The human data layer for post-training

Your post-training researchers are designing data generation pipelines, not managing vendors. We operate the human side of that pipeline so they don't have to.

• 01

  Post-Training Data

  Human feedback, preference data, and reward signals calibrated to your model's capability level.

  • Pairwise preference ranking on custom rubrics (helpfulness, reasoning, factuality, instruction-following)
  • Reward model training data across dimensions you define
  • DPO preference pairs and constitutional principle ratings
  • Human verification of model-assisted and RLAIF pipeline outputs
• 02

  Evaluation Data

  Evaluation is its own discipline now. We build the datasets your eval teams run against.

  • Custom benchmarks beyond MMLU, built for your model's target capabilities
  • Human eval campaigns with calibrated judges and full provenance
  • Agentic task evaluation: did the agent complete the multi-step workflow correctly?
  • Reasoning chain validation with step-by-step expert traces of CoT outputs
  • Comparative analysis across model versions with per-dimension breakdowns
• 03

  Safety & Alignment

  For labs shipping open-weight models or selling to sovereign customers, safety data is a compliance requirement, not a nice-to-have.

  • Red-team and adversarial evaluation datasets
  • Safety alignment labeling on your model spec dimensions
  • Automated safety benchmark creation that evolves with model capabilities
  • Multilingual safety for sovereign deployments
  • Regulatory-aligned evaluation (NIST AI RMF, EU AI Act)
• 04

  Domain Experts

  General-purpose raters can't evaluate experimental designs or validate scientific reasoning. We maintain vetted expert pools.

  • PhD scientists across physics, chemistry, biology, materials science
  • Financial and geopolitical analysts
  • Designers, illustrators, and UX researchers for creative and multimodal evaluation
  • Human trainers who craft reward functions alongside your RL researchers
• 05

  Multimodal & Spatial Data

  Your models learn from video, images, audio, and 3D space. The training data needs human quality signals across every modality.

  • 3D data labeling for spatial intelligence and world models
  • Audio alignment, transcription, and segmentation
  • Cross-modal alignment verification
  • Generated content evaluation for fidelity, coherence, and prompt adherence
• 06

  Data Quality Operations

  The quality layer between your data pipeline and your training run.

  • Pre-training corpus curation and filtering at web scale
  • Post-training QA covering rater drift detection and edge case resolution
  • LLM-as-a-Judge validation with human audit of your automated quality scoring
  • Data diversity and contamination auditing

• 
  PRM

  Process reward modeling

  Step-labeled reasoning traces and rejection-sampled rollouts that feed process reward models, not just outcome scores.

• 
  RLVR

  Verifiable rollouts

  Code tests, proof checkers, and grader nodes convert rollouts into verifiable reward signal for RL training.

• 
  Rubrics

  Expert rubric grading

  Calibrated domain experts grading trajectories on the dimensions that matter: correctness, reasoning, efficiency, safety.

• 01

  Built on Label Studio

  Your team already knows the platform. No new tooling to learn, no vendor lock-in, full export flexibility into your training pipeline.

• 02

  Quality at Frontier Scale

  Calibrated rater pools matched to your model's capability level. Real-time inter-annotator agreement tracking. Multi-tier review workflows. Data doesn't ship until quality thresholds are met.

• 03

  Secure by Default

  SOC 2 Type II. Air-gapped deployment in your infrastructure. NDA-covered workforce. Your training data is your moat, and we treat it that way.