AI researchers and enterprises often struggle with inadequate, biased, or poorly curated training data, which limits the performance and reliability of large language models and autonomous agents. Existing data pipelines lack the ability to capture expert judgment and real-world workflow contexts, making it difficult to fine‑tune models for specific industry needs.

AfterQuery converts real-world work and expert subjective judgments into high‑quality, scalable training data and reward signals. The company builds custom datasets tailored to fine‑tune large language models for precise performance requirements. It also creates high‑fidelity simulation and reinforcement‑learning environments that replicate production workflows, enabling safe training and evaluation of AI agents. In addition, AfterQuery offers vertical‑specific consulting, embedding researchers with client teams to design and deploy end‑to‑end AI solutions that integrate internal documents and data sources.

Proprietary dataset creation optimized for fine‑tuning LLMs to industry‑specific use cases

High‑fidelity simulation and RL environments that mirror production workflows for safe agent training

End‑to‑end agent deployment integrating internal files, databases, and custom models

Vertical‑specific AI consulting with researchers embedded in client teams

Custom reward signal generation from expert subjective judgments to guide model training

Primary customers are AI research labs and enterprise teams in regulated or data‑intensive industries that require bespoke training data, simulation environments, and tailored generative AI solutions.