Putting the Intelligence in Artificial Intelligence

Leverage our global network of advanced trainers to perform reinforcement learning and supervised fine-tuning tasks at scale that enhance model performance and accuracy.

SMEs across domains, including PhDs, fine-tune an AI model specialized to your needs, creating demonstration data that supports applications from summarizing complex data to expert-level conversational AI.

Our continuous feedback loops for AI model optimization help refine performance, adapt to new data, and reinforce against adversarial threats and hallucinations.

Our agile platform supports end-to-end AI implementation, including complex data structuring, and ensuring that AI integrates with your tech stack, processes, and people.

Enhance foundation model performance and alignment with human values by utilizing reinforcement learning from human feedback (RLHF) and supervised fine-tuning (SFT) techniques.

Ensure your AI models are refined to deliver nuanced, industry-specific insights and actions by applying domain expertise.

Create a cohesive workflow that integrates seamlessly with various enterprise systems by chaining multiple AI models for more complex tasks.

Test and improve model resilience against potential threats and biases by deploying adversarial training and red team exercises.

Generate highly accurate and contextually relevant content from your datasets at scale with our specialized RAG capabilities.

Produce high-quality, comprehensive reports from varied and disparate sources that synthesize proprietary information to analyze trends, make projections, and support decision-making.

Ensure clear accountability and responsibility in AI development and outcomes, in keeping with client goals. Combine rigorous internal policies for model application and training with collaboration on safety and potential harms to secure higher quality data, ensure trainer safety, and manage complex challenges effectively.

Develop and use AI applications in a manner that respects human rights, promoting the welfare of all individuals and a safe working environment. This includes the ethical sourcing and enrichment of data used to develop AI systems.

Maintain transparency through our AI practices and policies to improve and apply AI systems. Emphasize the careful management of high-quality inputs to align model outputs with an intended or assumed goal.

Assist clients in achieving their goals, while giving careful consideration to avoid mistreatment or discrimination.

Work with partners committed to responsible AI and human wellness, seeking those with a shared dedication to monitoring and evaluating AI applications while collaboratively adapting to challenges as they arise.

Ensure high standards of data privacy and security, so that data utilized is collected, stored, and processed with the consent of all parties and in accordance with all relevant legal requirements.

Prioritize the safety and reliability of AI systems throughout their lifecycle, ensuring they perform as intended and are free from known vulnerabilities. Based on client objectives, implement practices that ensure safety for our trainers and maintain reliability through consistent, data-driven methods.

Collaborate actively with a diverse range of stakeholders to gather perspectives on ethical AI development and deployment. Aim to advance these principles together, aligning with partner goals for better understanding and cooperation.

Foster a culture of continuous improvement and ethical innovation by encouraging an environment where sharing, learning, feedback, and the latest advancements in AI ethics are integrated into all aspects of our work.

Provide training for all workers involved in the AI lifecycle on ethical AI principles, the responsible use of AI technologies, and the importance of following these guidelines. Training is regularly updated to reflect current ethical standards and practices.

Encourage a culture of openness where ethical considerations are discussed and there are clear processes to report issues.