40% of FAANG companies trust Sama to deliver industry-leading data that powers AI
We’ll work with your internal specialists and domain experts to understand your requirements. Then we’ll align on prompt distribution targets including various dimensions such as tone, delivery format, justification and more.
Our AI specialists leverage their expertise to write high quality prompts along with corresponding answers across varying formats and dimensions. We’ll curate a highly specialized set of data to help streamline the large language model development process.
After an initial set of data has been created we’ll work with your team to review the prompts and responses created to ensure the data aligns with the intended purpose of the generative model or LLM. If needed, our teams will collaborate closely to recalibrate.
Once an initial set of prompts and responses has been created. Our team will scale the process by coming up with multiple variations of prompts to augment your training data. We’ll also use proprietary models to help create variants of human generated prompts to create large-scale tests.
When training data is complete, we follow a structured delivery process to ensure smooth integration with your LLM training pipeline. We offer flexible and customizable delivery formats, APIs, and the option for custom API integrations to support rapid development of 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 will help you build upon an existing LLM to create a proprietary model tailored to your specific needs. We’ll craft new prompts and responses, evaluate model outputs, and rewrite responses to improve accuracy and context optimization.
Our human-in-the-loop approach drives data-rich model improvements & RAG embedding enhancements through a variety of validation solutions. Our team provides iterative human feedback loops that score and rank prompts along with evaluating outputs. We also provide multi-modal captioning and sentiment analysis solutions to help models develop a nuanced understanding of user emotion and feedback.
We’ll help create new data sets that can be used to train or fine tune models to augment performance. If your model struggles with areas such as open Q&A, summarization or knowledge research, our team will help create unique, logical examples that can be used to train your model. We can also validate and reannotate poor model responses to create additional datasets for training.
Our team of highly trained of ML engineers and applied data scientists crafts prompts designed to trick or exploit your model’s weaknesses. They also help expose vulnerabilities, including generating biased content, spreading misinformation, producing harmful outputs and more to improve the safety and reliability of your Gen AI models. This includes large scale testing, fairness evaluation, privacy assessments and compliance.
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 a variety of solutions.
Our proactive approach minimizes delays while maintaining quality to help teams and models hit their milestones. All of our solutions are backed by SamaAssure™, the industry’s highest quality guarantee for Generative AI.
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 unwanted biases and other privacy or compliance vulnerabilities.
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.
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.
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
2024 Customer Satisfaction (CSAT) score and an NPS of 64
Learn more about Sama's work with data curation
To ensure effective and responsible implementation of Gen AI, financial institutions must navigate challenges such as model explainability, data privacy, and regulatory compliance. By understanding the tech’s potential and the strategies for overcoming associated risks, you can position your organization for a competitive advantage in the age of intelligent automation. Here are four key things to consider.
This data serves as the model's learning material, allowing it to grasp the underlying patterns, structures, and relationships within that information. Essentially, training data is the foundation upon which a generative AI model builds its understanding of the world, shaping its ability to generate creative text formats, translate languages, or produce realistic images.
High-quality data, free from biases and factual errors, leads to more accurate and reliable outputs from the generative model. Additionally, a diverse range of training data, encompassing various styles, formats, and viewpoints, equips the model to handle a wider range of prompts and scenarios effectively.
Prompt engineering is the process of creating the contextual instructions for your generative AI model. Prompt engineers don't just write generic instructions; they consider the model's capabilities, the specific task at hand, and the intended outcome. They might use clear and concise language, provide specific examples, or even break down complex tasks into smaller, easier-to-understand prompts.
By incorporating edge cases into training data and testing procedures, developers can identify these potential pitfalls. This can involve feeding the model nonsensical prompts, introducing data with deliberate errors or simulating unusual user interactions. By exposing the model to these edge cases, developers can refine its ability to handle unexpected situations, leading to more adaptable and reliable AI.