25% of Fortune 50 companies trust Sama to help them deliver industry-leading ML models
Enterprises rely on Sama to deliver the data and insights needed to train models effectively.
We provide the industry’s highest quality guarantee by employing the most comprehensive quality assurance processes. We work with you at the start of each project to collaboratively define what quality looks like and then provide in-depth training for all team members to drive consistency and eliminate high amounts of rework or lengthy delays
Leverage a centralized command center for annotation and validation workflows, quality review management, and reporting. You’ll get complete visibility into your projects and be able to directly collaborate with your dedicated Sama team. Plus, our suite of integrations makes onboarding easy so you can get started in days not weeks.
Our proprietary algorithms and human-in-the-loop approach enable our teams to identify actionable insights that help get models into production faster. From early edge case identification and senor or data distribution errors to deep visibility into model performance including exactly where and when models are failing to accurately predict outcomes.
We start with a 95% written quality guarantee for every project but can guarantee up to 99.5% regardless of complexity or scale.
Quality calibration is our first step in every project. We work closely with you to create comprehensive quality rubrics built from mutually agreed upon golden tasks, aligning on error definitions and criticality for your model type.
We stay in continuous alignment with your team and can iterate on instructions and rubrics even after production has started to adapt to changing workflows and more accurately reflect the real world.
Our full-time, in-house team of over 4,000 data experts have an average of over two years of direct experience and are never crowdsourced. All of our teams are vertically segmented and understand the unique nuances of each industry—such as being an expert in the AgTech industry and being able to differentiate between different types of bugs and weeds.
Before project kickoff, all team members undergo rigorous 2-week, project-specific training and certification based on quality rubrics developed during the quality calibration phase ensuring they are subject matter experts on your workflows. Throughout your project, we invest in ongoing coaching and upskilling for continuous improvements to accuracy and precision.
Our Automated QA process automatically reviews all annotations and proactively catches any logical fallacies. Incorrect annotations are automatically surfaced saving our team 1-3 hours per day on average.
This enables our teams to dedicate more time to complex edge cases and more detailed workflows, allowing us to scale more consistently. After a task passes AutoQA it goes through a final check with our QA agents who catch any final errors before they are sent to you.
Once passing AutoQA all annotations go to a QA agent with an average of 4 years of experience. This final review provides an additional layer of review to help ensure quality and consistency at scale. They typically review the correctness and completeness of labels and flag unusual or challenging edge cases to proactively review with the client to ensure instructions are accurate.
For example, in one instance our team flagged that some cones were movable versus drilled into the ground. Rather than label everything a cone, we proactively updated instructions to label the drilled cones as barriers.
Get full transparency into throughput, quality, and budget burndown while directly collaborating with your dedicated team of data experts.
Comprehensive reporting
Easily track key metrics for all your projects—including insights into invoice and budget burndown to actively monitor and manage budget usage and allocations to scale up or down as needed. Get comprehensive reports that provide details on progress on uploaded data batches and annotation throughput. Also, track how annotation complexity and quality change over time.
Collaborative Feedback Loops
Enjoy direct, two-way communication between your team and ours, down to the annotator level. Receive early alerts to edge cases or instruction gaps. Provide feedback on errors, answer questions, and send updated instructions for timely recalibration and continuous improvements to your data quality.
Sampling On-Demand
Have complete control. Monitor data quality on-demand through our self-service sampling portal. Easily sample batches and inspect completed tasks to confirm they meet quality standards. Provide direct feedback to annotators and reviewers to help them correct errors and improve the overall process.
Integrations
Get started in hours not weeks with our comprehensive suite of integrations. Our multi-cloud integrations not only reduce project onboarding time by 7x but eliminate the need for Sama to make or store a copy of your data. SSO integrations help you maintain security and access compliance.
Our proprietary algorithm and human-in-the-loop approach surface insights that often prevent models from surviving in production environments.
Incorporating early edge case and trend insights early in the data annotation and model training process helps strengthen the model's ability to handle diverse scenarios. It also enables ML teams to source more specific data to proactively fill in data diversity gaps. This reduces errors and unexpected failures, leading to faster and smoother processes in production.
Our team proactively identifies various forms of sensor errors—from calibration issues, environmental factors, and hardware malfunctions to background noise and sensor sensitivity. Filtering out faulty data improves a model's ability to learn correct patterns and relationships and helps avoid unexpected failures in production.
As we annotate and analyze your data, we’ll flag any distribution errors that could introduce biases or blind spots into your model—enabling you to proactively refine your training data sets. Our team also provides insights into the complexity and similarity of your data in order to better prioritize the data that is going to have the greatest impact on your model.
We help you drive toward model maturity by providing deeper visibility into where and why models are failing to predict accurate incomes. Understand model performance by class along with a complete error analysis highlighting false positives, false negatives, and misclassifications. These insights help highlight potential areas where biases might lie in your model and any deficient classes in your training data.
First batch client acceptance rate across 10B points per month
Get models to market 3x faster by eliminating delays, missed deadlines and excessive rework
Lives impacted to date thanks to our purpose-driven business model
Sama delivers not only accurate video annotation, but insights and recommendations via our vertically integrated platform combined with human-in-the-loop experts, all while embracing an ethical AI approach. This is why companies come to us when other video annotation solutions fail.
No matter how complex your models, we consistently deliver a 99% client acceptance rate as you scale, even with high ambiguity images and edge cases.
Sama has over 15 years of experience and our annotators have an average tenure of 2+ years. Vertically segmented teams provide expertise into industry nuances.
As the first AI certified B Corp, Sama has provided economic opportunities for over 65,000 employees from underserved communities.
ISO certified delivery centers, a biometric secured platform and our in-house workforce help protect your data from unauthorized access and data corruption from ingestion to delivery.
Your data remains protected and private because it’s managed in a secure facility by full-time in-house workforce of data experts. Your Data is Yours – Sama does not share or keep any datasets for training or other purposes, unlike crowdsourced alternatives.
Learn more about Sama's work with data curation
Large language models (LLMs) have emerged as powerful tools capable of generating human-like text, understanding complex queries, and performing a wide range of language-related tasks. Creating them from scratch however, can be costly and time consuming. Supervised fine-tuning has emerged as a way to take existing LLMs and hone them to a specific task or domain faster.
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
