AfterQuery is an applied research lab dedicated to advancing artificial intelligence by transforming expert human reasoning into high-quality training data. Recognizing that real-world expertise encompasses decisions, trade-offs, and contextual understanding not typically documented, AfterQuery collaborates with domain specialists to capture and structure this implicit knowledge. This approach enables AI models to learn not just from outputs but from the nuanced processes behind expert decision-making. Key Features and Functionality: - Supervised Fine-Tuning (SFT): Provides high-quality prompt-response pairs and chain-of-thought reasoning traces, teaching models to handle complex tasks effectively. - Reinforcement Learning with Rubrics: Develops expert-designed prompts accompanied by grading frameworks for reasoning and code generation, converting subjective judgments into scalable reward signals. - Agent Environments (API/MCP): Creates custom environments across various APIs, tools, and services, facilitating the training and evaluation of AI agents within real-world workflows. - Computer Use Trajectories: Captures human-demonstrated interactions in browser and desktop environments, enabling models to learn end-to-end software navigation and operation. Primary Value and Problem Solved: AfterQuery addresses the limitations of AI models that rely solely on output-based training data, which often leads to suboptimal performance in real-world applications. By incorporating the depth of human reasoning and decision-making processes into training datasets, AfterQuery enhances the capability of AI systems to perform complex tasks with greater accuracy and contextual understanding. This methodology bridges the gap between theoretical AI capabilities and practical, expert-level performance, empowering researchers and enterprises to develop more robust and reliable AI solutions.