We create synthetic datasets that simulate real-world conditions, enabling more accurate training of AI models. By using advanced techniques to generate diverse and representative data, we help you build more robust AI systems that perform well in real-world environments, without the challenges of sourcing and labeling massive datasets.

Large Language Models (LLMs) are powerful, but they often "hallucinate" or produce unreliable outputs. We provide a framework for evaluating LLM performance with specific metrics to ensure they meet the required standards. Whether you're fine-tuning an existing model or developing a new one, we tailor evaluation frameworks to your needs, helping you optimize model reliability and performance.

We offer end-to-end solutions for human-in-the-loop AI processes. Whether you need data tagging, results ranking, or validation, we provide scalable, real-time services from highly trained teams from across the globe. Our unique 'in-app voting' mechanism ensures data accuracy, and with our app-based platform, we make the process seamless, with instant payment for contributors at scale.

Our in-house enterprise app "TagGuard" is designed to securely crowdsource LLM and AI data tagging from across the globe. This app democratizes access to livelihood opportunities for individuals in low-income countries by allowing them to participate in the AI boom. Users can contribute by tagging data, which is then verified for accuracy by a network of skilled data taggers on the platform.