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Sama Launches Multimodal AI, Leveraging Diverse Data Types Alongside Human Intelligence for Next-Gen AI Models

Initial implementations have delivered 35% accuracy improvement and 10% reduction in product returns

SAN FRANCISCO, CA / ACCESS Newswire / June 4, 2025 / Sama, the leader in purpose-built, responsible enterprise AI with agile data labeling for model training and performance evaluation, today announced the launch of Sama Multimodal, a new solution that combines multiple data types and inputs with human-in-the-loop (HITL) validation to create more powerful, accurate AI systems. By integrating diverse modalities including images, video, text, audio, LiDAR and radar data, Sama Multimodal has demonstrated significant improvements in model accuracy for industries such as automotive and retail. Early results have been impressive, including a large retail implementation that saw a 35% increase in model accuracy and a 10% reduction in product returns.

Sama Multimodal offers enterprise AI teams a flexible framework and its widget-based architecture makes it easy to rapidly integrate multiple AI models at different stages of workflow, including using pre-annotations from open source, client and/or Sama-based models, while incorporating strategic HITL validation – ensuring quality and mitigating bias in model outputs.

“With Sama Multimodal, organizations can build differentiated AI solutions using the full spectrum of data available, including sensor data which is growing ever more prolific,” said Duncan Curtis, SVP of AI Product and Technology at Sama. “What makes our platform truly unique is its flexibility – teams can ingest, align, and annotate any combination of modalities, then transition from pre-trained to proprietary models at the right moment in their development workflow. It’s designed to evolve with AI itself.”

Sama Multimodal democratizes access to advanced AI technologies and creates differentiated customer experiences for the retail and automotive industries. In retail applications, for example, Sama’s multimodal capabilities significantly improve search relevance applications and product discovery with a combination of image, text, and video annotations. In automotive, Sama Multimodal excels at integrating camera, LiDAR, and radar data to create more comprehensive environmental understanding for advanced driver assistance systems and autonomous vehicles.

Sama’s multimodal infrastructure is future-proof, enabling enterprises to scale model sophistication without rebuilding data pipelines from scratch. By leveraging human expertise for complex contextual understanding while automating routine data processing tasks, Sama Multimodal is ideal not only for today’s applications but also for emerging needs, such as voice-assisted retail search, vision-enhanced robotics, and personalized customer experiences powered by real-time behavioral data.

Sama Multimodal is fully supported by SamaHub™, a collaborative workspace and by SamaAssure™, the industry’s highest quality guarantee, which routinely delivers a 98% first batch acceptance rate.

About Sama

Sama is a global leader in data annotation solutions for computer vision, generative AI and large language models. Our solutions minimize the risk of model failure and lower the total cost of ownership through an enterprise ready ML-powered platform and SamaIQ™, actionable data insights uncovered by proprietary algorithms and a highly skilled on-staff team of over 5,000 data experts. 40% of FAANG companies and other major Fortune 50 enterprises, including GM, Ford and Microsoft, trust Sama to help deliver industry-leading ML models.

Driven by a mission to expand opportunities for underserved individuals through the digital economy, Sama is a certified B-Corp and has helped more than 68,000 people lift themselves out of poverty. An MIT-led Randomized Controlled Trial has validated its training and employment program. For more information, visit www.sama.com.

Sama Media Contact:

press@samasource.org

SOURCE: Sama

View the original press release on ACCESS Newswire

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