[
  {
    "name": "Autonomous Feedback Loops",
    "category": "reinforcement_learning",
    "description": "Continuously tunes marketing experiments with reward shaping, policy updates, and offline evaluation.",
    "signal_keywords": ["reinforcement", "policy", "reward", "loop"],
    "learning_modes": ["offline rl", "batch policy improvement", "counterfactual evaluation"],
    "data_sources": ["campaign telemetry", "crm outcomes", "attribution logs"],
    "feedback_channels": ["reward shaping", "safety critics", "guarded exploration"],
    "tooling": ["ray rllib", "amazon sagemaker rl", "azure personalizer"],
    "metrics": ["policy_return", "exploration_rate", "stability_index"],
    "deployment_modes": ["shadow deployment", "progressive rollout", "multi-armed bandits"],
    "base_maturity": "advanced"
  },
  {
    "name": "Meta-Learning Playbooks",
    "category": "meta_learning",
    "description": "Learns how to learn new channels rapidly using few-shot adaptation and hyperparameter search.",
    "signal_keywords": ["meta", "few-shot", "hyperparameter", "adapt"],
    "learning_modes": ["maml", "reptile", "transfer learning"],
    "data_sources": ["historical experiments", "benchmark corpora", "synthetic blends"],
    "feedback_channels": ["episodic evaluation", "learning rate schedulers", "curriculum design"],
    "tooling": ["pytorch lightning", "weights & biases sweeps", "google vertex ai"],
    "metrics": ["adaptation_speed", "few_shot_accuracy", "generalization_gap"],
    "deployment_modes": ["meta-initialization", "auto ml", "prompt bootstrapping"],
    "base_maturity": "elite"
  },
  {
    "name": "Continuous Experiment Graph",
    "category": "knowledge_distillation",
    "description": "Transfers learnings across campaigns and compresses policies into lightweight deployment artifacts.",
    "signal_keywords": ["distillation", "teacher", "student", "compression"],
    "learning_modes": ["knowledge distillation", "policy compression", "graph propagation"],
    "data_sources": ["experiment graph", "knowledge store", "analytics warehouse"],
    "feedback_channels": ["validation cohorts", "drift detection", "human review"],
    "tooling": ["neptune.ai", "mlflow", "azure ml"],
    "metrics": ["distillation_loss", "cohort_lift", "policy_size"],
    "deployment_modes": ["edge deployment", "on-device agents", "microservices"],
    "base_maturity": "advanced"
  },
  {
    "name": "Self-Healing Learning Mesh",
    "category": "self_supervised",
    "description": "Applies self-supervised objectives to unify marketing signals, detect drift, and auto-correct models.",
    "signal_keywords": ["self-supervised", "contrastive", "drift", "self-heal"],
    "learning_modes": ["contrastive learning", "masked modeling", "representation alignment"],
    "data_sources": ["web analytics", "product telemetry", "support transcripts"],
    "feedback_channels": ["drift alarms", "self-healing patches", "anomaly queues"],
    "tooling": ["hugging face transformers", "databricks", "snowflake ml"],
    "metrics": ["representation_quality", "drift_delay", "auto_fix_rate"],
    "deployment_modes": ["continuous training", "stream processing", "feature store streaming"],
    "base_maturity": "advanced"
  },
  {
    "name": "Strategic Learning Governor",
    "category": "governance",
    "description": "Coordinates active learning, governance approvals, and compliance aware retraining for marketing AI.",
    "signal_keywords": ["governor", "compliance", "active", "governance"],
    "learning_modes": ["active learning", "human in the loop", "policy gating"],
    "data_sources": ["governance ledger", "compliance annotations", "risk reports"],
    "feedback_channels": ["approval workflows", "risk scoring", "model cards"],
    "tooling": ["monte carlo data", "aria", "openlineage"],
    "metrics": ["time_to_approval", "governance_coverage", "risk_score"],
    "deployment_modes": ["governed pipeline", "review gates", "audit trails"],
    "base_maturity": "foundational"
  }
]