AI Research Observability
Every protein folding prediction, every game AI move, every training runโtracked with complete audit trails. Make scientific AI discoveries reproducible, explainable, and compliant.
Live Research Event Stream
Protein Folding Prediction Pipeline
Research Query Examples
๐งฌ Protein Prediction Audit
Track every structure prediction with confidence scores, input sequences, and model versions.
WHERE event_id LIKE 'research.structure.%'
AND data.plddt_score > 90
AND timestamp > NOW() - INTERVAL '24h'
โ๏ธ Game AI Move Decisions
Reconstruct the decision tree for any game move, including MCTS search depth and value estimates.
value_estimate, policy_prior
FROM events
WHERE event_id = 'gameai.move.selected:1'
AND game_id = 'match_2026_finals'
๐ Training Run Analysis
Monitor training metrics, hyperparameters, and resource usage across distributed experiments.
gpu_utilization, memory_used
FROM events
WHERE event_id = 'research.training.epoch:1'
AND experiment_id = 'foundation_v3_pretrain'
๐ฌ Scientific Discovery Chain
Trace the full lineage of a scientific insight from data to hypothesis to validation.
evidence_chain, confidence
FROM events
WHERE event_id LIKE 'research.discovery.%'
AND domain = 'materials_science'
โ Without Event Model
Research decisions lost in black boxes
โ With Event Model
Complete scientific reproducibility
Why Observable AI Research?
Reproducibility
Every experiment fully reproducible with exact hyperparameters, seeds, and data snapshots.
Explainability
Trace any prediction back to the specific model weights, training data, and inference steps.
Peer Review Ready
Provide reviewers with complete decision chains and experimental methodology.
Ethics Compliance
Document responsible AI practices and safety evaluations for every deployed model.
Hypothesis Tracking
Log scientific hypotheses, experiments, and evidence chains for systematic research.
Resource Optimization
Track compute costs, carbon footprint, and resource efficiency across experiments.
Regulatory & Scientific Compliance
EU AI Act
High-risk AI system documentation, transparency obligations
NIST AI RMF
AI risk management framework compliance
FAIR Principles
Findable, Accessible, Interoperable, Reusable research data
Nature Guidelines
AI transparency requirements for scientific publishing
FDA/EMA
Drug discovery AI audit trails and validation
ISO 42001
AI management system certification
Make AI Research Reproducible
Join leading research institutions using the Event Model for transparent, auditable AI science.