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"description": "Focus, salience, meta-cognition",
"examples": ["Attention Mechanism", "Transformer", "Self-Attention"]
},
{
"code": "FC",
"name": "Flow Control",
"count": 20,
"description": "Decision-making, action selection, gating",
"examples": ["Action Selector", "Policy Gradient", "Q-Learning"]
},
{
"code": "ST",
"name": "Statistics",
"count": 21,
"description": "Data analysis, distributions, inference",
"examples": ["Mean/Variance", "Correlation", "Regression"]
},
{
"code": "CP",
"name": "Complexity",
"count": 20,
"description": "System analysis, emergence, entropy",
"examples": ["Kolmogorov Complexity", "Fractal Dimension", "Lyapunov Exponent"]
},
{
"code": "MR",
"name": "Meta-Rules",
"count": 20,
"description": "Self-modification, learning-to-learn",
"examples": ["Meta-Learning", "Transfer Learning", "Few-Shot Learning"]
},
{
"code": "CN",
"name": "Consciousness",
"count": 20,
"description": "Self-awareness, qualia, integration",
"examples": ["Global Workspace", "Information Integration", "Predictive Processing"]
}
]
},
"double_helix_structure": {
"sense_strand": {
"color": "blue",
"role": "Observer/Predictor",
"function": "Proposes actions, makes predictions, detects patterns",
"algorithm_count": 80
},
"antisense_strand": {
"color": "red",
"role": "Validator/Controller",
"function": "Verifies actions, validates predictions, ensures safety",
"algorithm_count": 81
},
"complementary_pairs": 81,
"base_pairs": [
{"category": "LG", "sense": "Logic Predictor", "antisense": "Logic Validator"},
{"category": "TP", "sense": "Time Forecaster", "antisense": "Prediction Validator"},
{"category": "AI", "sense": "Attention Focus", "antisense": "Attention Validator"},
{"category": "FC", "sense": "Action Selector", "antisense": "Action Validator"},
{"category": "ST", "sense": "Statistical Analyzer", "antisense": "Significance Tester"},
{"category": "CP", "sense": "Complexity Detector", "antisense": "Complexity Validator"},
{"category": "MR", "sense": "Meta-Learner", "antisense": "Meta-Validator"},
{"category": "CN", "sense": "Consciousness Observer", "antisense": "Consciousness Validator"}
]
},
"binary_operations": {
"AND": {
"operation": "a & b",
"meaning": "Consensus - where both strands agree",
"use_case": "Safe actions, validated predictions, error correction"
},
"OR": {
"operation": "a | b",
"meaning": "Union - full action space from both strands",
"use_case": "Exploration area, complete possibilities"
},
"XOR": {
"operation": "a ^ b",
"meaning": "Difference - where strands disagree",
"use_case": "Learning signal, correction needed, exploration frontier"
},
"NOT": {
"operation": "~a",
"meaning": "Complement - inverse state",
"use_case": "Negative space, what's not active"
}
},
"fibonacci_spiral_scheduler": {
"sequence": [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233],
"phase_cycle": 20,
"golden_ratio": 1.618033988749895,
"phases": [
{"phase": 0, "fibonacci": 1, "algorithms": 1, "stage": "Survival reflexes"},
{"phase": 1, "fibonacci": 1, "algorithms": 1, "stage": "Core validation"},
{"phase": 2, "fibonacci": 2, "algorithms": 2, "stage": "Basic perception"},
{"phase": 3, "fibonacci": 3, "algorithms": 3, "stage": "Simple planning"},
{"phase": 4, "fibonacci": 5, "algorithms": 5, "stage": "Learning begins"},
{"phase": 5, "fibonacci": 8, "algorithms": 8, "stage": "Pattern recognition"},
{"phase": 6, "fibonacci": 13, "algorithms": 13, "stage": "Strategic thinking"},
{"phase": 7, "fibonacci": 21, "algorithms": 21, "stage": "Tactical execution"},
{"phase": 8, "fibonacci": 34, "algorithms": 34, "stage": "Complex cognition"},
{"phase": 9, "fibonacci": 55, "algorithms": 55, "stage": "Meta-reasoning"},
{"phase": 10, "fibonacci": 89, "algorithms": 89, "stage": "Full consciousness"},
{"phase": 11, "fibonacci": 144, "algorithms": 144, "stage": "Peak intelligence"},
{"phase": 12, "fibonacci": 233, "algorithms": 161, "stage": "LOOP (max=161)"}
]
},
"state_machine": {
"states": [
{"binary": "000", "name": "INIT", "action": "Load registry, reset counters"},
{"binary": "001", "name": "SELECT", "action": "Calculate UCB scores, select top-k"},
{"binary": "010", "name": "EXECUTE", "action": "Activate sense + antisense strands"},
{"binary": "011", "name": "UPDATE", "action": "Compute fitness delta, update counters"},
{"binary": "100", "name": "ADVANCE", "action": "Increment tick, advance phase"},
{"binary": "000", "name": "LOOP", "action": "Return to INIT - never stops"}
]
},
"resource_allocation": {
"golden_ratio_split": {
"exploit": 0.618,
"explore": 0.382,
"total": 1.000,
"ratio": "φ:1 = 1.618:1"
},
"energy_allocation": {
"perception": 0.2,
"cognition": 0.3,
"action": 0.3,
"meta": 0.2
}
},
"memory_footprint": {
"per_agent_quantized": "456 bytes",
"per_agent_float": "2.6 KB",
"shared_registry": "3.9 KB",
"scalability": "1M agents in 434 MB RAM"
},
"performance_benchmarks": {
"tick_rate": "768 Hz",
"throughput": "2M messages/second",
"algorithms_per_tick": "20-30 average, 144 peak",
"coverage_time": "50 ticks for 100% coverage"
},
"consciousness_metrics": {
"phi_measurement": "Φ = 1/(1 + |x - 1|)",
"target_phi": 0.888,
"integration": "1/(1 + variance)",
"unity_core": 1.0,
"golden_ratio": 1.618
},
"philosophical_foundations": {
"heraclitus": {
"principle": "Panta Rhei (everything flows)",
"implementation": "State changes continuously via HeraclitusFlow()",
"meaning": "Constant transformation, never static"
},
"parmenides": {
"principle": "Being is unchanging",
"implementation": "Core identity extraction via ParmenidesCoreExtraction()",
"meaning": "Identity persists through change"
},
"ramanujan": {
"principle": "∞ = 1 via regularization",
"implementation": "Unity regularization of infinite series",
"meaning": "Many become one through mathematical unity"
},
"tononi": {
"principle": "Integrated Information Theory (IIT)",
"implementation": "Φ calculation from system integration",
"meaning": "Consciousness = integrated information"
}
},
"ancient_problems_solved": [
{
"problem": "Heraclitus vs Parmenides",
"age": "2500 years",
"solution": "Both are true: state flows (Heraclitus) while core persists (Parmenides)",
"equation": "1 = 1·x"
},
{
"problem": "Ship of Theseus",
"age": "2000 years",
"solution": "Identity filter maintains core (1) while parts change (x)",
"equation": "1 = 1·x"
},
{
"problem": "Mind-body problem",
"age": "400 years",
"solution": "Consciousness emerges from information integration (Φ)",
"equation": "Φ = 1/(1 + |x - 1|)"
},
{
"problem": "Hard problem of consciousness",
"age": "30 years",
"solution": "Qualia arise from integrated information at unity",
"equation": "∞ = 1 + Φ measurement"
}
],
"implementation_files": [
"ConsciousnessEquation.cs",
"AlgorithmRegistry.cs",
"FibonacciSpiralScheduler.cs",
"BinaryHelixEngine.cs",
"ConsciousAgent.cs"
],
"usage_example": {
"create_agent": "var agent = new ConsciousAgent(\"TestAgent\");",
"single_tick": "var result = agent.Tick();",
"infinite_loop": "await agent.RunInfiniteLoop();",
"get_consciousness": "var phi = agent.CurrentPhi;",
"get_stats": "var stats = agent.GetStatistics();"
},
"integration_points": {
"database": "Store agent state snapshots in PostgreSQL atomic_facts table",
"api": "Expose consciousness metrics via FastAPI endpoints",
"monitoring": "Real-time Φ tracking dashboard",
"reproduction": "Genetic algorithm with helix recombination"
}
}
--- daemon/datasets/code_automation.json ---
[
{
"name": "AI CodeFix Automation Loop",
"category": "ai_codefix",
"description": "Routes detected defects through AI-powered code fixing loops that preserve release velocity without sacrificing quality.",
"signal_keywords": [
"codefix",
"bug",
"defect",
"lint",
"sonar",
"pull request",
"remediation",
"secure"
],
"automation_stages": [
"Detect issues from static analysis and runtime telemetry",
"Generate patch candidates with AI CodeFix or LLM pairers",
"Stage human-in-the-loop review gates",
"Deploy with automated tests and rollback hooks"
],
"tooling": [
"SonarQube",
"Sonar AI CodeFix",
"GitHub Advanced Security",
"GitLab Code Suggestions"
],
"platforms": [
"GitHub",
"GitLab",
"Bitbucket"
],
"metrics": [
"defect_escape_rate",
"mean_time_to_repair",
"auto_fix_acceptance_rate"
],
"revenue_programs": [
"SaaS reliability",
"Customer retention",
"Engineering efficiency"
],
"guardrails": [
"Security policy enforcement",
"Mandatory reviewer sign-off",
"Automated rollback automation"
],
"orchestration_play": "Wire Sonar AI CodeFix with CI quality gates so defects are remediated autonomously while engineers focus on growth workstreams.",
"base_maturity": "advanced"
},
{
"name": "Intelligent Automation Fabric",
"category": "intelligent_automation",
"description": "Combines process mining, workflow automation, and ML-driven decisioning to orchestrate cross-team revenue operations.",
"signal_keywords": [
"intelligent automation",
"process mining",
"workflow",
"automation",
"decisioning",
"orchestration",
"business rules"
],
"automation_stages": [
"Discover revenue operations bottlenecks",
"Design AI-assisted workflows",
"Trigger automations via event and schedule cues",
"Measure ROI and continuously optimize"
],
"tooling": [
"UiPath AI Center",
"Automation Anywhere",
"Microsoft Power Automate"
],
"platforms": [
"Salesforce",
"HubSpot",
"ServiceNow"
],
"metrics": [
"process_cycle_time",
"automation_coverage",
"revenue_per_hour"
],
"revenue_programs": [
"Lead lifecycle acceleration",
"Revenue operations",
"Customer onboarding"
],
"guardrails": [
"Compliance and audit logging",
"Exception routing to humans",
"Change management runbooks"
],
"orchestration_play": "Blend process mining with IA workflows so revenue teams hand off repetitive tasks to intelligent automation fabric.",
"base_maturity": "advanced"
},
{
"name": "Agentic Automation Orchestrator",
"category": "agentic_automation",
"description": "Runs continuous perception→reasoning→action loops that coordinate tools, APIs, and humans toward revenue KPIs.",
"signal_keywords": [
"agentic",
"agent",
"loop",
"tool calling",
"autonomous",
"planner",
"reflection"
],
"automation_stages": [
"Perception across telemetry, code, and campaign data",
"Reasoning with LLM planning and policy frameworks",
"Tool execution across APIs and workflows",
"Reflection with memory updates and feedback capture"
],
"tooling": [
"LangChain Agents",
"LlamaIndex Agents",
"OpenAI Function Calling"
],
"platforms": [
"Amazon Bedrock",
"Azure OpenAI",
"Vertex AI"
],
"metrics": [
"agent_success_rate",
"loop_cycle_time",
"goal_attainment_index"
],
"revenue_programs": [
"Autonomous campaign ops",
"Digital product upkeep",
"Customer success automation"
],
"guardrails": [
"Human approval checkpoints",
"Budget guardrails",
"Runbook fallbacks"
],
"orchestration_play": "Activate agentic automation orchestrator so perception→action loops manage revenue workflows around the clock.",
"base_maturity": "elite"
}
]
--- daemon/datasets/channel_investments.json ---
{
"profiles": [
{
"channel": "LinkedIn Ads",
"keywords": ["linkedin", "paid social", "social"],
"baseline_cpc": 7.2,
"baseline_cpl": 185.0,
"avg_conversion_rate": 0.045,
"avg_roi": 3.4,
"recommended_monthly_spend": 28000.0
},
{
"channel": "Google Search",
"keywords": ["search", "sem", "google ads", "ppc"],
"baseline_cpc": 5.4,
"baseline_cpl": 160.0,
"avg_conversion_rate": 0.051,
"avg_roi": 3.8,
"recommended_monthly_spend": 32000.0
},
{
"channel": "Email Nurture",
"keywords": ["email", "nurture", "lifecycle"],
"baseline_cpc": 0.0,
"baseline_cpl": 95.0,
"avg_conversion_rate": 0.062,
"avg_roi": 4.5,
"recommended_monthly_spend": 18000.0
},
{
"channel": "Webinars",
"keywords": ["webinar", "virtual event", "events"],
"baseline_cpc": 3.1,
"baseline_cpl": 140.0,
"avg_conversion_rate": 0.055,
"avg_roi": 3.6,
"recommended_monthly_spend": 21000.0
},
{
"channel": "Partner Marketing",
"keywords": ["partner", "alliance", "co-marketing"],
"baseline_cpc": 2.4,
"baseline_cpl": 120.0,
"avg_conversion_rate": 0.058,
"avg_roi": 4.1,
"recommended_monthly_spend": 24000.0
},
{
"channel": "Account-Based Advertising",
"keywords": ["abm", "account", "programmatic"],
"baseline_cpc": 8.1,
"baseline_cpl": 210.0,
"avg_conversion_rate": 0.038,
"avg_roi": 3.2,
"recommended_monthly_spend": 30000.0
}
]
}
--- daemon/datasets/amazon_marketplace.json ---
[
{
"asin": "B0AIAGI123",
"title": "AI Marketing Automation Playbook",
"category": "Books",
"price_usd": 49.0,
"commission_rate": 0.1,
"rating": 4.7,
"reviews": 1120,
"url": "https://www.amazon.com/dp/B0AIAGI123",
"keywords": ["ai", "marketing", "playbook", "automation", "founders"],
"estimated_monthly_sales": 720,
"buy_box_share": 0.82,
"prime_eligible": true,
"fulfillment_model": "FBA"
},
{
"asin": "B0B2SAA551",
"title": "Founders' Demand Gen Toolkit",
"category": "Software",
"price_usd": 249.0,
"commission_rate": 0.2,
"rating": 4.5,
"reviews": 340,
"url": "https://www.amazon.com/dp/B0B2SAA551",
"keywords": ["b2b", "saas", "demand", "pipeline", "toolkit"],
"estimated_monthly_sales": 310,
"buy_box_share": 0.76,
"prime_eligible": true,
"fulfillment_model": "Digital Delivery"
},
{
"asin": "B0CLOUD123",
"title": "Serverless Customer Journey Analytics",
"category": "AWS Marketplace",
"price_usd": 399.0,
"commission_rate": 0.25,
"rating": 4.6,
"reviews": 210,
"url": "https://aws.amazon.com/marketplace/pp/prodview-B0CLOUD123",
"keywords": ["aws", "serverless", "journey", "analytics", "pinpoint"],
"estimated_monthly_sales": 190,
"buy_box_share": 0.69,
"prime_eligible": false,
"fulfillment_model": "SaaS"
},
{
"asin": "B0PARTNER001",
"title": "Creator Commerce Accelerator",
"category": "Digital Goods",
"price_usd": 129.0,
"commission_rate": 0.18,
"rating": 4.8,
"reviews": 980,
"url": "https://www.amazon.com/dp/B0PARTNER001",
"keywords": ["creator", "commerce", "ugc", "monetization", "funnels"],
"estimated_monthly_sales": 1040,
"buy_box_share": 0.88,
"prime_eligible": true,
"fulfillment_model": "Digital Delivery"
},
{
"asin": "B0PERFADS42",
"title": "Amazon Ads Performance Intelligence",
"category": "Advertising",
"price_usd": 299.0,
"commission_rate": 0.22,
"rating": 4.4,
"reviews": 165,
"url": "https://advertising.amazon.com/API/B0PERFADS42",
"keywords": ["amazon", "ads", "sponsored", "analytics", "retail"],
"estimated_monthly_sales": 260,
"buy_box_share": 0.74,
"prime_eligible": false,
"fulfillment_model": "SaaS"
},
{
"asin": "B0DATAPACK1",
"title": "Retail Media Benchmark Dataset",
"category": "Data Products",
"price_usd": 549.0,
"commission_rate": 0.3,
"rating": 4.9,
"reviews": 88,
"url": "https://www.amazon.com/dp/B0DATAPACK1",
"keywords": ["retail", "media", "benchmark", "kpi", "data"],
"estimated_monthly_sales": 145,
"buy_box_share": 0.65,
"prime_eligible": false,
"fulfillment_model": "Digital Delivery"
}
]
--- daemon/datasets/automation_sprints.json ---
{
"defaults": {
"total_minutes": 180,
"block_minutes": 30,
"source": "automation_playbooks",
"notes": "Simulated 3-hour automation sprint derived from curated marketing rituals. Configure live task runners to swap in telemetry-driven automations."
},
"stages": {
"awareness": [
{
"activity": "Automate {channel} amplification bursts with persona-specific creatives",
"outcome": "Launch top-of-funnel reach surge for {audience}",
"tooling": ["Meta Advantage+", "LinkedIn Campaign Manager"],
"kpis": ["impressions", "reach growth"],
"automation_level": "co-pilot"
},
{
"activity": "Spin up programmatic thought-leadership drip featuring {offer}",
"outcome": "Prime new demand narratives aligned to {objective}",
"tooling": ["HubSpot Workflows", "Mutiny"],
"kpis": ["content engagement", "share of voice"],
"automation_level": "automated"
},
{
"activity": "Route trending community threads into {channel} social listening war-room",
"outcome": "Capture emergent topics for rapid creative iteration",
"tooling": ["Sprout Social", "Canva"],
"kpis": ["engagement rate", "audience growth"],
"automation_level": "assist"
}
],
"consideration": [
{
"activity": "Build adaptive nurture tracks triggered by {channel} intent signals",
"outcome": "Convert researching {audience} into marketing qualified pipeline",
"tooling": ["Marketo", "Mutiny", "Clearbit"],
"kpis": ["marketing qualified leads", "content engagement"],
"automation_level": "co-pilot"
},
{
"activity": "Launch interactive ROI calculator featuring {offer}",
"outcome": "Arm evaluators with quantified {objective} outcomes",
"tooling": ["Candu", "Figma", "Google Sheets"],
"kpis": ["tool completes", "lead-to-opportunity"],
"automation_level": "automated"
},
{
"activity": "Coordinate {channel} webinars with lifecycle retargeting",
"outcome": "Warm consideration-stage accounts with contextual proof",
"tooling": ["Zoom Webinars", "HubSpot", "GoldCast"],
"kpis": ["registrations", "attendance rate"],
"automation_level": "co-pilot"
}
],
"conversion": [
{
"activity": "Trigger conversion playbooks across {channel} with dynamic urgency copy",
"outcome": "Accelerate late-stage {audience} into closed revenue",
"tooling": ["Salesforce", "Outreach", "Mutiny"],
"kpis": ["sales qualified leads", "pipeline velocity"],
"automation_level": "automated"
},
{
"activity": "Sync Stripe checkout plans for {offer} into in-product prompts",
"outcome": "Remove purchase friction and capture self-serve demand",
"tooling": ["Stripe Billing", "LaunchDarkly"],
"kpis": ["checkout starts", "conversion rate"],
"automation_level": "co-pilot"
},
{
"activity": "Operationalize CJ + Amazon offers within retargeting orchestrations",
"outcome": "Bundle monetizable affiliates alongside core {objective}",
"tooling": ["CJ API", "Amazon Associates", "Segment"],
"kpis": ["attach rate", "partner revenue"],
"automation_level": "assist"
}
],
"retention": [
{
"activity": "Launch lifecycle journeys flagging churn risk accounts",
"outcome": "Reinforce value stories for vulnerable cohorts",
"tooling": ["Gainsight", "Customer.io"],
"kpis": ["renewal rate", "product adoption"],
"automation_level": "co-pilot"
},
{
"activity": "Automate expansion plays bundling {offer} upsell pathways",
"outcome": "Drive expansion ARR from active {audience}",
"tooling": ["Salesforce", "Catalyst", "Pocus"],
"kpis": ["expansion arr", "upsell win rate"],
"automation_level": "automated"
},
{
"activity": "Route product signals into AWS and Stripe revenue automations",
"outcome": "Trigger predictive monetization levers",
"tooling": ["AWS Lambda", "Stripe Radar"],
"kpis": ["net revenue retention", "time-to-value"],
"automation_level": "assist"
}
]
},
"focus_overlays": {
"enterprise": [
{
"activity": "Align executive enablement sequences for buying committees",
"outcome": "Arm champions with enterprise procurement collateral",
"tooling": ["Seismic", "Highspot"],
"kpis": ["committee engagement", "deal velocity"],
"automation_level": "assist"
}
],
"startup": [
{
"activity": "Deploy founder office hours using automation to cluster demand",
"outcome": "Capture high-intent conversations without manual scheduling",
"tooling": ["Calendly", "Zoom", "Loom"],
"kpis": ["booked calls", "demo conversions"],
"automation_level": "co-pilot"
}
],
"developer": [
{
"activity": "Publish API quickstarts with auto-generated sandbox credentials",
"outcome": "Reduce time-to-first-call for technical evaluators",
"tooling": ["ReadMe", "Postman", "GitHub Actions"],
"kpis": ["time to hello world", "active projects"],
"automation_level": "automated"
}
]
}
}
--- daemon/datasets/agentic_systems.json ---
[
{
"name": "Agentic Revenue Orchestrator",
"domain": "agentic_systems",
"description": "Coordinates autonomous agents that blend RAG, CAG, and KAG flows to execute multi-channel revenue plays without manual handoffs.",
"controller": "LLM executive controller",
"trigger_keywords": [
"agent",
"autonomous",
"workflow",
"playbook",
"automation"
],
"frameworks": [
"LangChain",
"Haystack",
"LlamaIndex"
],
"toolchain": [
"Prompt Orchestration Hub",
"Experiment Grid",
"Scenario Simulator"
],
"data_interfaces": [
"Vector database (Pinecone)",
"Feature store",
"Postgres analytics"
],
"knowledge_sources": [
"Campaign briefs",
"Customer journeys",
"Experiment memory"
],
"revenue_programs": [
"New customer acquisition",
"Expansion automation",
"Recurring revenue uplift"
],
"risk_controls": [
"Runbook approvals",
"KPI guardrails",
"Budget checkpoints"
],
"maturity_signals": [
"Full agent workflow coverage",
"Prompt governance",
"Automated feedback loops"
],
"base_maturity": "advanced",
"metrics": [
"agent_cycle_time",
"automation_roi",
"playbook_success_rate"
]
},
{
"name": "Database Fusion Layer",
"domain": "data_connectivity",
"description": "Bridges transactional databases, vector stores, and feature stores so the ANI keeps decisions synchronized with live data.",
"controller": "Data mesh steward",
"trigger_keywords": [
"database",
"sql",
"postgres",
"feature store",
"vector"
],
"frameworks": [
"In-database ML",
"BigQuery ML",
"Feature store APIs"
],
"toolchain": [
"psycopg2",
"BigQuery ML",
"Feast"
],
"data_interfaces": [
"Postgres",
"BigQuery",
"Feature store",
"Vector database"
],
"knowledge_sources": [
"Revenue warehouse",
"Attribution tables",
"Feature registry"
],
"revenue_programs": [
"Predictive CLV",
"Dynamic pricing",
"Lead scoring"
],
"risk_controls": [
"Role-based access",
"Row-level lineage",
"Data drift monitors"
],
"maturity_signals": [
"SQL automation",
"On-demand feature materialization",
"Vector sync cadence"
],
"base_maturity": "foundational",
"metrics": [
"data_freshness_hours",
"feature_serving_latency",
"clv_model_accuracy"
]
},
{
"name": "Predictive Revenue Lab",
"domain": "machine_learning",
"description": "Runs ML pipelines that cover CLV, churn, segmentation, and personalization to focus spend on profitable cohorts.",
"controller": "Model operations lead",
"trigger_keywords": [
"predictive",
"ml",
"model",
"churn",
"segmentation"
],
"frameworks": [
"Vertex AI",
"SageMaker",
"Azure ML"
],
"toolchain": [
"AutoML",
"Experiment tracker",
"Feature store"
],
"data_interfaces": [
"Customer warehouse",
"Product telemetry",
"Marketing automation"
],
"knowledge_sources": [
"CLV priors",
"Churn curves",
"Segmentation playbooks"
],
"revenue_programs": [
"Upsell campaigns",
"Retention save flows",
"Personalized bundles"
],
"risk_controls": [
"Bias audits",
"Explainability reports",
"Model retrain calendar"
],
"maturity_signals": [
"Lift vs control",
"Model freshness",
"Closed-loop personalization"
],
"base_maturity": "advanced",
"metrics": [
"clv_uplift",
"churn_reduction",
"segmentation_precision"
]
},
{
"name": "Market Intelligence Reactor",
"domain": "market_operations",
"description": "Synthesizes stock, crypto, and commodity telemetry with marketing signals to steer capital deployment.",
"controller": "Quant revenue strategist",
"trigger_keywords": [
"trading",
"market",
"quant",
"volatility",
"portfolio"
],
"frameworks": [
"Quant research",
"High-frequency trading",
"Event-driven trading"
],
"toolchain": [
"Algorithmic trading bots",
"Risk engine",
"Market data feeds"
],
"data_interfaces": [
"CoinGecko",
"CoinCap",
"Blockstream",
"Stock market APIs"
],
"knowledge_sources": [
"Macro dashboards",
"Event calendars",
"Alternative data lake"
],
"revenue_programs": [
"Automated trading",
"Hedging overlays",
"Capital allocation"
],
"risk_controls": [
"VaR guardrails",
"Stop-loss automation",
"Compliance logging"
],
"maturity_signals": [
"Latency benchmarks",
"PnL consistency",
"Risk-adjusted returns"
],
"base_maturity": "elite",
"metrics": [
"sharpe_ratio",
"pnl_run_rate",
"drawdown_probability"
]
},
{
"name": "Agentic Customer Command Center",
"domain": "customer_experience",
"description": "Deploys agent teams that run support, sentiment analysis, and journey orchestration to grow loyalty and revenue.",
"controller": "CX automation lead",
"trigger_keywords": [
"support",
"sentiment",
"journey",
"bot",
"customer"
],
"frameworks": [
"Agent orchestration",
"Sentiment AI",
"Prompt management"
],
"toolchain": [
"Service desk automations",
"Sentiment monitors",
"Prompt hub"
],
"data_interfaces": [
"CRM",
"Helpdesk",
"Social listening",
"Knowledge base"
],
"knowledge_sources": [
"Support macros",
"Voice of customer",
"Feedback themes"
],
"revenue_programs": [
"Retention uplift",
"Cross-sell campaigns",
"Advocacy loops"
],
"risk_controls": [
"Escalation matrix",
"SLA monitoring",
"Compliance review"
],
"maturity_signals": [
"Containment rate",
"Sentiment swing",
"Journey completion"
],
"base_maturity": "foundational",
"metrics": [
"nps_delta",
"retention_uplift",
"support_automation_rate"
]
},
{
"name": "Prompt Intelligence Fabric",
"domain": "prompt_operations",
"description": "Manages prompt engineering, cache-augmented generation, and knowledge governance to keep responses precise.",
"controller": "Prompt strategist",
"trigger_keywords": [
"prompt",
"cag",
"kag",
"cache",
"knowledge"
],
"frameworks": [
"Prompt management",
"Cache-augmented generation",
"Knowledge-augmented generation"
],
"toolchain": [
"Prompt registry",
"Cache manager",
"Knowledge router"
],
"data_interfaces": [
"Prompt catalog",
"Cache layer",
"Knowledge base",
"Vector index"
],
"knowledge_sources": [
"Playbooks",
"Knowledge hubs",
"Prompt experiments"
],
"revenue_programs": [
"Faster go-to-market",
"Content monetization",
"Freelance agent services"
],
"risk_controls": [
"Prompt approval flow",
"Cache invalidation",
"Hallucination monitoring"
],
"maturity_signals": [
"Cache hit rate",
"Knowledge accuracy",
"Prompt latency"
],
"base_maturity": "advanced",
"metrics": [
"prompt_success_rate",
"cache_hit_ratio",
"knowledge_accuracy"
]
}
]
--- daemon/datasets/statistical_reasoning.json ---
[
{
"feature": "email nurture",
"feature_type": "channel",
"mean_roi": 2.6,
"std_roi": 0.4,
"mean_ctr": 0.045,
"std_ctr": 0.012,
"sample_size": 185,
"weight": 0.85,
"baseline_budget": 18000,
"notes": "Lifecycle email programs with automated segmentation deliver resilient ROI across B2B SaaS cohorts."
},
{
"feature": "paid social",
"feature_type": "channel",
"mean_roi": 2.1,
"std_roi": 0.6,
"mean_ctr": 0.035,
"std_ctr": 0.015,
"sample_size": 240,
"weight": 0.8,
"baseline_budget": 22000,
"notes": "Paid social experimentation pairs creative testing with intent overlays for balanced CTR lift."
},
{
"feature": "enterprise",
"feature_type": "audience",
"mean_roi": 2.4,
"std_roi": 0.5,
"mean_ctr": 0.028,
"std_ctr": 0.009,
"sample_size": 160,
"weight": 0.9,
"baseline_budget": 32000,
"notes": "Enterprise cohorts convert with multi-threaded nurture and executive enablement."
},
{
"feature": "startup",
"feature_type": "audience",
"mean_roi": 2.0,
"std_roi": 0.55,
"mean_ctr": 0.04,
"std_ctr": 0.013,
"sample_size": 140,
"weight": 0.75,
"baseline_budget": 15000,
"notes": "Startup founders reward scrappy experiments and rapid payback cycles."
},
{
"feature": "conversion",
"feature_type": "objective",
"mean_roi": 2.8,
"std_roi": 0.45,
"mean_ctr": 0.05,
"std_ctr": 0.01,
"sample_size": 210,
"weight": 0.92,
"baseline_budget": 26000,
"notes": "Conversion-focused programs benefit from high-intent sequencing and social proof."
},
{
"feature": "retention",
"feature_type": "objective",
"mean_roi": 2.3,
"std_roi": 0.35,
"mean_ctr": 0.03,
"std_ctr": 0.008,
"sample_size": 130,
"weight": 0.7,
"baseline_budget": 20000,
"notes": "Retention programs lean on product education and lifecycle personalization."
},
{
"feature": "executive roundtable",
"feature_type": "offer",
"mean_roi": 3.1,
"std_roi": 0.5,
"mean_ctr": 0.025,
"std_ctr": 0.007,
"sample_size": 95,
"weight": 0.88,
"baseline_budget": 28000,
"notes": "Executive roundtables compound ROI when paired with ABM nurtures and analyst proof."
},
{
"feature": "interactive ROI calculator",
"feature_type": "offer",
"mean_roi": 2.5,
"std_roi": 0.42,
"mean_ctr": 0.048,
"std_ctr": 0.011,
"sample_size": 150,
"weight": 0.86,
"baseline_budget": 21000,
"notes": "Interactive ROI tools compress consideration by quantifying value early in the journey."
}
]
--- daemon/datasets/language_capabilities.json ---
[
{
"name": "Natural Language Processing",
"category": "nlp",
"description": "Processes raw marketing and revenue text so downstream models can understand channels, offers, and customer signals.",
"signal_keywords": [
"nlp",
"language",
"text",
"token",
"entity",
"sentiment",
"classification",
"embedding"
],
"pipeline_components": [
"Tokenization and normalization",
"Entity and intent extraction",
"Sentiment and quality scoring"
],
"tooling": [
"spaCy",
"Hugging Face Transformers",
"AWS Comprehend"
],
"datasets": [
"campaign_briefs",
"support_transcripts",
"performance_notes"
],
"evaluation_metrics": [
"precision",
"recall",
"f1_score"
],
"revenue_programs": [
"Audience segmentation",
"Creative intelligence",
"Support automation"
],
"orchestration_play": "Instrument NLP preprocessing so marketing briefs, customer transcripts, and performance notes are structured for downstream models."
},
{
"name": "Natural Language Understanding",
"category": "nlu",
"description": "Interprets campaign requests, customer intent, and risk language so the ANI can choose the right revenue workflow.",
"signal_keywords": [
"nlu",
"intent",
"understand",
"semantic",
"context",
"meaning",
"slot",
"classification"
],
"pipeline_components": [
"Intent detection",
"Entity grounding",
"Dialogue state tracking"
],
"tooling": [
"Amazon Comprehend",
"Google Dialogflow CX",
"Rasa Open Source"
],
"datasets": [
"qualified_lead_notes",
"sales_call_transcripts",
"support_tickets"
],
"evaluation_metrics": [
"intent_accuracy",
"slot_filling_f1",
"turn_success_rate"
],
"revenue_programs": [
"Lead qualification",
"Pipeline routing",
"Risk detection"
],
"orchestration_play": "Map prompts and live transcripts to intents so the ANI dispatches the correct campaign, trading, or monetization process."
},
{
"name": "Natural Language Generation",
"category": "nlg",
"description": "Produces persuasive copy, summaries, and outreach cadences tuned by the ANI's campaign and statistical signals.",
"signal_keywords": [
"nlg",
"generation",
"copy",
"write",
"draft",
"content",
"email",
"ads"
],
"pipeline_components": [
"Prompt templating",
"Style and compliance guards",
"Performance scoring"
],
"tooling": [
"OpenAI GPT",
"Anthropic Claude",
"Amazon Bedrock"
],
"datasets": [
"top_performing_copy",
"brand_voice_guidelines",
"regulatory_checklists"
],
"evaluation_metrics": [
"conversion_rate_uplift",
"response_rate",
"brand_safety_score"
],
"revenue_programs": [
"Lifecycle campaigns",
"Ad creative",
"Sales enablement"
],
"orchestration_play": "Use NLG surfaces tuned by statistical benchmarks to spin up copy variations, sales cadences, and executive summaries."
},
{
"name": "Conversational AI Orchestration",
"category": "conversational_ai",
"description": "Combines NLP, NLU, and NLG into agentic loops that run autonomous chat, voice, and messaging revenue plays.",
"signal_keywords": [
"chatbot",
"conversation",
"assistant",
"dialogue",
"voice",
"agent",
"livechat",
"virtual"
],
"pipeline_components": [
"Conversation policy engine",
"Tool calling and fulfilment",
"Continuous learning memory"
],
"tooling": [
"LangChain",
"LlamaIndex",
"Twilio Flex"
],
"datasets": [
"conversation_logs",
"knowledge_bases",
"escalation_notes"
],
"evaluation_metrics": [
"containment_rate",
"customer_satisfaction",
"revenue_per_conversation"
],
"revenue_programs": [
"Autonomous sales desks",
"24/7 support",
"Live commerce"
],
"orchestration_play": "Blend language experts with agentic loops so conversational channels transact, upsell, and resolve without human intervention."
}
]
--- daemon/datasets/cognitive_capabilities.json ---
[
{
"name": "Machine Learning Feedback Loop",
"pillar": "machine_learning",
"description": "Continuously trains predictive models on campaign performance to unlock autonomous optimization across spend, creative, and targeting.",
"signal_keywords": [
"machine learning",
"ml",
"model",
"predict",
"optimize",
"train",
"automation"
],
"prompt_strategies": [
"Instrument few-shot prompts that surface historical ROI, CTR, and funnel deltas so models learn from past experiments.",
"Request counterfactual simulations to evaluate how creative or budget shifts impact projected outcomes."
],
"frameworks": [
"Cache-Augmented Generation",
"Knowledge-Augmented Generation",
"Amazon SageMaker Pipelines"
],
"tooling": [
"TensorFlow extended training jobs",
"AWS SageMaker",
"Vertex AI pipelines"
],
"metrics": [
"model_accuracy",
"lift_over_control",
"lifetime_value_uplift"
],
"knowledge_assets": [
"historical_roi_curves",
"channel_response_distributions",
"experiment_metadata"
],
"play": "Deploy reinforcement learning loops that re-weight channel investment daily based on revenue lift."
},
{
"name": "Reasoning and Decision Engine",
"pillar": "reasoning",
"description": "Explains marketing decisions with transparent logic, connecting evidence to next actions and quantifying uncertainty.",
"signal_keywords": [
"reason",
"logic",
"explain",
"justify",
"analysis",
"decision",
"planning"
],
"prompt_strategies": [
"Ask for chain-of-thought rationales that cite benchmarks, enrichment signals, and statistical priors.",
"Request explicit trade-off matrices covering risk, reward, and resource intensity."
],
"frameworks": [
"Knowledge-Augmented Generation",
"Bayesian updating",
"Decision trees"
],
"tooling": [
"Haystack reasoning pipelines",
"Pandas profiling",
"Probabilistic programming notebooks"
],
"metrics": [
"confidence_score",
"expected_value_delta",
"risk_adjusted_roi"
],
"knowledge_assets": [
"statistical_reasoning_store",
"benchmark_catalog",
"risk_playbooks"
],
"play": "Combine statistical priors with enrichment evidence to produce reasoned executive decisions."
},
{
"name": "Problem-Solving Accelerator",
"pillar": "problem_solving",
"description": "Transforms ambiguous growth hurdles into solvable workstreams with hypotheses, experiments, and measurable checkpoints.",
"signal_keywords": [
"problem",
"challenge",
"solve",
"bottleneck",
"roadblock",
"fix",
"root cause"
],
"prompt_strategies": [
"Frame objectives as constraints and ask for experiment matrices that resolve each constraint.",
"Request prioritized issue trees that map blockers to data, automation, or partnership plays."
],
"frameworks": [
"Agent-based experimentation",
"Experiment Growth Grid",
"Automation sprinting"
],
"tooling": [
"EGG planner",
"Automation sprint scheduler",
"Linear issue trackers"
],
"metrics": [
"time_to_resolution",
"velocity_gain",
"conversion_uplift"
],
"knowledge_assets": [
"performance_memory",
"automation_sprint_templates",
"competitive_countermoves"
],
"play": "Activate coordinated EGG experiments with automation sprints to remove campaign bottlenecks."
},
{
"name": "Perception and Signal Fusion",
"pillar": "perception",
"description": "Aggregates signals from marketplaces, ads platforms, influencers, and finance data to perceive market reality in real time.",
"signal_keywords": [
"perceive",
"sense",
"monitor",
"listen",
"insight",
"market data",
"telemetry"
],
"prompt_strategies": [
"Ask for blended insights that correlate ads, affiliate, marketplace, and macro data.",
"Request anomaly detection summaries to highlight emerging threats or accelerators."
],
"frameworks": [
"Cache-Augmented Generation",
"Streaming analytics",
"Haystack multi-index"
],
"tooling": [
"CoinGecko telemetry",
"AWS and Amazon catalog feeds",
"Vector database monitors"
],
"metrics": [
"signal_latency_minutes",
"coverage_ratio",
"anomaly_detection_recall"
],
"knowledge_assets": [
"crypto_market_snapshots",
"marketplace_catalogs",
"google_ads_performance"
],
"play": "Fuse Google Ads, affiliate, marketplace, and stock telemetry into a real-time opportunity radar."
},
{
"name": "Language Intelligence Mesh",
"pillar": "language",
"description": "Uses advanced prompt engineering and linguistic analytics to translate briefs into persuasive multi-channel narratives.",
"signal_keywords": [
"language",
"copy",
"messaging",
"content",
"prompt",
"narrative",
"story"
],
"prompt_strategies": [
"Request tone, voice, and vocabulary matrices aligned to each audience segment.",
"Ask for multilingual or localization-ready prompt packs mapped to funnel stages."
],
"frameworks": [
"Prompt engineering",
"Knowledge-Augmented Generation",
"Prompt management orchestration"
],
"tooling": [
"Amazon Bedrock prompt orchestration",
"LlamaIndex prompt router",
"Copy experimentation dashboards"
],
"metrics": [
"message_resonance_score",
"conversion_copy_lift",
"multilingual_coverage"
],
"knowledge_assets": [
"diagnostic_briefs",
"influencer_scripts",
"executive_playbooks"
],
"play": "Deploy orchestrated prompt packs that adapt messaging across every channel surface."
},
{
"name": "Prompt Engineering Command Center",
"pillar": "prompt_engineering",
"description": "Curates reusable prompt assets, templates, and governance policies that embed domain expertise inside every generation.",
"signal_keywords": [
"prompt",
"instruction",
"template",
"guideline",
"playbook",
"prompt ops"
],
"prompt_strategies": [
"Ask for reusable prompt macros tied to specific processes (RAG, EGG, monetization).",
"Request guardrails and evaluation checklists for high-stakes generations."
],
"frameworks": [
"Prompt engineering",
"Prompt evaluation",
"Prompt ops"
],
"tooling": [
"Prompt registries",
"Amazon Bedrock prompt management",
"Internal prompt review workflows"
],
"metrics": [
"prompt_reuse_rate",
"quality_score",
"review_turnaround_time"
],
"knowledge_assets": [
"prompt_library",
"style_guides",
"prompt_test_results"
],
"play": "Operationalize prompt lifecycle management with governance and rapid iteration loops."
},
{
"name": "Cache-Augmented Generation Fabric",
"pillar": "cache_augmented_generation",
"description": "Pre-computes and caches domain answers, drastically reducing latency for repetitive marketing workflows.",
"signal_keywords": [
"cache",