Train agents on the interactions that matter.
Persona-conditioned datasets that cover the goals, preferences, and constraints of real users.
What we deliver
SFT dataInstruction-response pairs and multi-turn conversations.
TrajectoriesConversations, tool calls, actions, and outcomes.
Preference dataAlternative behaviors with quality judgments.
Failure-focused setsExamples aimed at weaknesses found in evaluation.
DocumentationSpecs, coverage analysis, and generation methods.
Agreed formatsDelivered to fit your training pipeline.
From simulation to training data.
- 01
Define target behaviors
Capabilities, failure modes, formats.
- 02
Generate diverse scenarios
Persona-conditioned tasks and interactions.
- 03
Validate and filter
Checks, consistency analysis, human review.
- 04
Deliver
Datasets, metadata, and quality documentation.
Data for every kind of agent
Personal agentsPreference-aware conversations and task trajectories.
Chat assistantsMulti-turn dialogues across user needs.
Coding agentsDeveloper-persona sessions with evolving requirements.
Scientific agentsEvidence-grounded reasoning and research-coding tasks.
Quality is a design requirement.
Agreed with you before large-scale generation.
- Persona consistency
- Factual grounding
- Task validity
- Instruction adherence
- Outcome verification
- Diversity
- Expert review
Fluent text is not treated as proof of correctness. For science, simulation supports validation; it does not replace it.
Build the dataset your agent actually needs.
From fine-tuning examples to multi-turn trajectories.