What is Sama?
Formerly known as Samasource, Sama specializes in the critical domain of data annotation and training-data services for artificial intelligence. The company operates at the intersection of technology and social impact, providing professional skills development and educational support alongside its core business of preparing datasets for complex AI models. Its client roster includes industry titans such as Google, Microsoft, Walmart, and General Motors, cementing its status as a trusted partner in the AI supply chain. Headquartered in San Francisco, Sama maintains a sophisticated global footprint with delivery centers across East Africa and strategic partnerships in India, enabling it to provide scalable, high-fidelity data solutions that drive algorithmic accuracy and performance.
How much funding has Sama raised?
Sama has raised a total of $84.8M across 2 funding rounds:
Series A
$14.8M
Series B
$70M
Series A (2019): $14.8M with participation from Ridge Ventures
Series B (2021): $70M led by Caisse de dépôt et placement du Québec
Key Investors in Sama
Caisse de dépôt et placement du Québec
A prominent institutional investor managing public and private pension and insurance funds, focusing on long-term value creation and sustainable growth.
Ridge Ventures
An early-stage venture capital firm that partners with software-focused founders to achieve product-market fit and sustainable revenue growth.
What's next for Sama?
Looking ahead, Sama is poised to capitalize on the accelerating adoption of generative AI and computer vision technologies. The recent strategic investment will likely be deployed to enhance the company's proprietary annotation platforms and further automate workflows, ensuring that it remains competitive in an increasingly crowded market. As enterprise demand for specialized, high-quality training data continues to surge, Sama's focus on professional development and ethical data sourcing provides a distinct competitive advantage. The company is expected to prioritize scaling its operational efficiency while maintaining the rigorous quality standards required by its enterprise-level clientele, potentially exploring new frontiers in automated data labeling and synthetic data integration to maintain its market leadership.
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