Question 1

How many years has Sigma.ai been in business?

Accepted Answer

Sigma was founded by Daniel Tapias and Nuria Gómez Bermejo in 2008 in Madrid, Spain.

Question 2

Where is Sigma located?

Accepted Answer

Our European headquarters is in Madrid, Spain at Avenida de Burgos, 114; First Floor, Madrid, Spain 28050. We also have a London office at 26-28 Hammersmith Grove, Seventh Floor, London, W6 7BA. Our North American headquarters is at 1101 Brickell Ave., Suite N-1400, Miami, FL 33131.

Question 3

What does Sigma.AI do?

Accepted Answer

Sigma.AI is a global training data collection, preparation, and annotation services company. We solve one of the biggest problems in AI: high-quality, accurate training data at scale.

Question 4

What types of annotation does Sigma support?

Accepted Answer

Sigma supports audio, image, video and text annotation.

Question 5

What languages does Sigma support?

Accepted Answer

Sigma supports over 500 languages and dialects. Please contact us with your specific needs — we are continually adding to the languages we serve.

Question 6

What tools does Sigma use for annotation?

Accepted Answer

We are flexible in what tools are used for annotation. Our proprietary annotation platform can be configured to your needs, or we can use your tool of choice.

Question 7

What is Sigma's pricing model?

Accepted Answer

We use two pricing models: hourly-based and unit-based. The model is driven by client preferences and type of data annotation.

Question 8

How does the quantity of data and task quantity impact cost?

Accepted Answer

The larger the volume of data, the lower the cost. There are fixed costs common to all projects; with larger volumes, the impact of fixed costs is lower and the cost per hour or per unit is lower too. Larger volumes also allow labelers, QA, and project managers time to reach and maintain production peak. The number of tasks reduces costs because larger quantities of data reduce fixed costs, allow us to reuse workflows and experience, simplify communication with clients, and let us optimize labeler performance and quality through strategies like balancing cognitive load.

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