25% of Fortune 50 companies trust Sama to help them deliver industry-leading ML models
With over 15 years of industry experience, Sama’s data annotation and validation solutions help you build more accurate GenAI and LLMs—faster.
Our team helps elevate model accuracy and performance by creating fine tuning data sets and prompt engineering. We also review and provide insights on the curation of underlying data to improve data distribution.
Our human-in-the-loop approach drives data-rich model improvements through iterative RLHF feedback loops. Our team of experts will score and rank prompts along with evaluate model outputs to refine models for enhanced performance.
Our team generates descriptive and contextually rich captions for images and videos to aid captioning, enhanced search relevance, and accessibility efforts. We also validate image-sentence pairings, ensuring that the generated captions accurately reflect the content of the visuals. This process contributes to greater accuracy and coherence in the generated output.
Sama provides comprehensive sentiment analysis across all modalities including text, image and audio. We combine advanced techniques and expert curation to enhance the accuracy and effectiveness of sentiment models for a nuanced understanding of user emotions and feedback.
Drive personalization and enhanced search accuracy with Sama’s data curation and classification services. We help enhance search capabilities by meticulously organizing and optimizing datasets, and ensuring precision and relevance in search results for an elevated user experience.
Sama’s continuous improvement process for GenAI and LLMs results in more accurate models. By using partially annotated training data to create an initial model, successive iterations improve each time.
Our team is trained to provide comprehensive support across various modalities including text, image, and voice search applications. We help improve model accuracy and performance through data categorization, curation and validation solutions.
SamaAssure™ is the industry’s highest quality guarantee for data annotation for Generative AI. Each data annotation for foundation model development project has a dedicated team of in-house industry experts trained for that specific project. They’ve helped Sama achieve 10 billion data points per month with a 99% client acceptance rate and the lowest total cost of ownership in the industry. Sama’s proactive approach minimizes delays while maintaining quality to help models hit their milestones.
SamaIQ™ combines the expertise of the industry’s best specialists with deep industry knowledge and proprietary algorithms to deliver faster insights and reduce the likelihood of model failure. Our team can help provide recommendations on how to best structure data and set up reinforced learning cycles in order to get the best model outputs.
SamaHub™, our collaborative project space, is designed for enhanced communication. GenAI and LLM clients have access to collaboration workflows, self-service sampling and complete reporting to track their project’s progress, see how their data is being annotated, and shape their model’s advancement.
We offer a variety of integration options, including APIs, CLIs, and webhooks that allow you to seamlessly connect our platform to your existing workflows. The Sama API is a powerful tool that allows you to programmatically query the status of projects, post new tasks to be done, receive results automatically, and more.
Our team can annotate large datasets or documents for text categorization and classification tasks. This helps LLMs learn to classify and organize textual information into predefined categories along with learn to categorize and organize large sets of text documents.
Building smarter conversational AI solutions starts with Sama. Our team creates annotated datasets that help train LLMs to master the nuances of human language, enabling them to deliver natural and engaging responses within conversational AI systems.
Sama’s skilled annotators validate text annotations from images, ensuring accuracy and relevance. This process is crucial for applications requiring text recognition, improving the quality of GenerativeAI-generated text annotations.
Our human-in-the-loop feedback loop refines Generative AI outputs. Through contextual image analysis, production modality classification, and element tagging, we iteratively enhance annotation accuracy and relevance for specific use cases.
Sama can support Named Entity Recognition (NER) tasks by annotating entities such as names, locations, organizations, and other specific terms within text. This enhances LLMs’ ability to identify and extract relevant information.
Our team can help create annotated datasets to help LLMs grasp the nuances of language, understand relationships between concepts, and identify key information within text to comprehend complex questions. This helps models understand and respond to user queries more accurately.
Sama’s video annotation projects start with tailored consultations, assessing tagging needs via knowledgeable solutions engineers. After evaluating alternatives, projects launch on Sama’s cutting-edge video annotation platform.
For our video annotation services, we employ an integrated calibration process to maximize efficiency and value. In the initial session, we establish clear instructions, address edge cases, and align on key metrics. Subsequent collaborative meetings with a project manager, solutions engineer, and QA agent, whether weekly or monthly, ensure quality scaling as you utilize our services.
Each project is assigned to a dedicated team at Sama, consisting of internal experts with deep industry knowledge – avoiding consultants, contractors, or crowdsourcing. These teams undergo project-specific training, ensuring their effectiveness from the outset. This methodology extends to our video annotation services, where specialized training empowers our teams to excel in handling video training data right from the start.
Sama continually enhances its video annotation solutions through iterative feedback, with a focus on shaping and refining annotations using a video annotation tool. We utilize level feedback, percentage of data set review, and automated scoring, bolstered by real-time analytics that offer immediate actionable insights. Should data accuracy not meet client expectations, we provide complimentary rework services for the project.
Sama’s video annotation services offer flexible and customizable delivery formats for key metric reports and evaluations within its image annotation service. This is complemented by robust quality control measures to reduce inaccuracies swiftly, ensuring the rapid deployment of ML models.
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
Learn more about Sama's work with Data Curation
Generative AI uses knowledge from sophisticated algorithms to generate entirely new content that resembles real-world examples but isn’t simply copied. It learns the patterns and relationships within existing data to generate new things that fit the same styles or patterns.
Large language models are AI systems trained on massive amounts of text data that can generate realistic and coherent text. They are versatile tools that can analyze the context of a conversation and previous interactions to interpret requests and tailor their responses accordingly.
Foundation models are large machine learning models that are trained using a combination of supervised and self-supervised techniques. In computer vision specifically, they typically solve canonical vision tasks (image classification, object detection, instance segmentation), and can serve as a basis for a variety of downstream tasks and applications.
Foundation models enable artificial intelligence and natural language processing. The main benefit of foundation models is that they can be reused across a variety of specialized tasks and applications, avoiding the need to train an end-to-end model for each specific application.
Foundation models which are not trained correctly can result in bias, critical model failures, and model hallucinations. High-quality annotation for foundation AI is crucial to create models that are error free, meet deadlines, and adhere to client expectations.
Data annotation services for foundation models are important because they offer expertise, speed, and lower total cost of ownership. Data annotation best practices for AI models