PLG OS empowers businesses to focus on product-led growth (PLG), guiding them to make data-informed decisions. With comprehensive tools and analytics, the platform helps in understanding user behavior, improving product engagement, and driving growth. It caters to companies aiming to shift from traditional sales-led models to more dynamic product-led strategies. PLG OS is designed to uncover actionable insights, enhance user experiences, and ultimately contribute to the business's bottom-line growth.

Travel Agent based on Qwen2 RLHF is an AI-driven travel assistant that harnesses reinforcement learning from human feedback to optimize every aspect of trip planning. Combining real-time flight and hotel availability, local attraction databases, and user-defined preferences, the agent curates daily schedules, dining recommendations, transportation routes, and budget breakdowns. It supports multi-destination trips, group travel coordination, and last-minute changes, adjusting itineraries on the fly. Through a conversational interface, travelers input budget, dates, interests, and accommodation standards, receiving detailed plans including maps and packing checklists. Continuous interaction and feedback enable the model to learn individual tastes, improving relevance over time and delivering tailored, seamless travel experiences.

Build AI empowers users to design customized AI web apps that resonate with their audiences. By leveraging a no-code approach, users can easily integrate various AI features and personalize tools according to their expertise. It not only simplifies the process of creating sophisticated applications but also allows for quick iterations based on user feedback, enabling businesses to stay agile in a rapidly evolving digital landscape.

HoneyHive is a comprehensive platform providing AI evaluation, testing, and observability tools, primarily aimed at teams building and maintaining GenAI applications. It enables developers to automatically test, evaluate, and benchmark models, agents, and RAG pipelines against safety and performance criteria. By aggregating production data such as traces, evaluations, and user feedback, HoneyHive facilitates anomaly detection, thorough testing, and iterative improvements in AI systems, ensuring they are production-ready and reliable.