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.
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f"Activate {channel} program targeting {score.campaign.audience} to "
f"{score.campaign.objective}."
)
]
if experiment:
action_steps.append(
f"Run Experiment '{experiment.name}' optimizing {experiment.primary_metric}."
)
if google_signal:
action_steps.append(
f"Model spend with Google Ads ROAS {google_signal.roas:.1f}x benchmark."
)
if affiliate_signal:
commission_pct = affiliate_signal.commission_rate * 100
action_steps.append(
f"Layer CJ affiliate offer '{affiliate_signal.program_name}' at {commission_pct:.1f}% commission."
)
if amazon_signal:
action_steps.append(
f"Promote marketplace listing '{amazon_signal.title}' to unlock partner revenue."
)
if ebay_signal:
action_steps.append(
(
f"Launch eBay offer '{ebay_signal.title}' leveraging {ebay_signal.affiliate_rate * 100:.1f}%"
" partner share and rapid sell-through."
)
)
if paypal_signal:
action_steps.append(
(
f"Enable PayPal {paypal_signal.checkout_flow} flow with {paypal_signal.payout_schedule} "
"payout cadence for dependable paychecks."
)
)
if payment_plan:
focus = payment_plan.integration_focus[0] if payment_plan.integration_focus else "checkout"
action_steps.append(
f"Deploy {payment_plan.provider} {focus.lower()} runbook to operationalize payments and follow troubleshooting/codefix guides."
)
if investing_plan:
asset_summary = ", ".join(investing_plan.asset_classes[:2]) or "key assets"
action_steps.append(
f"Launch {investing_plan.provider} investing funnel targeting {asset_summary}."
)
if stock_signal:
action_steps.append(
(
f"Align budget pacing with {stock_signal.name} {stock_signal.trend} trend "
f"({stock_signal.forecast_return_30d*100:.1f}% 30d outlook)."
)
)
if influencer_signal:
action_steps.append(
(
f"Activate {influencer_signal.collab_format} with {influencer_signal.name} "
f"to reach {influencer_signal.estimated_reach:,.0f} engaged prospects."
)
)
if automation and automation.tasks:
first_task = automation.tasks[0]
kpi_focus = first_task.kpis[0] if first_task.kpis else "primary KPI"
action_steps.append(
f"Kick off automation sprint task '{first_task.activity}' for {kpi_focus}."
)
if statistics:
top_driver = max(
statistics.driver_confidence.items(),
key=lambda item: item[1],
)[0].split(":", 1)[1] if statistics.driver_confidence else "core drivers"
action_steps.append(
f"Instrument statistical driver '{top_driver}' to validate {statistics.expected_roi:.2f}x ROI forecast."
)
if cognitive_plan:
action_steps.append(
(
f"Operationalize {cognitive_plan.capability} via {cognitive_plan.frameworks[0]} "
"to keep ANI reasoning adaptive."
)
)
if agentic_plan:
primary_tool = agentic_plan.toolchain[0] if agentic_plan.toolchain else "agent orchestrator"
action_steps.append(
(
f"Deploy agentic system {agentic_plan.system_name} using {primary_tool} "
"to unify tool calling and database flows."
)
)
if knowledge_plan:
action_steps.append(
(
f"Refresh {knowledge_plan.category.upper()} workflow '{knowledge_plan.name}' "
"to ground recommendations with up-to-date context."
)
)
if marketing_plan:
action_steps.append(
(
f"Run {marketing_plan.update_frequency} sync for {marketing_plan.name} "
"so marketing intelligence stays current."
)
)
if language_plan:
action_steps.append(
(
f"Deploy language workflow '{language_plan.capability}' to keep prompts, chats, and copy aligned with revenue goals."
)
)
if moe_plan and moe_plan.activations:
top_activation = moe_plan.activations[0]
action_steps.append(
(
f"Route monetization decisions through {top_activation.name} ({top_activation.provider}) "
f"at {top_activation.confidence:.0%} confidence."
)
)
if loop_plan:
action_steps.append(
(
f"Run agentic loop '{loop_plan.name}' to cycle perception→action across revenue systems."
)
)
if quantum_plan:
top_scenario = quantum_plan.stress_tests[0] if quantum_plan.stress_tests else None
if top_scenario:
action_steps.append(
(
f"Execute quantum stress '{top_scenario.name}' to validate {top_scenario.domain} readiness before scale."
)
)
else:
action_steps.append(
f"Review quantum simulator {quantum_plan.simulator} telemetry prior to ramping spend."
)
if physics_plan:
action_steps.append(
(
f"Activate physics/economics orchestration '{physics_plan.capability}'"
" to align complex market mechanics with revenue plays."
)
)
if learnings:
next_action = (
learnings[0].next_actions[0]
if learnings[0].next_actions
else "capture fresh experiment data"
)
action_steps.append(
f"Apply learning '{learnings[0].learning}' with next action '{next_action}'."
)
action_steps = list(dict.fromkeys(action_steps))
dependencies: List[str] = []
if allocation:
dependencies.append(
f"Allocate ${allocation.allocation_usd:,.0f} to {channel} with expected ${allocation.expected_revenue:,.0f} returns"
)
else:
dependencies.append("Confirm working budget envelope for channel execution")
if google_signal and google_signal.source != "simulated":
dependencies.append("Validate Google Ads account credentials and conversion tracking")
if affiliate_signal and affiliate_signal.source != "cj_catalog":
dependencies.append("Sync CJ affiliate program approvals")
if automation and automation.tasks:
dependencies.append("Reserve automation operators for 3-hour sprint")
if ebay_signal and ebay_signal.source != "ebay_catalog":
dependencies.append("Confirm eBay developer token and partner campaign tracking")
if paypal_signal and paypal_signal.source != "simulated":
dependencies.append("Verify PayPal payout configuration and webhook capture")
elif paypal_signal:
dependencies.append("Export PAYPAL credentials to activate live paychecks")
if influencer_signal:
est_investment = influencer_signal.projected_cpm * influencer_signal.estimated_reach / 1000.0
dependencies.append(
(
f"Fund influencer collaboration (~${est_investment:,.0f}) to secure "
f"{influencer_signal.projected_roi:.1f}x projected ROI"
)
)
if stock_signal:
dependencies.append(
(
f"Track {stock_signal.symbol} volatility ({stock_signal.volatility:.2f}) "
"to schedule reinvestment windows."
)
)
if statistics and statistics.success_probability < 0.7:
dependencies.append(
"Schedule mid-cycle statistical refresh to bolster success probability"
)
if investing_plan:
dependencies.append(
f"Coordinate compliance review for {investing_plan.provider} investing campaigns"
)
if cognitive_plan:
dependencies.append(
(
f"Deploy {cognitive_plan.capability} ops with {cognitive_plan.tooling[0]} instrumentation"
)
)
if agentic_plan:
dependencies.append(
(
f"Enable {agentic_plan.system_name} access to {agentic_plan.data_interfaces[0] if agentic_plan.data_interfaces else 'core data systems'}"
)
)
if knowledge_plan:
dependencies.append(
f"Maintain {knowledge_plan.category.upper()} store '{knowledge_plan.vector_store}' freshness cadence"
)
if language_plan:
dependencies.append(
f"Provision language stack ({', '.join(language_plan.tooling[:2] or ['NLP tooling'])}) to operationalize {language_plan.capability}"
)
if moe_plan and not moe_plan.activations:
dependencies.append("Configure MoE router credentials to reach external experts")
if loop_plan:
dependencies.append("Instrument loop telemetry dashboards for autonomous retrospectives")
if quantum_plan:
avg_stability = (
mean(test.stability_index for test in quantum_plan.stress_tests)
if quantum_plan.stress_tests
else 0.5
)
dependencies.append(
(
f"Review quantum stress telemetry from {quantum_plan.simulator} "
f"(avg stability {avg_stability:.2f}) before campaign scale."
)
)
if physics_plan:
dependencies.append(
(
f"Coordinate physics/economics plan {physics_plan.capability} docs"
" to align hardware, compression, and market guards."
)
)
risk_mitigations: List[str] = []
for risk in diagnostics.risk_signals[:2]:
risk_mitigations.append(f"Mitigate risk: {risk}")
for competitor in competitors[:1]:
risk_mitigations.append(
f"Counter {competitor.competitor} via {competitor.recommended_play}"
)
if learnings and learnings[0].losses:
risk_mitigations.append(
f"Avoid prior loss: {learnings[0].losses[0]}"
)
if influencer_signal and influencer_signal.projected_roi < 3.5:
risk_mitigations.append(
"Negotiate performance clauses with influencer partner to protect ROI"
)
if stock_signal and stock_signal.volatility > 0.28:
risk_mitigations.append(
f"Hedge equity exposure while {stock_signal.symbol} volatility runs {stock_signal.volatility:.2f}"
)
if statistics and statistics.volatility_adjustment > 0.25:
risk_mitigations.append(
"Stage spend releases to account for statistical volatility adjustments"
)
if investing_plan and investing_plan.risk_controls:
risk_mitigations.append(
f"Apply investing guardrail: {investing_plan.risk_controls[0]}"
)
if cognitive_plan and cognitive_plan.maturity == "foundational":
risk_mitigations.append(
f"Invest in advancing {cognitive_plan.capability} maturity for sustained autonomy"
)
if agentic_plan and agentic_plan.maturity in {"foundational", "advanced"}:
risk_mitigations.append(
f"Upskill agentic system {agentic_plan.system_name} workflows for higher autonomy"
)
if code_automation and code_automation[0].maturity == "foundational":
risk_mitigations.append(
f"Harden code automation {code_automation[0].capability} before shipping critical releases"
)
if knowledge_plan and knowledge_plan.maturity == "foundational":
risk_mitigations.append(
f"Invest in elevating {knowledge_plan.category.upper()} workflow '{knowledge_plan.name}' maturity"
)
if marketing_plan and marketing_plan.maturity == "foundational":
risk_mitigations.append(
f"Enable live connectors for {marketing_plan.name} to accelerate knowledge automation"
)
if language_plan and language_plan.maturity == "foundational":
risk_mitigations.append(
f"Upskill language workflow '{language_plan.capability}' for autonomous conversational coverage"
)
if moe_plan and not moe_plan.activations:
risk_mitigations.append("Map fallbacks for MoE router until experts respond in real time")
if loop_plan and loop_plan.maturity != "autonomous":
risk_mitigations.append(
f"Calibrate agentic loop '{loop_plan.name}' checkpoints before scaling spend"
)
if quantum_plan and quantum_plan.stress_tests:
worst_stability = min(test.stability_index for test in quantum_plan.stress_tests)
if worst_stability < 0.5:
risk_mitigations.append(
"Instrument fallback flows for domains flagged by quantum stress instability"
)
else:
risk_mitigations.append(
"Monitor quantum telemetry for emerging instability across revenue systems"
)
if physics_plan and physics_plan.maturity in {"emerging", "advanced"}:
risk_mitigations.append(
f"Backtest physics/economics plan {physics_plan.capability} against live market shocks"
)
if not risk_mitigations:
risk_mitigations.append("Maintain rapid feedback loops across processes")
directive_confidence = round(
min(
0.99,
max(
0.3,
mean(
[
score.metrics.confidence,
min(0.95, max(0.3, score.metrics.expected_roi / 5.0)),
learnings[0].confidence if learnings else diagnostics.confidence,
]
),
),
),
2,
)
time_horizon = base_horizon + index * 48
supporting_metrics_set: set[str] = {north_star_metric}
if experiment:
supporting_metrics_set.add(experiment.primary_metric)
supporting_metrics_set.update(experiment.secondary_metrics)
if influencer_signal:
supporting_metrics_set.add("influencer reach")
if stock_signal:
supporting_metrics_set.add("market return correlation")
if statistics:
supporting_metrics_set.add("statistical success probability")
if investing_plan and investing_plan.kpis:
supporting_metrics_set.add(investing_plan.kpis[0])
if cognitive_plan:
supporting_metrics_set.add("cognitive readiness index")
if agentic_plan:
supporting_metrics_set.add(agentic_plan.metrics[0] if agentic_plan.metrics else "agentic readiness")
if language_plan and language_plan.evaluation_metrics:
supporting_metrics_set.add(language_plan.evaluation_metrics[0])
if meta_plan and meta_plan.metrics:
supporting_metrics_set.add(meta_plan.metrics[0])
if code_automation:
supporting_metrics_set.add(
code_automation[0].metrics[0] if code_automation[0].metrics else "automation stability"
)
if marketing_plan and marketing_plan.metrics:
supporting_metrics_set.add(marketing_plan.metrics[0])
if quantum_plan:
supporting_metrics_set.add("quantum stability")
if physics_plan:
supporting_metrics_set.add("physics_economics_readiness")
directive = StrategicDirective(
name=f"Directive {index + 1}: {score.campaign.channel.title()} Surge",
priority=index + 1,
objective=score.campaign.objective,
primary_channel=channel,
supporting_channels=supporting_channels,
investment_usd=round(allocation.allocation_usd, 2) if allocation else None,
expected_revenue_usd=round(allocation.expected_revenue, 2) if allocation else None,
confidence=directive_confidence,
time_horizon_hours=time_horizon,
north_star_metric=north_star_metric,
supporting_metrics=sorted(supporting_metrics_set),
action_steps=action_steps,
dependencies=dependencies,
risk_mitigations=risk_mitigations,
inspiration=" | ".join(
filter(
None,
[
experiment.name if experiment else None,
learnings[0].play_name if learnings else None,
competitors[0].competitor if competitors else None,
rag_channels.get(index),
],
)
)
or "Campaign intelligence fusion",
)
directives.append(directive)
summary = (
f"Synthesize {len(directives)} directives to accelerate {diagnostics.stage} outcomes "
f"for {diagnostics.focus}."
)
notes_candidates = []
if excellence:
notes_candidates.append(excellence.headline)
if budget:
notes_candidates.append(budget.summary)
if automation:
notes_candidates.append(automation.summary)
if influencer_pool:
notes_candidates.append(
(
f"Influencer plan anchored by {influencer_pool[0].name} "
f"({influencer_pool[0].projected_roi:.1f}x ROI outlook)"
)
)
if stocks:
top_stock = stocks[0]
notes_candidates.append(
(
f"Market outlook: {top_stock.name} {top_stock.trend} with "
f"{top_stock.forecast_return_90d*100:.0f}% 90d upside"
)
)
if statistics:
notes_candidates.append(statistics.narrative)
if cognitive:
notes_candidates.append(
f"Cognitive architecture anchored by {cognitive[0].capability} ({cognitive[0].maturity})."
)
if language:
notes_candidates.append(
f"Language workflow {language[0].capability} ({language[0].maturity}) powering conversational loops."
)
if meta_learning:
notes_candidates.append(
f"Meta-learning {meta_learning[0].capability} ({meta_learning[0].maturity}) keeps experimentation adaptive."
)
if code_automation:
notes_candidates.append(
f"Code automation {code_automation[0].capability} ({code_automation[0].maturity}) safeguards release velocity."
)
if payments:
notes_candidates.append(
f"Payments orchestrated via {payments[0].provider} ({payments[0].maturity})."
)
if investing:
notes_candidates.append(
f"Investing runway anchored by {investing[0].provider} ({investing[0].maturity})."
)
if agentic:
notes_candidates.append(
f"Agentic system {agentic[0].system_name} ({agentic[0].maturity}) orchestrates tool stack."
)
if quantum:
notes_candidates.append(
f"Quantum simulator {quantum[0].simulator} covers {len(quantum[0].stress_tests)} stress scenarios."
)
if physics_economics:
notes_candidates.append(
f"Physics/economics plan {physics_economics[0].capability} ({physics_economics[0].maturity}) guiding complex mechanics."
)
notes = " | ".join(notes_candidates) if notes_candidates else None
return ExecutivePlaybook(
headline=
f"{diagnostics.stage.title()} momentum for {diagnostics.focus} with autonomous orchestration",
focus=diagnostics.focus,
urgency=diagnostics.urgency,
north_star_metric=north_star_metric,
total_budget_usd=round(budget.total_budget, 2) if budget else None,
expected_revenue_usd=round(budget.expected_revenue, 2) if budget else None,
confidence=overall_confidence,
directives=directives,
summary=summary,
notes=notes,
)
@lru_cache(maxsize=1)
def default_marketing_engine() -> MarketingIntelligenceEngine:
return MarketingIntelligenceEngine.from_file()