Sama provides managed data annotation, validation, and model evaluation services plus a platform to deliver human-verified, production-ready training data for computer vision, multimodal, and GenAI/LLM models at enterprise scale.
Sama is a data annotation, validation, and model evaluation company that helps AI teams ship high-performance models with human-verified data at scale. The company combines automation with expert human-in-the-loop (HITL) workflows across computer vision, NLP, and multimodal AI to improve production accuracy, reduce rework, and keep AI programs on predictable timelines and budgets. Sama provides both managed data services and a data-centric platform, positioning itself as "responsible by design" with an ethical AI approach. It emphasizes rigorous quality systems (SamaAssure) and collaborative delivery (SamaHub) to provide transparency into throughput, quality, and budget burndown while enabling tight feedback loops down to the annotator level. The company highlights long-term enterprise relationships (customers stay an average of 8 years) and high quality outcomes such as a 99% first-batch acceptance rate, and written quality guarantees ranging from 95% up to 99.5% depending on project needs. Sama also foregrounds its impact mission inspired by founder Leila Janah, creating full-time fair-wage work for people historically excluded from the digital economy. Founded in 2008, Sama is a Certified B Corporation and reports significant social impact, including supporting tens of thousands of lives and building careers for thousands of associates, while delivering production-ready training data for enterprises and leading AI teams.
HC score
verified business cases
Security and compliance program for secure data labeling and validation delivery including secure facilities, privacy support, and compliance frameworks and certifications.
Secure Facilities
Background Checks
GDPR Compliance
Managed data annotation solution with a full-time in-house workforce for image, video, and 3D point cloud annotation, with multi-level quality management and AutoQA to reduce rework and improve delivery predictability.
Image Annotation
Video Annotation
3D Annotation
Data curation tools that help teams filter, visualize, and prioritize the most impactful data to label, reducing total cost of ownership and development time.
Data curation
Embeddings analytics
Metadata filtering
Generative AI and LLM training data, validation, and evaluation services including fact checking, instruction following, preference ranking, captioning, creative writing, and synthetic data creation backed by SamaAssure.
Model Validation
Fact Checking
Instruction Following
A data annotation and validation platform for complex computer vision, traditional ML, and GenAI workflows, including quality processes, project collaboration, reporting, integrations, and proactive insights.
SamaAssure
SamaHub
SamaIQ
Managed service data validation to review model predictions, correct outputs, and provide reporting on where models perform well or need improvement.
Prediction review
Corrective actions
Custom workflows
Top German OEM (name not provided)
A major German OEM needed to improve assembly-line quality inspection before vehicles left the factory. Manual inspection limited throughput and made it harder to consistently catch issues early. The customer wanted to shift from human checks to camera-based detection to increase efficiency. The goal was to upgrade inspection workflows without disrupting production. The team implemented a camera-based inspection approach to detect quality issues earlier in the process. Inspection workflows were upgraded to support higher-throughput quality checks on the assembly line. The solution replaced portions of human-led inspection with automated, camera-driven detection. This change modernized the quality inspection process while supporting the OEM’s efficiency objectives. The upgraded workflows delivered a 4x increase in assembly line efficiency. The project also decreased overall costs, though the savings were not quantified. The improved inspection process increased throughput and helped identify issues before vehicles left the factory. Overall, the initiative improved efficiency while reducing cost pressure.
Skills
Project Details
Walmart
Walmart aimed to improve search relevance on its online store. It needed a labeling partner that could scale without sacrificing quality. Existing item coverage limited search accuracy and user experience. Walmart implemented high-quality labeling to power more accurate search results. The labeling approach was designed to scale while maintaining quality. This improved the underlying data used to support better user experiences. The initiative improved retail store item coverage from 91% to 98%. These improvements supported better search relevance on the online store. The outcomes impacted Walmart’s 385 million online visitors.
Skills
Project Details
Ecommerce leader (name not provided)
A market leader in online retail delivery struggled to maintain accuracy across a product catalog with millions of items. Foundational-model-generated listings often contained errors. These issues threatened search relevance and listing quality. The customer needed to improve catalog accuracy at scale. The customer ran an initiative focused on improving catalog accuracy across its large catalog. The work addressed errors found in foundational-model-generated product listings. The implementation aimed to enhance search relevance and improve overall listing quality. The approach was applied across the catalog to support consistent customer experience. The initiative improved catalog accuracy and strengthened listing quality. Search relevance was enhanced as a result of more accurate listings. The customer achieved a 1% year-over-year increase in AOV. The results were realized across 4M products in the online retail catalog.
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Project Details
Automotive OEM
An automotive OEM needed annotations for millions of samples to improve a multi-class object detection system. The work spanned semantic segmentation, object tracking, and object detection. The customer required tight annotation precision across these tasks. The team delivered annotation services tailored to the OEM’s multi-class object detection needs. The implementation covered semantic segmentation, object tracking, and object detection at scale. The project operated under strict precision requirements to meet quality expectations. The engagement met a 2-pixel tolerance requirement for annotations. It also delivered a 98% Quality SLA. The OEM received annotations for millions of samples to support improvements to its multi-class object detection system.
Skills
Project Details
Full service creative production company helping brands maximise the impact of their marketing content
Human Cloud Verification ensures that the listed end customer is verified. It's used across kudos, customers, and business cases, and performed by Human Cloud. Think about it like a background check.
Empowering US startups with unrivaled access to global engineering talent, seamless hiring, and improved retention.
Human Cloud Verification ensures that the listed end customer is verified. It's used across kudos, customers, and business cases, and performed by Human Cloud. Think about it like a background check.
An independent global marketing consultancy delivering outsized growth.
Human Cloud Verification ensures that the listed end customer is verified. It's used across kudos, customers, and business cases, and performed by Human Cloud. Think about it like a background check.
