A continuation of Janady13/AI_Brain / services/seeder_api/intelligence.py. The complete source identity, every recorded occurrence, and linked equations are on its first page.
Source transcript · continued
Download complete source text ↓9,479 lines · 364.7 KB
Credential-bearing and unrelated personal details were removed from this reading copy. The source identity and original checksum remain recorded.
if overlays:
templates.extend(overlays)
if not templates:
templates = [
{
"activity": "Audit acquisition funnel touchpoints",
"outcome": "Document automation opportunities",
"tooling": ["Notion"],
"kpis": ["pipeline velocity"],
"automation_level": "assist",
}
]
tasks: List[AutomationTask] = []
summary_drivers: List[str] = []
for idx in range(blocks):
template = templates[idx % len(templates)]
context = self._campaign_context(campaigns, idx)
context["stage"] = diagnostics.stage
context["focus"] = diagnostics.focus
activity = self._safe_format(template.get("activity", "Execute automation sprint"), context)
outcome = self._safe_format(template.get("outcome", "Drive measurable impact"), context)
tooling = list(template.get("tooling", []))
kpis = [kpi.lower() for kpi in template.get("kpis", [])]
automation_level = template.get("automation_level", "assist")
start_minute = idx * block
end_minute = start_minute + block
source = self._defaults.get("source", "automation_playbooks")
tasks.append(
AutomationTask(
start_minute=start_minute,
end_minute=end_minute,
activity=activity,
outcome=outcome,
tooling=tooling,
kpis=kpis,
automation_level=automation_level,
source=source,
)
)
channel = context.get("channel")
if channel and channel not in summary_drivers:
summary_drivers.append(channel)
channels_summary = ", ".join(summary_drivers[:3]) or "omnichannel"
summary = (
f"Three-hour automation sprint targeting {diagnostics.stage} intent for {diagnostics.focus} focus "
f"anchored in {channels_summary} plays."
)
notes = self._defaults.get("notes")
return AutomationSprint(
total_minutes=blocks * block,
stage=diagnostics.stage,
focus=diagnostics.focus,
summary=summary,
tasks=tasks,
notes=notes,
source=self._defaults.get("source", "automation_playbooks"),
)
class MarketingIntelligenceEngine:
"""Ranks marketing campaigns against free-form prompts."""
def __init__(
self,
campaigns: Sequence[MarketingCampaign],
benchmarks: MarketingBenchmarks | None = None,
google_process: GoogleAdsProcess | None = None,
cj_process: CJAffiliateProcess | None = None,
crypto_process: CryptoMarketProcess | None = None,
stock_process: StockMarketProcess | None = None,
stripe_process: StripeProcess | None = None,
paypal_process: PayPalProcess | None = None,
payment_process: PaymentIntegrationProcess | None = None,
investing_process: InvestingProcess | None = None,
amazon_process: AmazonMarketplaceProcess | None = None,
ebay_process: EbayMarketplaceProcess | None = None,
aws_process: AWSRevenueProcess | None = None,
budget_process: BudgetAllocatorProcess | None = None,
automation_process: AutomationSprintPlanner | None = None,
learning_process: LearningLoopProcess | None = None,
meta_learning_process: MetaLearningProcess | None = None,
competitive_process: CompetitiveIntelligenceProcess | None = None,
influencer_process: InfluencerCollaborationProcess | None = None,
scenario_process: "ScenarioPlanningProcess" | None = None,
statistical_process: StatisticalReasoningProcess | None = None,
cognitive_process: CognitiveArchitectureProcess | None = None,
agentic_process: AgenticSystemsProcess | None = None,
knowledge_process: KnowledgeWorkflowProcess | None = None,
marketing_knowledge_process: MarketingKnowledgeProcess | None = None,
code_process: CodeAutomationProcess | None = None,
language_process: LanguageIntelligenceProcess | None = None,
moe_process: MixtureOfExpertsProcess | None = None,
agentic_loop_process: AgenticLoopProcess | None = None,
quantum_process: QuantumSimulationProcess | None = None,
physics_process: PhysicsEconomicsProcess | None = None,
):
if not campaigns:
raise ValueError("MarketingIntelligenceEngine requires at least one campaign")
self._campaigns = list(campaigns)
self._benchmarks = benchmarks or MarketingBenchmarks.from_file()
self._google_process = google_process or GoogleAdsProcess()
self._cj_process = cj_process or CJAffiliateProcess()
self._crypto_process = crypto_process or CryptoMarketProcess()
self._stock_process = stock_process or StockMarketProcess()
self._stripe_process = stripe_process or StripeProcess()
self._paypal_process = paypal_process or PayPalProcess()
self._payment_process = payment_process or PaymentIntegrationProcess()
self._investing_process = investing_process or InvestingProcess()
self._amazon_process = amazon_process or AmazonMarketplaceProcess()
self._ebay_process = ebay_process or EbayMarketplaceProcess()
self._aws_process = aws_process or AWSRevenueProcess()
self._budget_process = budget_process or BudgetAllocatorProcess()
self._automation_process = automation_process or AutomationSprintPlanner()
self._learning_process = learning_process or LearningLoopProcess()
self._meta_learning_process = meta_learning_process or MetaLearningProcess()
self._competitive_process = competitive_process or CompetitiveIntelligenceProcess()
self._influencer_process = influencer_process or InfluencerCollaborationProcess()
self._scenario_process = scenario_process or ScenarioPlanningProcess()
self._statistical_process = statistical_process or StatisticalReasoningProcess()
self._cognitive_process = cognitive_process or CognitiveArchitectureProcess()
self._agentic_process = agentic_process or AgenticSystemsProcess()
self._knowledge_process = knowledge_process or KnowledgeWorkflowProcess()
self._marketing_knowledge_process = (
marketing_knowledge_process or MarketingKnowledgeProcess()
)
self._code_process = code_process or CodeAutomationProcess()
self._language_process = language_process or LanguageIntelligenceProcess()
self._moe_process = moe_process or MixtureOfExpertsProcess()
self._agentic_loop_process = agentic_loop_process or AgenticLoopProcess()
self._quantum_process = quantum_process or QuantumSimulationProcess()
self._physics_process = physics_process or PhysicsEconomicsProcess()
@classmethod
def from_file(
cls,
path: str = MARKETING_ENUMERATION,
benchmark_path: str = MARKETING_BENCHMARKS,
) -> "MarketingIntelligenceEngine":
lines = _load_lines(path)
campaigns = [MarketingCampaign.parse(line) for line in lines]
benchmarks = MarketingBenchmarks.from_file(benchmark_path)
return cls(campaigns, benchmarks)
@staticmethod
def _keyword_set(prompt: str) -> List[str]:
tokens = re.findall(r"[a-zA-Z0-9]+", prompt.lower())
filtered = [token for token in tokens if len(token) > 2]
return filtered or ["campaign"]
def recommend(self, prompt: str, limit: int = 3) -> List[MarketingCampaign]:
tokens = self._keyword_set(prompt)
def score(campaign: MarketingCampaign) -> tuple[int, int, str]:
haystacks = {
"channel": campaign.channel.lower(),
"audience": campaign.audience.lower(),
"offer": campaign.offer.lower(),
"objective": campaign.objective.lower(),
}
weighted = 0
matched_features: set[str] = set()
for token in tokens:
for feature, text in haystacks.items():
if token in text:
matched_features.add(feature)
weight = 4 if feature == "audience" else 3 if feature == "offer" else 2
weighted += weight
# fallback to slight preference for broader coverage even if nothing matched
coverage_bonus = len(matched_features)
return (-(weighted + coverage_bonus), len(campaign.raw), campaign.raw)
ranked = sorted(self._campaigns, key=score)
return ranked[: max(1, limit)]
def recommend_statistical(self, prompt: str, limit: int = 3) -> List[CampaignScore]:
tokens = self._keyword_set(prompt)
scored: List[CampaignScore] = []
for campaign in self._campaigns:
metrics = self._benchmarks.metrics_for(campaign)
keyword_hits: Dict[str, int] = {"channel": 0, "audience": 0, "offer": 0, "objective": 0}
haystacks = {
"channel": campaign.channel.lower(),
"audience": campaign.audience.lower(),
"offer": campaign.offer.lower(),
"objective": campaign.objective.lower(),
}
match_bonus = 0.0
for token in tokens:
for feature, text in haystacks.items():
if token in text:
keyword_hits[feature] += 1
match_bonus += 0.35
roi_component = metrics.expected_roi * 0.6
ctr_component = metrics.expected_ctr * 80.0 * 0.25
confidence_component = metrics.confidence * 10.0 * 0.1
keyword_component = match_bonus
composite = roi_component + ctr_component + confidence_component + keyword_component
scored.append(
CampaignScore(
campaign=campaign,
score=composite,
metrics=metrics,
keyword_coverage=keyword_hits,
)
)
ranked = sorted(scored, key=lambda entry: entry.score, reverse=True)
return ranked[: max(1, limit)]
def summarize(self, prompt: str, campaigns: Sequence[MarketingCampaign]) -> str:
tokens = self._keyword_set(prompt)
highlights = ", ".join(sorted(set(tokens)))
count = len(campaigns)
return f"Surface {count} campaign plays tuned for {highlights}."
@staticmethod
def _infer_stage(tokens: Sequence[str]) -> Tuple[str, List[str]]:
mapping = {
"awareness": {"awareness", "reach", "impressions", "brand", "launch", "attention"},
"consideration": {"consideration", "education", "webinar", "nurture", "pipeline", "engagement"},
"conversion": {
"conversion",
"roi",
"ctr",
"leads",
"demos",
"trial",
"customers",
"acquisition",
},
"retention": {"retention", "renewal", "expansion", "upsell", "loyalty", "churn"},
}
hits: Dict[str, List[str]] = {key: [] for key in mapping}
for token in tokens:
for stage, keywords in mapping.items():
if token in keywords:
hits[stage].append(token)
stage = max(mapping, key=lambda key: len(hits[key]))
if all(len(values) == 0 for values in hits.values()):
stage = "conversion"
return stage, hits[stage]
@staticmethod
def _infer_urgency(prompt: str) -> str:
lowered = prompt.lower()
if any(keyword in lowered for keyword in ["urgent", "asap", "immediately", "right away", "now"]):
return "urgent"
if any(keyword in lowered for keyword in ["this quarter", "q4", "deadline", "accelerate"]):
return "time-sensitive"
return "standard"
@staticmethod
def _infer_focus(tokens: Sequence[str]) -> Tuple[str, List[str]]:
focus_buckets = {
"enterprise b2b": {"enterprise", "cxo", "c-suite", "fortune", "b2b", "saas"},
"startup founders": {"founder", "startup", "scaleup", "seed", "series"},
"consumer": {"consumer", "b2c", "retail", "shopper", "app"},
"developer": {"developer", "devops", "engineer", "technical", "api"},
}
hits: Dict[str, List[str]] = {bucket: [] for bucket in focus_buckets}
for token in tokens:
for focus, keywords in focus_buckets.items():
if token in keywords:
hits[focus].append(token)
focus = max(focus_buckets, key=lambda key: len(hits[key]))
if len(hits[focus]) == 0:
return "broad b2b", []
return focus, hits[focus]
@staticmethod
def _recommend_kpis(stage: str) -> List[str]:
mapping = {
"awareness": ["impressions", "share of voice", "reach growth"],
"consideration": ["content engagement", "marketing qualified leads", "pipeline velocity"],
"conversion": ["sales qualified leads", "demo-to-close rate", "customer acquisition cost"],
"retention": ["net revenue retention", "renewal rate", "expansion ARR"],
}
return mapping.get(stage, ["pipeline velocity", "conversion rate"])
@staticmethod
def _recommend_opportunities(stage: str, focus: str) -> List[str]:
opportunities = {
"awareness": [
"Anchor thought-leadership content to prime the market",
"Deploy programmatic amplification across hero stories",
],
"consideration": [
"Introduce nurture sequences aligned to buyer pains",
"Layer intent scoring to prioritize engaged accounts",
],
"conversion": [
"Bundle conversion offers with social proof assets",
"Tighten sales enablement loops with marketing triggers",
],
"retention": [
"Launch customer marketing to reinforce product value",
"Pair success cadences with expansion playbooks",
],
}
base = opportunities.get(stage, [])
if focus.startswith("enterprise"):
base = base + ["Align executive-level narratives with ROI benchmarks"]
elif "startup" in focus:
base = base + ["Stand up scrappy experiments to validate fast wins"]
elif "consumer" in focus:
base = base + ["Personalize lifecycle messaging using behavioral cohorts"]
return base
@staticmethod
def _detect_risks(prompt: str) -> List[str]:
lowered = prompt.lower()
risks: List[str] = []
if "flat" in lowered or "stalled" in lowered or "plateau" in lowered:
risks.append("Performance plateau signaled")
if "limited budget" in lowered or "tight budget" in lowered:
risks.append("Budget constraints highlighted")
if "new market" in lowered or "unknown" in lowered:
risks.append("Market unfamiliarity risk")
if "churn" in lowered or "retention" in lowered:
risks.append("Retention fragility noted")
return risks
def diagnose(self, prompt: str) -> PromptDiagnostics:
tokens = self._keyword_set(prompt)
stage, stage_hits = self._infer_stage(tokens)
urgency = self._infer_urgency(prompt)
focus, focus_hits = self._infer_focus(tokens)
kpis = self._recommend_kpis(stage)
opportunities = self._recommend_opportunities(stage, focus)
risks = self._detect_risks(prompt)
keyword_summary = {
"intent": stage_hits,
"focus": focus_hits,
"tokens": tokens,
}
confidence_seed = len(stage_hits) + len(focus_hits)
confidence = max(0.35, min(0.95, 0.35 + 0.1 * confidence_seed))
return PromptDiagnostics(
stage=stage,
urgency=urgency,
focus=focus,
recommended_kpis=kpis,
opportunities=opportunities,
risk_signals=risks,
keyword_summary=keyword_summary,
confidence=round(confidence, 2),
)
def portfolio(self, prompt: str, campaigns: Sequence[CampaignScore]) -> CampaignMetrics:
if not campaigns:
raise ValueError("portfolio requires at least one campaign score")
roi = sum(entry.metrics.expected_roi for entry in campaigns) / len(campaigns)
ctr = sum(entry.metrics.expected_ctr for entry in campaigns) / len(campaigns)
confidence = sum(entry.metrics.confidence for entry in campaigns) / len(campaigns)
drivers: Dict[str, float] = {}
for entry in campaigns:
for feature, value in entry.metrics.drivers.items():
drivers[feature] = max(drivers.get(feature, 0.0), value)
return CampaignMetrics(
expected_roi=roi,
expected_ctr=ctr,
confidence=confidence,
drivers=drivers,
)
def keywords(self, prompt: str) -> List[str]:
return self._keyword_set(prompt)
def statistical_reasoning_summary(
self,
prompt: str,
campaigns: Sequence[CampaignScore],
*,
budget: BudgetPlan | None,
stock_markets: Sequence[StockMarketForecast] | None,
crypto_markets: Sequence[CryptoMarketSnapshot] | None,
limit: int,
) -> StatisticalReasoningSummary:
return self._statistical_process.analyze(
prompt,
campaigns,
budget=budget,
stock_markets=stock_markets,
crypto_markets=crypto_markets,
limit=limit,
)
def expansion_grid(self, prompt: str, campaigns: Sequence[CampaignScore]) -> List[GrowthExperiment]:
"""Design a set of Experiment Growth Grid (EGG) plays from campaign rankings."""
if not campaigns:
raise ValueError("expansion_grid requires at least one campaign score")
diagnostics = self.diagnose(prompt)
stage_recipes: Dict[str, List[Dict[str, str]]] = {
"awareness": [
{
"name": "Narrative Amplification Sprint",
"hypothesis": (
"If we spotlight {offer} through {channel}, "
"we will warm {audience} toward {objective}."
),
"play": "Launch hero content and amplify across paid/owned reach streams.",
"metric": "impressions",
},
{
"name": "Thought Leadership Cascade",
"hypothesis": (
"Positioning {audience} challenges alongside {offer} "
"via {channel} will expand top-of-funnel attention."
),
"play": "Sequence authoritative narratives across social and partner syndication.",
"metric": "share of voice",
},
],
"consideration": [
{
"name": "Intent Magnet Program",
"hypothesis": (
"If we orchestrate {offer} journeys through {channel}, "
"we can mature {audience} into evaluators for {objective}."
),
"play": "Build nurture modules triggered by behavioral intent signals.",
"metric": "marketing qualified leads",
},
{
"name": "Education Sprint",
"hypothesis": (
"Providing {audience} with {offer} across {channel} "
"will compress evaluation cycles."
),
"play": "Layer webinars, playbooks, and retargeting to reinforce learnings.",
"metric": "content engagement",
},
],
"conversion": [
{
"name": "Pipeline Acceleration Lab",
"hypothesis": (
"If {audience} experience {offer} via {channel}, "
"we can unlock {objective} faster."
),
"play": "Run multi-touch conversion blitz with calibrated social proof.",
"metric": "sales qualified leads",
},
{
"name": "Revenue Experiment Track",
"hypothesis": (
"Blending {offer} within {channel} for {audience} will uplift deal velocity."
),
"play": "Pair high-intent triggers with offer experimentation and CRO support.",
"metric": "conversion rate",
},
],
"retention": [
{
"name": "Value Expansion Studio",
"hypothesis": (
"If {audience} engage with {offer} programs via {channel}, "
"we will fortify {objective}."
),
"play": "Launch customer storytelling and advocacy loops to reinforce value.",
"metric": "renewal rate",
},
{
"name": "Lifecycle Momentum Cell",
"hypothesis": (
"Activating {offer} through {channel} for {audience} "
"will catalyze expansion outcomes."
),
"play": "Coordinate success-led campaigns with product usage nudges.",
"metric": "net revenue retention",
},
],
}
focus_overlays: Dict[str, Dict[str, str]] = {
"enterprise b2b": {
"play": "Map executive-specific proof points and ABM orchestration layers.",
"metric": "executive meeting volume",
},
"startup founders": {
"play": "Prototype scrappy experiments with rapid iteration cadences.",
"metric": "activation-to-demo ratio",
},
"consumer": {
"play": "Employ personalization matrices and lifecycle messaging bursts.",
"metric": "repeat purchase rate",
},
"developer": {
"play": "Enable technical champions with sandboxes, tutorials, and community proof.",
"metric": "trial-to-adoption rate",
},
"default": {
"play": "Instrument experiment tracking with weekly conversion retros.",
"metric": "conversion rate",
},
}
base_recipes = stage_recipes.get(diagnostics.stage, stage_recipes["conversion"])
overlay = focus_overlays.get(diagnostics.focus, focus_overlays["default"])
recommended = diagnostics.recommended_kpis or ["conversion rate"]
experiments: List[GrowthExperiment] = []
for index, scored in enumerate(campaigns[: max(1, len(campaigns))]):
recipe = base_recipes[index % len(base_recipes)]
name = f"{recipe['name']} via {scored.campaign.channel.title()}"
hypothesis = recipe["hypothesis"].format(
offer=scored.campaign.offer,
channel=scored.campaign.channel,
audience=scored.campaign.audience,
objective=scored.campaign.objective,
)
playbook = [
f"Activate {scored.campaign.channel} targeting {scored.campaign.audience}.",
recipe["play"],
overlay["play"],
f"Align offer '{scored.campaign.offer}' to accelerate {scored.campaign.objective} outcomes.",
]
primary_metric = recipe.get("metric", recommended[0])
secondary_candidates = set(recommended[1:])
secondary_candidates.add(overlay["metric"])
secondary_metrics = sorted(secondary_candidates)
confidence = (diagnostics.confidence + scored.metrics.confidence) / 2
experiments.append(
GrowthExperiment(
name=name,
hypothesis=hypothesis,
primary_metric=primary_metric,
secondary_metrics=secondary_metrics,
playbook=playbook,
confidence=round(max(0.2, min(0.99, confidence)), 2),
)
)
return experiments
def google_ads_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[GoogleCampaignPerformance]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._google_process.performance(prompt, scored, limit)
def cj_affiliate_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[CJAffiliateRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._cj_process.offers(prompt, scored, limit)
def crypto_market_enrichment(
self,
prompt: str,
limit: int = 3,
) -> List[CryptoMarketSnapshot]:
return self._crypto_process.market_data(prompt, limit)
def stock_market_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[StockMarketForecast]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._stock_process.forecasts(prompt, scored, limit)
def stripe_checkout_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[StripeMonetizationPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._stripe_process.plans(prompt, scored, limit)
def paypal_checkout_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[PayPalMonetizationPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._paypal_process.plans(prompt, scored, limit)
def payment_integration_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 4,
) -> List[PaymentIntegrationPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._payment_process.plans(prompt, scored, limit)
def investing_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[InvestingPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._investing_process.plans(prompt, scored, limit)
def physics_economics_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[PhysicsEconomicsPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._physics_process.plans(prompt, scored, limit)
def influencer_collaboration_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[InfluencerRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._influencer_process.recommendations(prompt, scored, limit)
def amazon_marketplace_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[AmazonProductRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._amazon_process.listings(prompt, scored, limit)
def ebay_marketplace_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[EbayRevenuePlay]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._ebay_process.listings(prompt, scored, limit)
def aws_cloud_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[AWSRevenueRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._aws_process.plays(prompt, scored, limit)
def budget_optimizer(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> BudgetPlan:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return BudgetPlan(
total_budget=0.0,
allocations=[],
expected_revenue=0.0,
expected_roi=0.0,
payback_period_weeks=0.0,
summary="No campaigns available for budget allocation.",
)
return self._budget_process.plan(prompt, scored, limit)
def automation_sprint(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> AutomationSprint:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
diagnostics = self.diagnose(prompt)
return self._automation_process.plan(prompt, diagnostics, scored, limit)
def competitive_landscape(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[CompetitiveSignal]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
diagnostics = self.diagnose(prompt)
return self._competitive_process.signals(prompt, diagnostics, scored, limit)
def learning_loop(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[LearningLoopRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
diagnostics = self.diagnose(prompt)
return self._learning_process.recommend(prompt, diagnostics, scored, limit)
def cognitive_architecture_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[CognitiveCapabilityPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._cognitive_process.plans(prompt, scored, limit)
def agentic_system_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[AgenticSystemPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._agentic_process.plans(prompt, scored, limit)
def knowledge_workflow_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[KnowledgeWorkflowPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._knowledge_process.plans(prompt, scored, limit)
def marketing_knowledge_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[MarketingKnowledgePlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._marketing_knowledge_process.plans(prompt, scored, limit)
def language_capability_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[LanguageCapabilityPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._language_process.plans(prompt, scored, limit)
def code_automation_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[CodeAutomationPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._code_process.plans(prompt, scored, limit)
def meta_learning_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[MetaLearningPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._meta_learning_process.plans(prompt, scored, limit)
def rag_pipeline_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[KnowledgeWorkflowPlan]:
plans = self.knowledge_workflow_enrichment(prompt, campaigns, limit)
return [plan for plan in plans if plan.category == "rag"]
def knowledge_graph_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[KnowledgeWorkflowPlan]:
plans = self.knowledge_workflow_enrichment(prompt, campaigns, limit)
return [plan for plan in plans if plan.category == "kag"]
def mixture_of_experts_activation(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[MixtureOfExpertsPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._moe_process.activate(prompt, scored, limit)
def agentic_loop_blueprints(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 1,
) -> List[AgenticLoopPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, max(1, limit))
if not scored:
return []
return self._agentic_loop_process.plans(prompt, scored, limit)
def quantum_simulation_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 1,
) -> List[QuantumStressTestPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, max(1, limit))
if not scored:
return []
return self._quantum_process.plans(prompt, scored, limit)
def scenario_planning(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
*,
limit: int = 3,
budget: BudgetPlan | None = None,
google_ads: Sequence[GoogleCampaignPerformance] | None = None,
cj_affiliate: Sequence[CJAffiliateRecommendation] | None = None,
amazon: Sequence[AmazonProductRecommendation] | None = None,
ebay: Sequence[EbayRevenuePlay] | None = None,
aws: Sequence[AWSRevenueRecommendation] | None = None,
stripe: Sequence[StripeMonetizationPlan] | None = None,
paypal: Sequence[PayPalMonetizationPlan] | None = None,
payments: Sequence[PaymentIntegrationPlan] | None = None,
investing: Sequence[InvestingPlan] | None = None,
crypto: Sequence[CryptoMarketSnapshot] | None = None,
influencers: Sequence[InfluencerRecommendation] | None = None,
stocks: Sequence[StockMarketForecast] | None = None,
statistics: StatisticalReasoningSummary | None = None,
cognitive: Sequence[CognitiveCapabilityPlan] | None = None,
agentic: Sequence[AgenticSystemPlan] | None = None,
knowledge: Sequence[KnowledgeWorkflowPlan] | None = None,
marketing_knowledge: Sequence[MarketingKnowledgePlan] | None = None,
code_automation: Sequence[CodeAutomationPlan] | None = None,
language: Sequence[LanguageCapabilityPlan] | None = None,
meta_learning: Sequence[MetaLearningPlan] | None = None,
moe: Sequence[MixtureOfExpertsPlan] | None = None,
agentic_loops: Sequence[AgenticLoopPlan] | None = None,
quantum: Sequence[QuantumStressTestPlan] | None = None,
physics_economics: Sequence[PhysicsEconomicsPlan] | None = None,
) -> ScenarioAnalysis:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
diagnostics = self.diagnose(prompt)
return self._scenario_process.analyze(
prompt,
diagnostics,
scored,
budget=budget,
google_ads=google_ads or [],
cj_affiliate=cj_affiliate or [],
amazon=amazon or [],
ebay=ebay or [],
aws=aws or [],
stripe=stripe or [],
paypal=paypal or [],
payments=payments or [],
investing=investing or [],
crypto=crypto or [],
influencers=influencers or [],
stocks=stocks or [],
statistics=statistics,
cognitive=cognitive or [],
agentic=agentic or [],
knowledge=knowledge or [],
marketing_knowledge=marketing_knowledge or [],
code_automation=code_automation or [],
language=language or [],
meta_learning=meta_learning or [],
moe=moe or [],
agentic_loops=agentic_loops or [],
quantum=quantum or [],
physics_economics=physics_economics or [],
limit=limit,
)
def excellence_summary(
self,
prompt: str,
campaigns: Sequence[CampaignScore],
*,
limit: int,
rag: Sequence[MarketingCampaign],
egg: Sequence[GrowthExperiment],
google_ads: Sequence[GoogleCampaignPerformance],
cj_affiliate: Sequence[CJAffiliateRecommendation],
crypto_markets: Sequence[CryptoMarketSnapshot],
stripe: Sequence[StripeMonetizationPlan],
paypal: Sequence[PayPalMonetizationPlan],
payments: Sequence[PaymentIntegrationPlan],
investing: Sequence[InvestingPlan],
amazon: Sequence[AmazonProductRecommendation],
ebay: Sequence[EbayRevenuePlay],
aws: Sequence[AWSRevenueRecommendation],
budget: BudgetPlan | None,
automation: AutomationSprint | None,
learning_loop: Sequence[LearningLoopRecommendation],
competitive: Sequence[CompetitiveSignal],
influencers: Sequence[InfluencerRecommendation],
stocks: Sequence[StockMarketForecast],
statistics: StatisticalReasoningSummary | None = None,
cognitive: Sequence[CognitiveCapabilityPlan] | None = None,
agentic: Sequence[AgenticSystemPlan] | None = None,
knowledge: Sequence[KnowledgeWorkflowPlan] | None = None,
marketing_knowledge: Sequence[MarketingKnowledgePlan] | None = None,
code_automation: Sequence[CodeAutomationPlan] | None = None,
language: Sequence[LanguageCapabilityPlan] | None = None,
meta_learning: Sequence[MetaLearningPlan] | None = None,
moe: Sequence[MixtureOfExpertsPlan] | None = None,
agentic_loops: Sequence[AgenticLoopPlan] | None = None,
quantum: Sequence[QuantumStressTestPlan] | None = None,
physics_economics: Sequence[PhysicsEconomicsPlan] | None = None,
) -> ExcellenceSummary:
limit = max(1, limit)
roi_avg = mean(entry.metrics.expected_roi for entry in campaigns) if campaigns else 0.0
ctr_avg = mean(entry.metrics.expected_ctr for entry in campaigns) if campaigns else 0.0
confidence_avg = (
mean(entry.metrics.confidence for entry in campaigns) if campaigns else 0.0
)
roi_norm = min(1.0, max(0.0, roi_avg / 5.0))
ctr_norm = min(1.0, max(0.0, ctr_avg / 0.05))
confidence_norm = min(1.0, max(0.0, confidence_avg))
coverage_map: Dict[str, float] = {
"rag": min(1.0, len(rag) / limit),
"egg": min(1.0, len(egg) / limit),
"google_ads": min(1.0, len(google_ads) / limit),
"cj_affiliate": min(1.0, len(cj_affiliate) / limit),
"crypto_markets": min(1.0, len(crypto_markets) / limit),
"stock_markets": min(1.0, len(stocks) / limit),
"stripe": min(1.0, len(stripe) / limit),
"paypal": min(1.0, len(paypal) / limit),
"payment_integrations": min(1.0, len(payments) / limit),
"investing": min(1.0, len(investing) / limit),
"influencer_collaborations": min(1.0, len(influencers) / limit),
"amazon_marketplace": min(1.0, len(amazon) / limit),
"ebay_marketplace": min(1.0, len(ebay) / limit),
"aws_cloud": min(1.0, len(aws) / limit),
"learning_loop": min(1.0, len(learning_loop) / limit),
"competitive_landscape": min(1.0, len(competitive) / limit),
}
coverage_map["budget_optimizer"] = 1.0 if budget and budget.allocations else 0.0
coverage_map["automation"] = 1.0 if automation and automation.tasks else 0.0
coverage_map["statistical_reasoning"] = 1.0 if statistics else 0.0
coverage_map["cognitive_architecture"] = (
min(1.0, len(cognitive or []) / limit) if cognitive is not None else 0.0
)
coverage_map["agentic_systems"] = (
min(1.0, len(agentic or []) / limit) if agentic is not None else 0.0
)
coverage_map["code_automation"] = (
min(1.0, len(code_automation or []) / limit) if code_automation is not None else 0.0
)
coverage_map["marketing_knowledge"] = (
min(1.0, len(marketing_knowledge or []) / limit)
if marketing_knowledge is not None
else 0.0
)
coverage_map["meta_learning"] = (
min(1.0, len(meta_learning or []) / limit) if meta_learning is not None else 0.0
)
rag_workflows = [plan for plan in (knowledge or []) if plan.category == "rag"]
kag_workflows = [plan for plan in (knowledge or []) if plan.category == "kag"]
coverage_map["rag_workflows"] = min(1.0, len(rag_workflows) / limit) if knowledge is not None else 0.0
coverage_map["knowledge_graphs"] = min(1.0, len(kag_workflows) / limit) if knowledge is not None else 0.0
coverage_map["quantum_simulation"] = (
min(1.0, len(quantum or []) / limit) if quantum is not None else 0.0
)
coverage_map["language_intelligence"] = (
min(1.0, len(language or []) / limit) if language is not None else 0.0
)
coverage_map["mixture_of_experts"] = 1.0 if moe and any(plan.activations for plan in moe) else 0.0
coverage_map["agentic_loops"] = 1.0 if agentic_loops else 0.0
coverage_map["physics_economics"] = (
min(1.0, len(physics_economics or []) / limit)
if physics_economics is not None
else 0.0
)
coverage_norm = mean(coverage_map.values()) if coverage_map else 0.0
excellence_index = round(
(roi_norm * 0.35 + ctr_norm * 0.2 + confidence_norm * 0.25 + coverage_norm * 0.2)
* 100,
2,
)
if excellence_index >= 85:
maturity = "world-class"
elif excellence_index >= 70:
maturity = "elite"
elif excellence_index >= 55:
maturity = "scaling"
else:
maturity = "emerging"
keywords = self.keywords(prompt)[:3]
if keywords:
headline_focus = ", ".join(keywords)
else:
headline_focus = "core growth themes"
headline = (
f"{maturity.title()} marketing ANI readiness anchored in {headline_focus}."
)
strengths: List[str] = []
if roi_norm >= 0.75:
strengths.append("Benchmark ROI trajectory rivals top performers.")
elif roi_norm >= 0.5:
strengths.append("ROI projections support confident scaling.")
if coverage_norm >= 0.7:
strengths.append("Process coverage spans key enrichment workflows.")
if len(competitive) >= 1:
strengths.append("Competitive counter-moves actively inform campaign plans.")
if payments:
payment_plan = payments[0]
strengths.append(
f"{payment_plan.provider} integration operating at {payment_plan.maturity} maturity accelerates revenue capture."
)
if investing:
investing_plan = investing[0]
strengths.append(
f"Investing platform {investing_plan.provider} ({investing_plan.maturity}) opens financial product monetization."
)
if len(influencers) >= 1:
strengths.append("Influencer collaborations unlock rapid audience amplification.")
if stocks:
top_stock = stocks[0]
if top_stock.forecast_return_90d > 0.1:
strengths.append(
f"{top_stock.name} outlook adds capital market tailwind to marketing scale."
)
if statistics and statistics.success_probability >= 0.75:
strengths.append(
f"Statistical reasoning signals {statistics.success_probability:.0%} success probability."
)
if cognitive:
top_capability = cognitive[0]
if top_capability.maturity in {"advanced", "autonomous"}:
strengths.append(
f"Cognitive architecture running {top_capability.maturity} on {top_capability.capability}."
)
if language:
top_language = language[0]
if top_language.maturity in {"advanced", "autonomous"}:
strengths.append(
f"Language intelligence {top_language.capability} {top_language.maturity} for conversational monetization."
)
if code_automation:
code_plan = code_automation[0]
if code_plan.maturity in {"advanced", "autonomous"}:
strengths.append(
f"Code automation {code_plan.capability} operating at {code_plan.maturity} maturity stabilizes releases."
)
if meta_learning:
meta_plan = meta_learning[0]
if meta_plan.maturity in {"advanced", "autonomous", "elite"}:
strengths.append(
f"Meta-learning {meta_plan.capability} ({meta_plan.maturity}) keeps adaptive improvements compounding."
)
if agentic:
system = agentic[0]
strengths.append(
f"Agentic system {system.system_name} orchestrates {system.maturity} tool calling stack."
)
if knowledge:
strengths.append("Knowledge workflows keep retrieval and reasoning grounded for every prompt.")
if moe and moe[0].activations:
strengths.append("Mixture-of-experts router balances revenue experts for routing confidence.")
if agentic_loops:
strengths.append("Agentic loop blueprint sustains continuous perception→action execution.")
if quantum:
top_plan = quantum[0]
strengths.append(
f"Quantum simulator {top_plan.simulator} ({top_plan.maturity}) validates cross-provider stress scenarios."
)
if physics_economics:
physics_plan = physics_economics[0]
strengths.append(
f"Physics/economics fusion {physics_plan.capability} ({physics_plan.maturity}) fortifies complex revenue models."
)
watchouts: List[str] = []
if ctr_norm < 0.4:
watchouts.append(
"Click-through benchmarks trail aggressive targets; iterate creative experiments."
)
if confidence_norm < 0.6:
watchouts.append("Benchmark confidence is moderate; collect more lift validation.")
if coverage_norm < 0.5:
watchouts.append(
"Several enrichment processes returned limited data; review integration readiness."
)
if len(influencers) == 0:
watchouts.append("Source influencer partners to extend reach beyond owned channels.")
if stocks and mean(forecast.volatility for forecast in stocks) > 0.25:
watchouts.append("Equity volatility elevated; stage reinvestment with safeguards.")
if statistics and statistics.success_probability < 0.6:
watchouts.append(
"Statistical reasoning indicates moderate success probability; reinforce testing cadence."
)
if not cognitive:
watchouts.append(
"Activate cognitive architecture plans so reasoning and learning stay orchestrated."
)
if not language:
watchouts.append(
"Stand up language intelligence workflows so prompts, chats, and copy stay revenue-ready."
)
if not code_automation:
watchouts.append(
"Instrument AI code fixing and intelligent automation so shipping velocity stays revenue-safe."
)
if not meta_learning:
watchouts.append(
"Stand up meta-learning loops so experiments keep getting smarter between cycles."
)
if not payments:
watchouts.append("Stand up payment provider runbooks so monetization launches stay unblockable.")
if not investing:
watchouts.append("Add investing partners to unlock financial services revenue streams.")
if not agentic:
watchouts.append("Wire agentic systems to automate tool calling and database access.")
if not knowledge:
watchouts.append("Stand up RAG/KAG pipelines to ground responses in curated knowledge.")
if not moe:
watchouts.append("Calibrate mixture-of-experts routing to blend marketplace monetization signals.")
if not agentic_loops:
watchouts.append("Document an agentic loop so automation can self-correct between cycles.")
if not quantum:
watchouts.append("Activate quantum stress testing to pressure test markets and payments in tandem.")
if not physics_economics:
watchouts.append("Stand up physics/economics orchestration so complex market mechanics stay resilient.")
if not strengths:
strengths.append("Foundational datasets available for orchestration.")
if not watchouts:
watchouts.append("Continue monitoring live integrations to maintain excellence.")
notes = (
"Excellence index synthesizes ROI, CTR, confidence, and process coverage heuristics."
)
if stocks:
top_stock = stocks[0]
notes += (
f" | Stock outlook {top_stock.symbol} {top_stock.trend} with "
f"{top_stock.forecast_return_30d*100:.1f}% 30d move."
)
if statistics:
notes += f" | {statistics.narrative}"
if cognitive:
notes += (
f" | Cognitive architecture anchored by {cognitive[0].capability} ({cognitive[0].maturity})."
)
if agentic:
notes += (
f" | Agentic system {agentic[0].system_name} ({agentic[0].maturity}) governing tool orchestration."
)
if knowledge:
notes += (
f" | Knowledge workflows cover {len(rag_workflows)} RAG and {len(kag_workflows)} KAG pipelines."
)
if marketing_knowledge:
notes += (
f" | Marketing knowledge sync {marketing_knowledge[0].name} ({marketing_knowledge[0].maturity})."
)
if language:
notes += (
f" | Language workflows anchor {language[0].capability} ({language[0].maturity})."
)
if meta_learning:
notes += (
f" | Meta-learning loops led by {meta_learning[0].capability} ({meta_learning[0].maturity})."
)
if code_automation:
notes += (
f" | Code automation {code_automation[0].capability} ({code_automation[0].maturity}) protects deployments."
)
if payments:
notes += (
f" | Payment integrations led by {payments[0].provider} ({payments[0].maturity})."
)
if investing:
notes += (
f" | Investing runway anchored by {investing[0].provider} ({investing[0].maturity})."
)
if moe:
activation_count = len(moe[0].activations) if moe and moe[0].activations else 0
notes += f" | MoE router activated {activation_count} revenue experts."
if agentic_loops:
notes += f" | Agentic loop {agentic_loops[0].name} maintains continuous execution."
if quantum:
notes += (
f" | Quantum simulator {quantum[0].simulator} covers {len(quantum[0].stress_tests)} stress scenarios."
)
if physics_economics:
notes += (
f" | Physics/economics plan {physics_economics[0].capability} ({physics_economics[0].maturity}) anchors complex mechanics."
)
return ExcellenceSummary(
excellence_index=excellence_index,
maturity_level=maturity,
headline=headline,
strengths=strengths,
watchouts=watchouts,
process_coverage=coverage_map,
notes=notes,
)
def executive_playbook(
self,
prompt: str,
campaigns: Sequence[CampaignScore],
*,
limit: int,
rag: Sequence[MarketingCampaign],
egg: Sequence[GrowthExperiment],
google_ads: Sequence[GoogleCampaignPerformance],
cj_affiliate: Sequence[CJAffiliateRecommendation],
amazon: Sequence[AmazonProductRecommendation],
ebay: Sequence[EbayRevenuePlay],
paypal: Sequence[PayPalMonetizationPlan],
payments: Sequence[PaymentIntegrationPlan] | None = None,
investing: Sequence[InvestingPlan] | None = None,
budget: BudgetPlan | None,
automation: AutomationSprint | None,
learning_loop: Sequence[LearningLoopRecommendation],
competitive: Sequence[CompetitiveSignal],
influencers: Sequence[InfluencerRecommendation],
stocks: Sequence[StockMarketForecast],
excellence: ExcellenceSummary | None = None,
statistics: StatisticalReasoningSummary | None = None,
cognitive: Sequence[CognitiveCapabilityPlan] | None = None,
agentic: Sequence[AgenticSystemPlan] | None = None,
knowledge: Sequence[KnowledgeWorkflowPlan] | None = None,
marketing_knowledge: Sequence[MarketingKnowledgePlan] | None = None,
code_automation: Sequence[CodeAutomationPlan] | None = None,
language: Sequence[LanguageCapabilityPlan] | None = None,
meta_learning: Sequence[MetaLearningPlan] | None = None,
moe: Sequence[MixtureOfExpertsPlan] | None = None,
agentic_loops: Sequence[AgenticLoopPlan] | None = None,
quantum: Sequence[QuantumStressTestPlan] | None = None,
physics_economics: Sequence[PhysicsEconomicsPlan] | None = None,
) -> ExecutivePlaybook:
diagnostics = self.diagnose(prompt)
def _channel_key(text: str) -> str:
return re.sub(r"\s+", " ", text.lower().strip())
limit = max(1, limit)
top_scores = list(campaigns[: limit if campaigns else 1])
if not top_scores and campaigns:
top_scores = list(campaigns)
experiment_map: Dict[str, GrowthExperiment] = {}
for experiment in egg:
name_lower = experiment.name.lower()
channel_fragment: str | None = None
if " via " in name_lower:
channel_fragment = name_lower.split(" via ", 1)[1].strip()
if (not channel_fragment or not channel_fragment.strip()) and experiment.playbook:
first_step = experiment.playbook[0].lower()
match = re.search(r"activate (.+?) targeting", first_step)
if match:
channel_fragment = match.group(1).strip()
if channel_fragment:
experiment_map.setdefault(_channel_key(channel_fragment), experiment)
budget_map: Dict[str, BudgetAllocation] = {}
if budget:
for allocation in budget.allocations:
budget_map[_channel_key(allocation.channel)] = allocation
learning_map: Dict[str, List[LearningLoopRecommendation]] = {}
for recommendation in learning_loop:
channel_hint = recommendation.channel or recommendation.stage or recommendation.play_name
key = _channel_key(channel_hint)
learning_map.setdefault(key, []).append(recommendation)
competitive_map: Dict[str, List[CompetitiveSignal]] = {}
for signal in competitive:
competitive_map.setdefault(_channel_key(signal.channel), []).append(signal)
rag_channels = {idx: rag_campaign.channel for idx, rag_campaign in enumerate(rag)}
influencer_pool = list(influencers)
investing = list(investing or [])
meta_learning = list(meta_learning or [])
quantum = list(quantum or [])
physics_economics = list(physics_economics or [])
marketing_knowledge = list(marketing_knowledge or [])
urgency_to_horizon = {"urgent": 72, "time-sensitive": 120, "standard": 168}
base_horizon = urgency_to_horizon.get(diagnostics.urgency, 168)
campaign_confidence = (
mean(score.metrics.confidence for score in top_scores) if top_scores else 0.5
)
roi_confidence = (
mean(score.metrics.expected_roi for score in top_scores) if top_scores else 0.5
)
budget_confidence = (
min(0.95, max(0.35, (budget.expected_roi / 5.0))) if budget else 0.55
)
learning_confidence = (
mean(rec.confidence for rec in learning_loop) if learning_loop else 0.55
)
automation_confidence = 0.75 if automation and automation.tasks else 0.5
statistics_confidence = statistics.confidence if statistics else 0.6
agentic_confidence = 0.78 if agentic else 0.5
knowledge_confidence = 0.76 if knowledge else 0.5
code_confidence = 0.76 if code_automation else 0.5
language_confidence = 0.76 if language else 0.5
meta_learning_confidence = 0.78 if meta_learning else 0.5
moe_confidence = 0.78 if moe and moe[0].activations else 0.5
loop_confidence = 0.8 if agentic_loops else 0.55
investing_confidence = 0.78 if investing else 0.5
payment_confidence = 0.78 if payments else 0.5
quantum_confidence = 0.78 if quantum else 0.5
physics_confidence = 0.78 if physics_economics else 0.5
overall_confidence = round(
min(
0.99,
max(
0.35,
mean(
[
diagnostics.confidence,
campaign_confidence,
min(0.95, max(0.3, roi_confidence / 5.0)),
budget_confidence,
learning_confidence,
automation_confidence,
(mean(stock.confidence for stock in stocks) if stocks else 0.6),
statistics_confidence,
agentic_confidence,
knowledge_confidence,
code_confidence,
language_confidence,
meta_learning_confidence,
moe_confidence,
loop_confidence,
investing_confidence,
payment_confidence,
quantum_confidence,
physics_confidence,
]
),
),
),
2,
)
directives: List[StrategicDirective] = []
north_star_metric = (
diagnostics.recommended_kpis[0]
if diagnostics.recommended_kpis
else "revenue acceleration"
)
for index, score in enumerate(top_scores):
channel = score.campaign.channel
channel_key = _channel_key(channel)
allocation = budget_map.get(channel_key)
experiment = experiment_map.get(channel_key)
learnings = learning_map.get(channel_key, [])
competitors = competitive_map.get(channel_key, [])
google_signal = google_ads[index % len(google_ads)] if google_ads else None
affiliate_signal = cj_affiliate[index % len(cj_affiliate)] if cj_affiliate else None
amazon_signal = amazon[index % len(amazon)] if amazon else None
ebay_signal = ebay[index % len(ebay)] if ebay else None
paypal_signal = paypal[index % len(paypal)] if paypal else None
payment_plan = payments[index % len(payments)] if payments else None
influencer_signal = (
influencer_pool[index % len(influencer_pool)] if influencer_pool else None
)
investing_plan = investing[index % len(investing)] if investing else None
stock_signal = stocks[index % len(stocks)] if stocks else None
cognitive_plan = cognitive[index % len(cognitive)] if cognitive else None
agentic_plan = agentic[index % len(agentic)] if agentic else None
knowledge_plan = knowledge[index % len(knowledge)] if knowledge else None
marketing_plan = (
marketing_knowledge[index % len(marketing_knowledge)]
if marketing_knowledge
else None
)
language_plan = language[index % len(language)] if language else None
meta_plan = meta_learning[index % len(meta_learning)] if meta_learning else None
moe_plan = moe[0] if moe else None
loop_plan = agentic_loops[index % len(agentic_loops)] if agentic_loops else None
quantum_plan = quantum[index % len(quantum)] if quantum else None
physics_plan = (
physics_economics[index % len(physics_economics)] if physics_economics else None
)
supporting_channels = [channel]
if google_signal:
supporting_channels.append("Google Ads")
if affiliate_signal:
supporting_channels.append("Affiliate")
if amazon_signal:
supporting_channels.append("Marketplace")
if ebay_signal:
supporting_channels.append("eBay")
if paypal_signal:
supporting_channels.append("PayPal")
if payment_plan:
supporting_channels.append(f"Payments - {payment_plan.provider}")
if investing_plan:
supporting_channels.append(f"Investing - {investing_plan.provider}")
if influencer_signal:
supporting_channels.append(f"Influencer - {influencer_signal.platform}")
if stock_signal:
supporting_channels.append(f"Markets - {stock_signal.symbol}")
if automation and automation.tasks:
supporting_channels.append("Automation Sprint")
if statistics:
supporting_channels.append("Statistical Reasoning")
if cognitive:
supporting_channels.append(f"Cognitive - {cognitive[0].capability}")
if agentic:
supporting_channels.append(f"Agentic - {agentic[0].system_name}")
if knowledge_plan:
supporting_channels.append(f"Knowledge - {knowledge_plan.category.upper()}")
if marketing_plan:
supporting_channels.append(f"Knowledge Sync - {marketing_plan.name}")
if language_plan:
supporting_channels.append(f"Language - {language_plan.capability}")
if meta_plan:
supporting_channels.append(f"Meta-Learning - {meta_plan.capability}")
if moe_plan:
supporting_channels.append("Mixture-of-Experts")
if loop_plan:
supporting_channels.append("Agentic Loop")
if quantum_plan:
supporting_channels.append(f"Quantum - {quantum_plan.simulator}")
if physics_plan:
supporting_channels.append(
f"Physics/Economics - {physics_plan.capability}"
)
supporting_channels = list(dict.fromkeys(supporting_channels))
action_steps: List[str] = [
(