# The human data layer for post-training

Evaluation. Alignment. Agentic tasks. Domain expertise. Data services for the teams building frontier models.

## AI Research Labs Consultation

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.

### 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

### 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

### 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)

### 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

### 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

### 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

### Process reward modeling

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

### Verifiable rollouts

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

### Expert rubric grading

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

### 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.

### 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.

### 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.