Sama

Sama labels image, video, and text data for AI teams using a managed workforce it hires and trains directly, built around an impact-sourcing model that pays fair wages and invests in workers in underserved regions.

You hand Sama the data and the quality bar. Sama staffs a vetted team, runs the labeling through its own platform with review built in, and delivers checked data back. Because the workforce is employed rather than gig-sourced, the same annotators stay on a project long enough to learn its edge cases, and there is real accountability when a label is wrong.

The company started in computer vision and still runs deep there:

• Image and video annotation, including bounding boxes and segmentation
• 2D and 3D labeling for autonomous driving and robotics
• LiDAR and sensor data for perception models
• Content and product categorization for retail and media

More recently Sama moved into generative AI work. That covers producing and rating data to fine-tune and evaluate language and multimodal models, plus Sama Red Team, a service for probing generative models for unsafe or unreliable behavior before they ship.

A platform sits under the service. Its automation pre-labels the easy cases so annotators spend their time on the hard ones, and quality tracking flags where the team disagrees so a reviewer can settle it. Teams that want the tooling without the workforce can use the platform on its own, though most come to Sama for the managed team.

Who uses it:

• Automotive and robotics teams labeling perception data at scale
• Retail and media companies categorizing large product and content sets
• AI labs producing fine-tuning and evaluation data for generative models
• Teams that want an accountable, ethically sourced workforce rather than an anonymous crowd

Pricing is a managed service, quoted per project by data type, volume, and turnaround.

Best for teams that want large-scale labeling handled by a stable, vetted workforce, especially in computer vision, and that care about how the people doing the work are treated. Not the right fit for a team that only wants a self-serve platform to run with its own annotators, where an annotation-platform vendor is a closer match.