Document Type: Canon
Status: Canon
Version: v1.2
Authority: HeadOffice
Applies To: All Operational Brains
Parent: Research Brain Canon
Last Reviewed: 2026-03-15
Purpose
Research Brain Architecture defines:
• structural components
• data flow
• logging hierarchy
• cross-brain integration rules
• enforcement boundaries
• reporting mechanisms
It governs how intelligence moves through MWMS.
Scope
This canon applies to:
• Research Brain structural layers
• intelligence intake and classification flow
• hypothesis and experiment linkage rules
• competitor and win/loss intelligence handling
• pattern detection and reporting flow
• cross-brain research integration requirements
• research governance enforcement across operational Brains
This document governs how research intelligence is structured, linked, and transferred inside MWMS.
It does not govern:
• campaign execution
• capital allocation
• scaling approval
• direct operational commands
• independent decision-making by Research Brain
Research Brain remains advisory and reporting-focused only.
Definition / Rules
Architectural Purpose
Research Brain Architecture defines the structural model through which intelligence moves across MWMS.
It exists to ensure that insights are captured, classified, linked to experiments, fed back into system memory, and elevated into pattern reporting without drift or loss.
Core Architectural Layers
Research Brain consists of 5 mandatory layers.
Layer 1 – Insight Intake Layer
Purpose:
Capture raw signals before interpretation.
Sources include:
• course absorption
• newsletter intelligence
• competitor monitoring
• test data
• win/loss surveys
• win/loss interviews
• market research tools
• manual observation
Mandatory fields:
• Insight_ID
• Date_Logged
• Source_Type
• Source_Link
• Domain_Tag
• Initial_Theme_Tag
• Raw_Observation
No filtering is allowed at the intake stage.
Layer 2 – Evidence Classification Layer
Purpose:
Determine signal strength.
Each Insight_ID must be evaluated for:
• Evidence_Source_Count
• Evidence_Independence
• Evidence_Type_Diversity
Classification:
• 1 Source → Exploratory
• 2 Independent Sources → Emerging
• 3+ Independent Sources → Validated
Evidence_Density must be calculated.
Confidence_Level must be declared separately.
Layer 3 – Hypothesis and Experiment Linkage Layer
Purpose:
Prevent assumption-based execution.
Rule:
No experiment may launch without referencing Insight_ID.
Each experiment must log:
• Experiment_ID
• Hypothesis_Statement
• Linked_Insight_ID
• Expected_Mechanism
• Test_Type
• Traffic_Source
• Funnel_Stage
Upon completion, within 48 hours, the following must be logged:
• Outcome
• Revenue_Impact
• Statistical_Confidence
• Learning_Notes
Learning must update the originating Insight_ID.
Layer 4 – Competitive and Win/Loss Intelligence Layer
Purpose:
Track structured market signals.
For each Domain:
Maximum 5 Primary Competitors tracked.
Per competitor, log:
• positioning shifts
• pricing updates
• funnel structure changes
• traffic expansion
• messaging updates
• offer additions
• market signal flags
Win/Loss must log:
• Won / Lost
• mechanism acceptance or rejection
• objection category
• refund signals
• competitive displacement mention
This layer feeds Pattern Detection.
Layer 5 – Pattern Detection and Meta-Analysis Layer
Purpose:
Convert data into intelligence.
Quarterly Meta-Analysis is mandatory.
It must detect:
• repeating Theme_Tags
• cross-domain correlations
• mechanism trend acceleration
• angle fatigue
• refund clustering
• competitive saturation
• market timing signals
Output:
Research Pattern Report
This report is delivered to HeadOffice.
Research Brain cannot act on findings.
It may only report.
Data Flow Model
Intake
↓
Classification
↓
Hypothesis Linkage
↓
Experiment Execution (External Brain)
↓
Outcome Logging
↓
Pattern Detection
↓
HeadOffice Review
No bypass is allowed.
Cross-Brain Integration Rules
Affiliate Brain must:
• reference Insight_ID before Phase 4
• log outcomes within 48 hours
• update Research records
PPL Brain must:
• log local competitor signals
• log conversion pattern changes
• submit win/loss notes
Opportunity Brain must:
• submit market viability signals
• log saturation indicators
AIBS Brain must:
• log B2B trend signals
• log infrastructure shifts
Research Brain remains neutral.
Governance Enforcement
Failure to log:
• blocks Stage 4
• blocks scaling
• triggers SIT Drift Alert
No deletion is allowed.
Archive only.
Retroactive modification is prohibited.
Transition Plan
Phase 1 – Foundation (Immediate)
• create Research Brain database table
• move Insight_ID authority from Affiliate to Research
• require Insight_ID for new experiments
• mirror existing Affiliate research into Research table
Phase 2 – Stabilization
• require all experiment logs to update Research
• begin competitor tracking logs
• begin win/loss structured logging
• create quarterly pattern report template
Phase 3 – Maturity
• automate pattern clustering
• add cross-domain correlation metrics
• add mechanism trend scoring
• integrate Research dashboard into HeadOffice
Required Database Structure (High-Level)
Tables required:
• research_insights
• research_evidence
• research_experiments
• research_competitors
• research_win_loss
• research_patterns
All must be linked via Insight_ID and Domain_Tag.
No standalone test data is allowed outside linkage.
Research Brain Employees (Future Layer)
Future advisory employees may include:
• Insight Registrar
• Evidence Classifier
• Hypothesis Validator
• Competitive Monitor
• Pattern Analyst
All are advisory only.
No execution power is permitted.
Activation Checklist (Immediate Actions)
To activate v1.0:
☐ Canon page created
☐ Architecture page created
☐ Database tables defined
☐ Affiliate updated to require Insight_ID
☐ SIT monitoring rule updated
☐ First 5 Insight_ID entries created
☐ Competitor tracking list initialized
Structural Outcome
When operational:
• no repeated failed angles
• no forgotten lessons
• no emotional scaling
• no competitor blind spots
• cross-domain pattern awareness active
• evidence-driven decision culture
Drift Protection
The system must prevent:
• experiments launching without Insight_ID linkage
• research observations existing without intake structure
• outcome learning failing to flow back into originating insight records
• standalone test data being stored outside Research linkage
• operational Brains bypassing Research for high-confidence claims
• retroactive alteration of research outcome history
Research intelligence must remain linked, cumulative, and auditable.
Architectural Intent
Research Brain Architecture exists to create a governed intelligence spine for MWMS.
Its role is to ensure that observations become structured insights, experiments remain evidence-linked, competitive and win/loss signals are preserved, and pattern reporting reaches HeadOffice without intelligence drift or historical loss.
Final Rule
If intelligence cannot be linked, traced, and updated through the Research flow, it is not structurally reliable enough to support MWMS learning.
Unlinked intelligence is weak intelligence.
Change Log
Version: v1.2
Date: 2026-03-15
Author: MWMS HeadOffice
Change: Standardised the page fully to the locked cleanup format for this pass. Preserved the original five-layer architecture, data-flow model, cross-brain integration rules, governance enforcement, transition plan, required database structure, future employee layer, activation checklist, structural outcome, and drift-protection logic. Added a dedicated Architectural Intent section, a dedicated Final Rule section, and updated the review date.
Version: v1.1
Date: 2026-03-14
Author: MWMS HeadOffice
Change: Rebuilt Research Brain Architecture to align with MWMS document standards. Added Document Type header, formalised Purpose / Scope / Definition / Rules structure, normalised layer formatting, simplified Parent metadata to page reference format, and preserved the original five-layer architecture, data-flow logic, enforcement rules, and activation checklist.
Version: v1.0
Date: 2026-03-03
Author: HeadOffice
Change: Initial creation of Research Brain Architecture defining structural layers, data flow, cross-brain integration rules, database structure, transition plan, and activation checklist for Research Brain.
END – RESEARCH BRAIN ARCHITECTURE v1.2