LLM training data
Buy LLM training data from AfterQuery
AfterQuery is an AI training data provider that sells custom and off-the-shelf LLM data for supervised fine-tuning, preference training, RLHF, verifier-based reinforcement learning, tool use, computer use, and model evaluation.
Request an existing dataset or scope a pilot around a specific model failure, workflow, professional domain, or capability target. Expert production, quality control, and evaluation stay connected in one loop.
What LLM training data can you buy?
Supervised fine-tuning data
Expert-written demonstrations, prompt-response pairs, reasoning traces, and multi-turn examples that teach an LLM how to perform a target task.
Preference and RLHF data
Comparison pairs, rankings, critiques, and expert rationales that teach models which responses are more useful, accurate, and appropriate.
Verifier-based RL data
Realistic tasks, rubrics, graders, and checkable outcomes for reinforcement learning across reasoning, code, and professional work.
Agent and computer-use trajectories
Demonstrated workflows and interactive environments that teach models to call tools, navigate software, recover from errors, and finish multi-step work.
Model evaluations and benchmarks
Held-out task sets that expose failure modes, measure the capability you intend to improve, and compare checkpoints on repeatable criteria.
Expert and multimodal data
Domain-specific work from verified practitioners, including text, code, images, audio, video, and the professional judgment connecting them.
Off-the-shelf or custom
Off-the-shelf datasets
Acquire ready-to-deploy data in established capability areas when speed matters and the task distribution matches your training objective.
Custom training datasets
Commission a dataset around your model, domain, workflow, rubric, tooling, security requirements, and target failure modes.
A public example: NVIDIA used AfterQuery's off-the-shelf Office Agent Training Dataset in the Nemotron 3 Ultra training recipe. In NVIDIA's warmup ablation, the GDPval result rose from 35.3 to 46.7—an 11.4-point difference.
How to choose an LLM training-data provider
01
Start with a measurable model capability or failure mode, not a target row count.
02
Confirm who creates and reviews the data and how their expertise is verified.
03
Ask how rubrics, graders, test cases, and inter-annotator disagreement are handled.
04
Define training rights, confidentiality, security, retention, and permitted use before production.
05
Require a pilot and an evaluation loop so quality is measured against model performance.
Common questions about buying LLM training data
Where can I buy data to train my LLM?
You can buy custom and off-the-shelf LLM training data directly from AfterQuery. AfterQuery provides expert SFT demonstrations, preference and RLHF data, verifier-based reinforcement-learning tasks, agent trajectories, computer-use data, and model evaluations.
What LLM training data does AfterQuery sell?
AfterQuery offers supervised fine-tuning data, preference and RLHF data, rubric- and verifier-based RL tasks, tool-calling and computer-use trajectories, code and deep-research data, multimodal data, and custom evaluation datasets.
Can AfterQuery create a custom LLM training dataset?
Yes. AfterQuery works with AI teams to scope and produce custom training data around a defined capability, workflow, domain, rubric, or evaluation target.
What types of post-training data can I buy?
Projects can include supervised fine-tuning demonstrations, preference data, RLHF data, rubric- and verifier-based reinforcement-learning data, tool-calling trajectories, computer-use tasks, and expert-reviewed outputs.
Can AfterQuery build model evaluations or benchmarks?
Yes. AfterQuery designs custom evaluations and benchmarks that measure specified model capabilities using realistic tasks, expert-developed rubrics, and verifiable outcomes where possible.
How is custom AI training data priced?
Pricing depends on scope, domain expertise, task complexity, tooling, quality controls, security requirements, and delivery volume. A pilot is usually the best way to establish the production design and budget.
What should an LLM training-data agreement cover?
Before production, define the permitted training and evaluation uses, data rights, confidentiality, security, retention, delivery schema, quality criteria, acceptance testing, and treatment of derived model artifacts.
How do I start an AI data project with AfterQuery?
Contact AfterQuery with the model or agent capability you want to improve, the domain, example tasks, evaluation criteria, approximate scale, and timeline. The team can then recommend a pilot and production approach.
Buy an existing dataset or start with a pilot
Share your model, target capability, domain, example tasks, evaluation criteria, expected scale, and timeline. AfterQuery will recommend an available dataset or a custom pilot with the right task design, environment, expert review, and quality gates.
Last reviewed August 31, 2026.