High-quality data annotation services

Get balanced, representative, domain-specific training data with up to 99.99% of accuracy to tackle even the most complex challenges and GenAI use cases.

99.99%

Accuracy
available, 98%
guaranteed

Services

Supporting each stage of the data preparation process, with a human-centered approach

25000

Professionals

25000

Professionals

Human-in-the-loop for LLMs

Training Large Language Models (LLMs) requires massive amounts of data. Data annotation is particularly relevant in this context, as it allows you to train, fine-tune, and evaluate LLMs so they can perform specific tasks or learn about a particular domain. 

At Sigma.AI, we pair you with a diverse, continuously trained workforce of over 25,000 professionals to support the specific needs of your industry and use cases.

Our human-in-the-loop approach for LLMs leverages human intelligence, judgment, and creativity in order to add further context to the data and enhance AI’s performance.

These are the main tasks that our team of linguists and annotators support:

Data strategy

We evaluate your unique project needs and design a tailored, efficient data preparation strategy to ensure high quality training data at scale.

Finding a team of annotators with the skill sets and expertise that best suit your use case
Defining well-structured guidelines for annotators, to improve consistency and reduce ambiguity when labeling data
Assessing the quality of your database and providing continuous feedback loops during the annotation process
Recommending the optimal combination of annotation tools, helping you reduce hours of annotation time
Providing security and privacy advice based on your use case, industry, and compliance needs
Ensuring datasets are balanced & representative, and identifying situations prone to bias
Finding a team of annotators with the skill sets and expertise that best suit your use case
Defining well-structured guidelines for annotators, to improve consistency and reduce ambiguity when labeling data
Assessing the quality of your database and providing continuous feedback loops during the annotation process
Recommending the optimal combination of annotation tools, helping you reduce hours of annotation time
Providing security and privacy advice based on your use case, industry, and compliance needs
Ensuring datasets are balanced & representative, and identifying situations prone to bias

Data collection

Smart AI starts with high-quality data. We source, collect, & curate the data that best represents the conditions under which your AI model will be tested. If necessary, we augment your dataset with synthetic data.

Analyze existing data

We assess your dataset to ensure it meets these quality standards:

-> Domain coverage: the data accurately and fully covers the task domain the AI is supposed to perform.

-> User coverage: the dataset represents all users, avoiding biases based on gender, age, race, politics, religion, and more.

-> Balance: the data equally represents all areas of the domain and all users.

Collect new data

Our team sources and selects the data that best aligns to your use case. 

We focus on data quality, ensuring your dataset is accurate, relevant, and unbiased.

Curate data

Now it’s time to evaluate which data is valid, helpful, and relevant to train the model. 

We use customization curation tools to:

-> Clean, filter, and format the data
-> Remove outliers
-> Distill out relevant subsets (if needed)

Augment datasets with synthetic data

Sometimes it can be hard to source a complete and balanced dataset.

Missing values can lead to biased data and poor AI performance. 

Fortunately, we can overcome this problem by augmenting your existing dataset with synthetic data.

Synthetic data is artificially generated based on exact specifications. It can help improve coverage and balance by covering less frequent or random use cases. 
We can generate synthetic data for text, speech, and images.

Data labeling

Producing accurate, unbiased labeled data at scale is one of the main challenges when building complex machine learning models.

Our data labeling service gives you access to a curated, in-house global team of expert annotators, translators, and linguists with domain specific knowledge, to help you achieve your data annotation goals – no matter how complex they are.

We support various data labeling methods:

Get high quality, accurate data by working with a team specifically assembled for your project:

1.
Select a team of annotators with the skill sets that best suit your use case
2.
Define guidelines, requirements, deadlines & deliverables
3.
Setup Sigma’s platform and AI-assisted tools to your own needs
4.
Annotators collect data to test the tools & procedures, and share a report
5.
Client feedback & improvements (if needed)
6.
Annotators label data at scale and prepare it for quality assessment
1.Select a team of annotators with the skill sets that best suit your use case
2.Define guidelines, requirements, deadlines & deliverables
3.Setup Sigma’s platform and AI-assisted tools to your own needs
4.Annotators collect data to test the tools & procedures, and share a report
5.Client feedback & improvements (if needed)
6.Annotators label data at scale and prepare it for quality assessment

WHY SIGMA.AI

Work with a curated team of experts to get the exact data that your AI project needs

Data annotation services with 99.99% of accuracy available

500+ languages & dialects

Customized processes and tools to fit your unique business needs

Human-in-the-loop + automation: the best mix to train LLMs

30+ years of expertise in the data annotation space

25,000+ annotators, linguists & subject matter experts across 5 continents

ES