AfterQuery
UnknownApplied research lab that curates expert-sourced training data, RL environments, and evaluations to accelerate foundation model development.
ツール情報
AfterQueryとは何ですか?
AfterQuery is an applied research lab that captures real-world expert reasoning, decisions, workflows, and professional knowledge and structures it into high-quality datasets, reinforcement learning environments, supervised fine-tuning data, and custom evaluations for training and improving foundation AI models. It turns real-world work into training data that reflects how experts actually solve problems—step by step, decision by decision.
コア機能
Rubric and Verifier-based RL
Combines expert-crafted rubrics with automated verifiers that grade model outputs the way a seasoned professional would, rewarding nuance and penalizing shortcuts across reasoning, code generation, and instruction-following tasks.
Tool-calling RL Environments
Provides custom RL environments built on top of real APIs, MCP servers, and developer tools, enabling models to learn how to call, chain, and recover from errors across complex service workflows with automated evaluation at every step.
SFT
Delivers high-quality prompt-response pairs and chain-of-thought demonstrations through supervised fine-tuning, giving models a foundation of skills before RL begins, teaching them to reason, follow instructions, and navigate professional tasks from the ground up.
Computer-use and Browser-use Environments
Pairs high-fidelity browser and desktop environments with expert-demonstrated trajectories, teaching agents to navigate interfaces, complete multi-step workflows, and operate software the way a domain expert would.
RLHF
Captures the subtleties of what makes one response genuinely better than another through RL from human feedback, training models to internalize the taste, judgment, and standards of domain experts across thousands of comparison pairs.
Code Generation
Spans expert-written code, test cases, and debugging traces that teach models to write production-quality software, handle edge cases, and reason through architectural decisions the way experienced engineers do.
Professional Domains
Draws on 100,000+ verified practitioners across medicine, law, finance, engineering, and more, capturing the tacit knowledge and real-world judgment that textbooks leave out and synthetic data can’t replicate.
Deep Research
Covers long-horizon research tasks, teaching models to gather evidence across sources, synthesize findings, and produce thorough analyses that mirror how skilled researchers build understanding over hours of investigation.
Loss Analyses
Identifies where and why models fail in professional contexts through systematic study, pinpointing the precise failure modes and distributional gaps that inform how every dataset and environment is designed.
Multimodal
Teaches models to see, interpret, and reason across image, audio, video, and text together, closing the gap between how humans experience the world and how AI processes it.
Off-the-shelf Data
Offers ready-to-deploy datasets across high-demand capability areas, giving labs and companies immediate access to rigorously validated training data without the lead time of a custom engagement.
Custom Evals and Training Datasets
Tailors evaluation suites and training datasets to your specific capability targets, designing every prompt, rubric, and environment from scratch to address the exact gaps your models need to close.
使用例
- #1
Foundation model supervised fine-tuning
Providing high-quality prompt-response pairs and chain-of-thought reasoning traces to teach models how to behave across complex professional tasks before reinforcement learning.
- #2
Reinforcement learning with expert rubrics
Using expert-designed prompts and grading frameworks for reasoning and code generation to turn subjective judgment into scalable reward signals for model training.
- #3
Agent training in real tool and API workflows
Building custom environments across APIs, tools, MCP servers, and services for training and evaluating agents on real multi-step service workflows.
- #4
Computer and browser use agent training
Teaching models to navigate interfaces, complete multi-step workflows, and operate software using human-demonstrated trajectories in high-fidelity browser and desktop environments.
- #5
Domain-specific professional model improvement
Capturing tacit knowledge and real-world judgment from verified practitioners in medicine, law, finance, engineering, and other fields for specialized model training.
- #6
Custom evaluation and gap-closing datasets
Designing tailored evaluation suites and training data from scratch to address specific capability targets and failure modes identified in professional contexts.
料金プラン
No pricing details, plans, or commercial terms are described on the official product or homepage snapshots.
代替案
よくある質問
What is AfterQuery?
What is the primary purpose of AfterQuery?
Who is AfterQuery designed for?
What kinds of training data and environments does AfterQuery offer?
Does AfterQuery cover professional domains?
Is pricing information available for AfterQuery?
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