of the most successful data labeling companies in Vietnam. Our customers’ most complex labeling needs can be well handled and addressed.

 

 

 

With high-quality data labeling and data annotation services, SIBAI is to elevate your business growth. SIBAI VIETNAM has developed a talent pool of more than 200 well-trained annotators in diverse areas. With all combined, we can provide the most suitable solutions that you are looking for, anytime you need them.

Besides the usual data annotation service, SIBAI VIETNAM also focuses on content moderation solutions. SIBAI provides human-level accuracy that significantly moderates community-generated threats in image, video, text, and audio. SIBAI can help brands limit risk exposure and safeguard their online platforms from content that has been flagged as inappropriate or violating community guidelines.

 

SANEI HYTECHS VIETNAM Co., Ltd.

Established on 19th June 2015, SANEI HYTECHS VIETNAM Co., Ltd. is currently one of the best data labeling companies in Vietnam. With the association with Japanese branches and companies, Sanei has strong resources and a foundation for top-notch services. Their service offering includes:

• Software Development (Embedded software, third-party unit verification, software application on Windows, Android, iOS and Bluetooth, etc.)
• LSI Design (FPGA Design/Verification, Logic Dedsign/Verification), Ip Design/Verification)
• Annotation Center (Create, analyze and provide design/evaluation data toward Big Data processing, deep learning data creation of the Artificial intelligence development, BPO service)

SANEI HYTECHS VIETNAM Co., Ltd. is currently operating with small number of employees but it can stretch in scale if requested.

 

 

 

BEETSOFT Co., Ltd

Beetsoft is another stand-out name among the data labeling companies in Vietnam. With more than 5 years of experience working in IT Consultancy and outsourcing services, Beetsoft knows how to play a stellar role in honing the skills of professionals, assisting companies to achieve success in their operating fields. Based in Vietnam and Japan, Beetsoft focuses on providing services to these two markets. Especially in the data labeling and data annotation fields, Beetsoft stands out as it can provide high-quality projects thanks to international standards and a multi-layered QA system.

Beetsoft offers high-end services at competitive rates as our development and annotator team is based in Vietnam. The competitive price of Beetsoft is always accompanied by the best work there is, so their customers can rest assured of the quality.

 

MP.BPO

BPO.MP Co., Ltd. is the first BPO enterprise with the Vietnam-Japan joint venture model to provide Business Process Outsourcing services, including document digitization, data entry & processing data management, financial and accounting processing, content writing, translation-interpretation, image processing, document labeling, etc.

With the motto “Successful cooperation to overcome limits”, the company’s development goal is to combine the advantages of the two cultures of Vietnam – Japan, take advantage of the strengths of businesses of the two countries to provide the best services. MP.BPO promises to bring services of international quality for customers in Vietnam and around the world.

 

Vietnam Smart BPO (VSBPO)

Vietnam Smart BPO (VSBPO) is a brand under Free’t Planning Vietnam, a joint venture between Vietnam, Free’t Planning Japan and I-Corporation Japan. VSBPO takes pride in being a pioneer in the industry, and a leader in providing business process outsourcing (BPO) services in Vietnam. Their partner, Free’t Planning Japan, has 20+ years of experience in IT & BPO industries. Today, the total number of employees is 200+ across 3 countries (Japan, Vietnam, China).

With the vision of becoming the leading BPO company in Vietnam, VSBPO is to provide the best quality services at optimal cost to clients.

 

Kotwel

Kotwel is the emerging data service provider for artificial intelligence. Relying on its own data resources, technical advantages and rich data processing experience, since its establishment, Kotwel has provided high-quality data services to many technology companies and scientific research institutions worldwide.

 

 

Kotwel is committed to total customer satisfaction by providing consistently high-quality data & services that meet or exceed the expectations of our worldwide customers.

Their purpose remains to embrace the power of human ingenuity and technology to create value for your AI & Business Initiatives. Kotwel wants to enable enterprises globally with stellar quality data services by using the combination of advanced tools and human intelligence. Benefitting and creating an optimistic social change through employment.

By supporting the development of game-changing AI & Technology applications with cutting edge workforce solutions, Kotwel wants to become a global leader when it comes to solving your data needs.

 

Ikorn Solutions

As a leader in contemporary online trends, Ikorn Solutions has grown as a highly respected IT company and become a trusted partner of many large Korean firms since entering the IT outsourcing market in 2007. They specialize in software development and I.T. outsourcing services such as data labeling services that are comprehensive, integrated, and customized to suit individual business needs across industries.

Driven by a passion for technology, Ikorn strongly believes that quality integration and technological development are at the center of their business. Ikorn´s competitive advantages are a force to be proud of as an excellent pool of skilled resources recruited from the finest professional education institutions in the industry. In 2017, following 10 years of operation and great persistence in development, Ikorn Solutions took a consistent and rigorous approach to expand our outsourcing services into the automotive industry and began to seek new partners for the next phase of business. This move served to affirm, step by step, the company’s strong position in the software technology market.

 

Vie-Partner

In 2016, VP Studio was founded by a team of computer graphics artists, providing graphic and 2D/3D designs for movies and games productions.

After observing the similarities of working methods and logic between Computer Graphics and Data Annotation, they found that experienced graphic designers achieve a 30% higher annotation speed and accuracy than average.

With years of experience in graphics training, they founded Vie-Partner specializing in Data Annotation. The goal of Vie-Partner is to provide organizations with trustworthy labeling solutions while creating work chances for underprivileged youngsters in Vietnam, minimalize costs without compromising quality.

 

If you are looking for the high-quality data labeling services in Vietnam, contact Lotus QA for more information from experts:

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

Automated Data Labeling is a new feature that is currently being constantly mentioned among Data annotation trends, and some even deem it the solution for the time-consuming and resource-consuming casual manual annotation.

As the Manual Data Labeling – aka Manual Data Annotation takes hours to annotate one dataset, the Automated data labeling technology now proposes a simpler, faster and more advanced way of processing data, through the use of AI itself.

 

How we normally handle dataset

The most common and simplest approach to data labeling is, of course, a fully manual one. A human user is presented with a series of raw, unlabeled data (such as images or videos), and is tasked with labeling it according to a set of rules.

For example, when processing image data for machine learning, the most common types of annotations are classification tags, bounding boxes, polygon segmentation, and key points.

 

 

Classification tags, which are the easiest and cheapest annotation, may take as little as a few seconds whereas fine-grained polygon segmentation could take a few minutes per each instance of objects.

In order to calculate the impact of AI automation on data labeling times, let’s assume that it takes a user 10 seconds to draw a bounding box around an object, and select the object class from a given list.

In this case, provided with a typical dataset with 100,000 images and 5 objects per image, annotators would have to spend 1,500 man-hours to complete the annotation process. This eventually would cost approximately $10,000 just for data labeling. 

The price of $10.000 is only for data labeling. For annotation project managers, AI data processing takes more than that. To ensure the high quality of the training data, they are compelled to add other layers of quality control and quality assurance. This helps manually verify and review each piece of labeled data, but it would be very costly. Moreover, the quality control and quality assurance staff must be trained of the sample output so that they understand what is required in the outcome of the annotation projects, thereby increasing the labeling costs by about 10%.

 

 

Some annotation project managers might choose consensus-based quality control. By implementing this method, the whole annotation project goes through multiple annotations. The same piece of data is annotated multiple times, and the results are consolidated and compared for quality control purposes. With this method, the amount of time and money is proportional to the number of annotators working on the same task. Simply put, if you had three users label the same image three times, you would have to pay for all 3 annotations. 

All this is to emphasize that, the two most expensive steps in data labeling are:

• The data labeling itself
• Reviewing and verifying it for quality control. 

 

Looking at all the huge costs that it would take in an annotation project, many business leaders have turned into a less time-consuming and tedious solution, which is the auto annotation tool technology.

Thankfully, with the latest technologies in artificial intelligence and machine learning, automated data labeling, or auto annotation, is usable now. However, to create an effective and well-rounded auto annotation tool now, it even requires more training data and human input for correcting errors induced by the AI. Therefore, anyone has the naive attempt to entirely apply auto annotation tools, they have to be cognizant of the truth that the tools are not the one-size-fits-all solution.

 

The advantages of Automated Data Labeling

Automated data labeling is quite a new term in the field, but the technology advancement implementing and making it happen is developing with high speed, shown in the large number of tools on the market now. So what are auto data labeling and its benefits?

 

What’s automated data labeling?

Automatic labeling is a feature found in data annotation tools that apply artificial intelligence (AI) to enrich, annotate, or label a dataset. Tools with this feature augment the work of humans in the loop to save time and money on data labeling for machine learning.

 

 

Most tools allow you to load pre-annotated data into the tool. More advanced tools, which are evolving into platforms (e.g., tool plus Software Development Kit or SDK), allow you to leverage AI or bring your own algorithm to the tool to improve the data enrichment process by auto labeling data.

Other tools offer prediction models that suggest annotations so workers can validate them. Some features leverage embedded neural networks that can learn from every annotation made. All of these features can save time and resources for machine learning teams and will have a profound effect on data annotation workflows.

 

Outstanding benefits of automated data labeling

When working with organizations using tools to annotate images for machine learning, we find two optimal ways to apply auto labeling in data annotation workflow:

• Pre-annotate some or all of your dataset. Workers come behind the automation to review, correct, and complete the annotations. Automation cannot annotate everything; there will be exceptions and edge cases. It’s also far from perfect, so you must plan for people to make reviews and corrections as necessary.
• Reduce the amount of work sent to people. An auto-labeling model can assign a confidence level based on the use case, task difficulty, and other factors. It enriches the dataset with annotations, and sends annotations with lower confidence scores to a person for review or correction.

We’ve run time experiments, with one team using tools that have an automation feature versus another team that is manually annotating the same data. In some cases, we’ve seen auto labeling provide low-quality results which increase the amount of time required per annotation task. Other times, it has provided a helpful starting point and reduced task time.

 

 

In one image annotation experiment, auto labeling combined with human-powered review and improvements was 10% faster than the 100% manual labeling process. That time savings increased from 40% to 50% faster as the automation learned over time.

It also had a more than the five-pixel margin of error for vehicles and missed the objects that were farthest from the camera. As you can see in the image, an auto-labeling feature tagged a garbage bin as a person. It’s important to keep in mind that pre-annotation predictions are based on existing models and any misses in the auto labeling reflect the accuracy of those models.

Data annotation tools can include automation, also called auto labeling, such as Labelbox and Tagtog, which uses artificial intelligence to label data, and workers can confirm or correct those labels, saving time in the process.

While auto labeling is not perfect, it can provide a helpful starting point and reduce task time for data labelers.

 

 

Some tasks are ripe for pre-annotation. For example, if you use the example from our experiment, you could use pre-annotation to label images, and a team of data labelers can determine whether to resize or delete the labels, or bounding boxes.

This reduction of labeling time can be helpful for a team that needs to annotate images at pixel-level segmentation.

Our takeaway from the experiments is that applying auto labeling requires creativity. We find that our clients who use it successfully are willing to experiment, fail, and pivot their process as necessary.

As auto data labeling is one of the breakthroughs for a better outlook of the AI technology, specifically machine learning, we still have a lot to discover with this new term.

 

 

If you want to hear from our experts concerning the matter of Automated data labeling, please contact us for further details.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

 

Outsourcing the data labeling services to emerging BPO destinations like Vietnam, China, and India has become a recent trend. However, it is not easy to choose the most suitable data labeling outsourcing vendor among numerous companies. In this article, LQA will walk you through some advices to find the best vendor.

 

1. Prepare a clear project requirement

 

First of all, it is crucial to prepare a clear and detailed requirement which shows all of your expectations toward the final results. You should include the project overview, timeline and budget in your request. A good requirements should include:

– What data types annotators have to work with?
– What kind of annotations need to be done?
– Is it required to have expertise knowledge to label your data?
– The dataset need to be annotated with how much accuracy rate?
– How many files need to be annotated?
– What is the deadline for your project?
– How much can you spend on this project?

 

2. Must-have Criteria to Evaluate the vendors

 

After finalizing your requirements, you should evaluate the vendors with whom you will sign the contract. This stage is crucial since you don’t want to spend plenty of money to receive a poor-labored dataset. We suggest evaluating them based on their experience, quality, efficiency, security, and teammate.

 

Experience

 

While data labeling may often seem like a simple task, it does require great attention to detail and a special set of skills to execute efficiently and accurately on a large scale. You need to gain a solid understanding of how long each vendor has been working specifically in the data annotation space and how much experience their annotators have. To evaluate this, you can ask the vendor some questions about their years of experience, the domain they have worked with, and the annotation types. For example:

How many years of experience in data annotation do the vendors have?
Did they work with a project that requires special domain knowledge before?
Do the vendors provide the type of annotation that matches your requirements?

 

Quality

 

The data scientists often define the quality in datasets for model training by how precisely the labels are placed. However, it is not about labeling correctly one or two times, but it requires consistently accurate labeling. You can figure out the capability of providing high-quality labeled data of the vendors by checking:

The error rates of their previous annotation projects
How accurately placed were the labels
How often did the annotator properly tag each label?

 

Data Quality – 5 Essentials of AI Training Data Labeling Work

 

Efficiency

 

Annotation is more time-consuming than you imagine. For example, a 5-minute video will have an average of 24 frames in one sentence, which made up to 7200 images that need to be labeled. The longer time annotators spend labeling one image, the more hour required to complete the task. To estimate correctly how many man-hours requested to complete your project, you should check with the vendor:

How long did it take to place each label on average?
How long did it take to label each file on average?
How long did it take to execute quality checking on each file?

 

Team

 

Understanding the ability of your vendor annotation team is important as they are the ones who directly execute the project. The vendor should commit to providing you a well-trained team. Moreover, if you want to label text, you need to check if the labeling team can speak the language or not. Besides, confirm with your vendors whether they are ready to scale up or down the annotation team in a short period. Although you may estimate the amount of data to be labeled, your project size still can change over time.

 

Data Annotators: The secret weapon of AI development

 

 

3. Require a pilot project

 

A pilot project is an initial small-scale implementation that is used to prove the viability of a project idea. It enables you to manage the risk of a new project and analyze any deficiencies before substantial resources are committed.

If you ask the vendor to do a pilot project, you will need to choose some sample data from your dataset. You can start with a small amount containing various types of data (10-15 files, depending on the complexity of your dataset).

Remember to provide a detailed guideline for the demo so you can evaluate the vendor correctly. Last but not least, ask them how you can check the progress of the demo test. As a result, you can rate if their quality and performance tracking tools or processes satisfy your requirement or not.

 

We went along with all the set up you need to notice before signing any contract with a data labeling outsourcing vendor. Hopping that with this preparation, you can choose the most decent partner.

If you are shortlisting data labeling vendors, why don’t you include LQA in the list? We have many experiences of labeling data in various fields like healthcare, automotive, and e-commerce. Contact our experts to know more about our experience and previous projects.