Proprietary Synthetic Data

High-Fidelity Agentic Reasoning Trajectories generated securely on our bare-metal clusters.

Data Packet Preview

Download a sample of our high-fidelity agentic reasoning trajectories. Includes AST analysis, memory utilization, and reward scoring.

Download .JSONL (5kb)

Our Generation Process

Our proprietary datasets are deterministically generated using heavily orchestrated Swarm Architectures running on our secure NVIDIA A100 and B200 clusters. We do not scrape public internet data. Every trajectory is fully synthesized within isolated, sandboxed environments.

By simulating complex engineering tasks across massive codebases, we capture multi-agent debate, AST-level mutation attempts, memory retrieval metrics, and verifiable reward scoring. This data is the critical missing piece for fine-tuning your foundational LLMs or evaluating your internal RAG systems.

Data Structure & Schema

Each .jsonl packet conforms to a strict, parseable schema designed for immediate ingestion into fine-tuning pipelines (e.g., HuggingFace datasets or PyTorch DataLoaders).

{
  "id": "string (unique trajectory identifier)",
  "task": "string (the original problem statement)",
  "environment": "string (hardware/sandbox context)",
  "duration_sec": "float (execution time)",
  "reward": "float (0.0 to 1.0 success metric)",
  "ast_mutations_attempted": "integer (number of code changes)",
  "memory_retrieval_count": "integer (RAG context hits)",
  "outcome": "string (SUCCESS | FAILURE)"
}

Need Custom Synthetic Data?

We take custom requests. If your foundational models require highly specific reasoning trajectories, adversarial examples, or niche domain-specific codebases, we can orchestrate a targeted swarm to generate exactly what you need.

Request Custom Dataset