System: MWMS
Document Type: Framework
Authority Level: MCR Source Of Truth
Status: Draft For MCR
Primary Location: MCR
Future Operational Destination: HeadOffice Brain, AI Business Systems Brain, Content Brain, Offer Brain, Sales Brain, Creative Brain, AI Manager, AI Employee Router, Course Absorption System, Future AIBS Client Systems
Parent Page: HeadOffice
Owner: Martyn
Developer Boundary: No Development Action Authorized By This Page
Source Of Truth: MCR
Purpose
The purpose of this document is to define the MWMS Client IP Excavation Framework.
This framework establishes how MWMS extracts the real intellectual property, expertise, voice, methodology, decision logic, customer understanding, offer logic, objections, and differentiation from a founder, expert, business, offer, or client before building AI systems or customer-facing assets.
MWMS must not build serious AI systems from shallow prompts.
MWMS must build from structured business intelligence.
This framework exists because most weak AI output is not caused by the AI tool itself.
It is caused by missing context.
If MWMS does not extract the human intelligence behind a business first, AI outputs may become:
generic
off-brand
over-polished
strategically weak
disconnected from the buyer
unsupported by real customer language
unable to reproduce expert judgment
dependent on repeated manual explanation from Martyn or the client
The Client IP Excavation Framework ensures that every serious MWMS client Brain, offer Brain, AI Employee, content system, funnel, ad system, sales asset, or future AIBS client system begins with the real thinking underneath the business.
Scope
This framework applies to all MWMS work where a business, offer, founder, client, expert, or internal MWMS system needs to be converted into reusable AI context.
This includes:
AI Business Systems Brain
HeadOffice Brain
Offer Brain
Content Brain
Creative Brain
Sales Brain
Conversion Brain
Customer Brain
Research Brain
Affiliate Brain
Ads Brain
AI Manager
AI Employee Router
Course Absorption System
Client Brain creation
future AIBS client systems
offer context library creation
AI Employee setup
content system creation
ad angle generation
VEO3 script systems
lead magnet creation
webinar creation
sales page creation
email sequence creation
client onboarding systems
This framework applies before MWMS creates major AI-generated business assets.
It does not authorize technical development, plugin changes, Supabase changes, WordPress changes, automation wiring, or M developer action.
Core Definition
Client IP Excavation refers to the structured process of extracting the business intelligence that makes a founder, expert, offer, client, or internal MWMS system distinct and usable by AI.
This includes:
what the business believes
what the founder rejects
what the market misunderstands
how the expert works
how the expert diagnoses problems
how the offer creates transformation
how the buyer thinks and speaks
what objections block action
what language should be preserved
what language should be retired
what makes the offer different
what proof supports the offer
what decision logic AI Employees must follow
Client IP Excavation is not copywriting.
It is not summarization.
It is not prompt writing.
It is the creation of the intelligence layer that sits underneath future AI work.
Core Principle
The core principle of this framework is:
Extract the human intelligence first. Build the AI system second.
AI should not be asked to create important business outputs until MWMS understands the real context behind the business or offer.
Prompts tell AI what to produce.
Context tells AI what it is working from.
The stronger the context layer, the stronger the AI output.
The weaker the context layer, the more generic, unstable, and unreliable the output becomes.
Primary Excavation Layers
MWMS uses three primary excavation layers before building a full context library.
These layers are:
Contrarian Stances
Methodology And Process
Expert Thinking
These three layers form the foundation of the business intelligence layer.
Layer 1: Contrarian Stances
Contrarian Stances define what the founder, expert, business, or offer believes that the market usually gets wrong.
This layer extracts:
industry advice the founder rejects
mainstream beliefs the offer challenges
harmful best practices
bad assumptions in the market
false simplicity
false complexity
villains in the current market
beliefs that create buyer pain
what the founder believes instead
what the offer exists to replace
Contrarian Stances are important because they create the spine of strong positioning.
They support:
ad hooks
VEO3 pre-video angles
email openers
social content
sales page contrast
lead magnet positioning
webinar framing
founder authority
category differentiation
creative strategy
Weak stance:
Businesses need better AI prompts.
Stronger stance:
Businesses do not need more AI prompts. They need a context layer that stops every AI tool from starting from zero.
Layer 2: Methodology And Process
Methodology And Process define how the founder, expert, business, or offer creates transformation.
This layer extracts:
the first move
the sequence
the framework
the system
the delivery logic
the process steps
the skipped steps
the named mechanism
the transformation path
the operational method
what must happen before output is created
what happens if the process is followed correctly
Methodology is important because AI Employees cannot act like trained operators if they do not understand the process they are meant to follow.
This layer supports:
AI Employee workflows
SOPs
client delivery
offer fulfillment
training systems
sales mechanisms
content frameworks
funnel logic
course architecture
internal MWMS Brain modules
Weak methodology:
We help businesses use AI better.
Stronger methodology:
We extract the founder’s beliefs, process, customer language, offer logic, and expert judgment into a structured context library before building AI Employees or business assets.
Layer 3: Expert Thinking
Expert Thinking defines the hidden judgment behind expert decisions.
This layer extracts:
diagnostic logic
pattern recognition
decision branches
quality thresholds
judgment calls
when to slow down
when to escalate
what to check first
how similar problems are separated
how advice changes based on context
what makes the expert reject an output
what signals beginners usually miss
Expert Thinking is important because a checklist tells AI what to do, but expert judgment tells AI when and why to adapt.
This layer supports:
AI Employee decision logic
Research Brain
Ads Brain
Offer Brain
Sales Brain
Conversion Brain
Experimentation Brain
HeadOffice governance
client advisory systems
quality control
strategic routing
Weak expert thinking:
Check the data before making changes.
Stronger expert thinking:
Before changing the ad, check whether the landing page click-through is healthy. If the ad is getting attention but VSL clicks are weak, the problem may be message continuity rather than creative quality.
Required Source Material
MWMS should gather source material before excavation begins.
The goal is not to create polished new content.
The goal is to collect existing raw evidence.
Useful source material may include:
emails
newsletters
sales pages
landing pages
social posts
course outlines
framework notes
SOPs
client onboarding documents
proposal documents
case studies
testimonials
customer replies
sales call notes
objections
support messages
discovery notes
audit documents
Loom reviews
training material
voice memos
workshop notes
strategy documents
buyer surveys
Raw material is valuable because it contains real language, real thought patterns, real objections, and real decision signals.
MWMS must not over-clean source material before excavation.
The rough language is often where the strongest intelligence lives.
Excavation Modes
MWMS uses two excavation modes.
Brain Dump Mode
Brain Dump Mode is used when the founder, business, offer, or client already has useful source material.
This is the preferred mode when enough evidence exists.
Brain Dump Mode requires the AI Employee to:
read the supplied material
extract only what is present
preserve strong real phrases
identify repeated themes
separate signal from noise
flag gaps
avoid invention
prepare structured outputs for review
Brain Dump Mode is best for:
established businesses
course creators
consultants
agencies
experts with content archives
brands with past launch material
offers that have already sold
internal MWMS systems with existing pages
Conversation Mode
Conversation Mode is used when source material is thin, scattered, or missing.
In Conversation Mode, the AI Employee interviews the founder, client, or operator one question at a time.
Conversation Mode requires the AI Employee to:
ask one question at a time
wait for the answer
reflect the answer briefly
ask a useful follow-up if the answer is shallow
avoid leading the user into fake clarity
flag gaps
compile only after enough material has been gathered
Conversation Mode is best for:
early-stage businesses
new offers
messy ideas
experts who think out loud
clients without documentation
internal MWMS concepts still being clarified
new Brain concepts
Standard MWMS Excavation Workflow
The standard workflow is:
Select One Offer Or System
Gather Source Material
Choose Brain Dump Mode Or Conversation Mode
Extract Contrarian Stances
Extract Methodology And Process
Extract Expert Thinking
Review For Specificity
Flag Missing Evidence
Confirm With Human Operator
Approve For Context Library Construction
No serious output should be created until the excavation layer has been reviewed.
Core Output Files
This framework produces three foundation files.
My Contrarian Stances
Purpose:
Captures what the founder, business, offer, or system believes that the market gets wrong.
Used by:
Content Brain
Creative Brain
Ads Brain
Sales Brain
Offer Brain
Affiliate Brain
VEO3 script systems
My Methodology
Purpose:
Captures the process, framework, system, or mechanism that creates the transformation.
Used by:
AI Business Systems Brain
Offer Brain
Sales Brain
Content Brain
AI Employee workflows
client delivery systems
My Expert Thinking
Purpose:
Captures judgment, diagnostic logic, pattern recognition, and decision rules.
Used by:
HeadOffice Brain
AI Manager
AI Employee Router
Research Brain
Experimentation Brain
Offer Brain
Ads Brain
Conversion Brain
Future AIBS client systems
These three files become the foundation for the broader context library.
Full Context Library Destination
After excavation, the next system may create a broader context library.
The broader context library may include:
Right-Fit Client Profile
Offer Profile
Voice Architecture
Differentiation Profile
Objection Library
Brand Visual Style
Retired Language
Proof Library
Customer Language Bank
Methodology Map
Expert Thinking Rules
The excavation layer feeds these files.
The broader context library should not be built until the excavation files are approved.
MWMS Operating Rules
Rule 1: One Offer At A Time
MWMS must avoid extracting an entire business at once unless the task is specifically brand-wide.
Offer-specific excavation is sharper than general business extraction.
Rule 2: Extract, Do Not Invent
The AI Employee must not fabricate:
founder beliefs
proof
examples
customer language
testimonials
objections
results
statistics
case studies
expert quotes
If material is missing, mark it clearly.
Use:
Missing Evidence
Requires Founder Confirmation
Requires Customer Data
Requires Offer Clarification
Rule 3: Preserve Useful Real Language
MWMS should preserve strong phrases from the founder, buyer, or source material where useful.
Over-polishing weakens voice.
Rule 4: Separate Founder Beliefs From Buyer False Beliefs
Founder beliefs and buyer false beliefs are not the same thing.
Contrarian Stances belong to the founder or business.
False beliefs belong to the buyer before they understand the offer.
Rule 5: Separate Methodology From Expert Thinking
Methodology is the process.
Expert Thinking is the judgment used to apply the process.
Both must be captured separately.
Rule 6: Do Not Build Assets Too Early
MWMS must not rush into:
ads
scripts
funnels
lead magnets
webinars
emails
sales pages
AI Employees
client systems
until the intelligence layer is clear enough.
Rule 7: Human Review Is Required
Excavation outputs must be reviewed by Martyn, the founder, or the client before becoming source context for future work.
Rule 8: Weak Material May Be Parked
If the course, client, offer, or founder material is not strong enough yet, MWMS may park the excavation instead of forcing a full context library.
Quality Standards
A strong excavation output should be:
specific
grounded
usable
distinct
offer-relevant
buyer-aware
AI-readable
strategically useful
capable of improving future outputs
A weak excavation output will usually be:
generic
polished but empty
not tied to an offer
not tied to a buyer
missing real language
missing judgment
missing process
filled with invented clarity
too broad to guide AI Employees
Quality Check Questions
Before approving the excavation, ask:
Does this sound specific to this business or offer?
Could this apply to almost anyone?
Is the founder’s actual thinking visible?
Is the methodology clear enough to follow?
Is the expert judgment captured?
Are any claims invented?
Are any examples unsupported?
Does this improve future AI output?
Would an AI Employee know what to do with this?
Does this need more source material?
Does this need founder confirmation?
Common Failure Modes
MWMS must prevent:
generic AI strategy language
fake contrarian positioning
invented expertise
over-polished founder voice
confusing buyer pain with founder belief
methodology reduced to a vague list
expert judgment missing entirely
building assets before context is ready
mixing multiple offers
mixing client and MWMS context
treating rough draft outputs as canon
creating duplicate MCR pages from partial insight
absorbing course language without translating it into MWMS architecture
Integration With MWMS Brains
AI Business Systems Brain
Uses this framework as the first serious client-intelligence intake stage before building client AI systems.
HeadOffice Brain
Governs when excavation is required, whether outputs are approved, and whether material moves into MCR or remains parked.
Offer Brain
Uses the extracted methodology, transformation logic, objections, and positioning to strengthen offer structure.
Content Brain
Uses contrarian stances, customer language, and expert thinking to create more grounded content.
Creative Brain
Uses belief tension, villains, and emotional contrast to create stronger creative angles.
Ads Brain
Uses extracted stances and buyer contrast to create hooks, pre-video concepts, and campaign angles.
Sales Brain
Uses methodology, objections, and expert thinking to strengthen sales conversation systems.
Research Brain
Uses the excavation files to compare internal beliefs with external market evidence.
Experimentation Brain
Tests which extracted beliefs, angles, messages, and mechanisms perform best.
Conversion Brain
Uses extracted objections, anxieties, and transformation logic to improve page and funnel conversion.
Future AIBS Client Systems
Uses the framework as the standard first step in creating client-specific AI systems.
AI Employee Implications
This framework supports future AI Employees including:
Client IP Excavator
Founder Interview Agent
Methodology Mapper
Contrarian Stance Extractor
Expert Thinking Mapper
Context Library Builder
Voice Architecture Builder
Offer Context Librarian
Client Brain Intake Agent
Extraction Quality Auditor
These employees must not invent missing intelligence.
They must extract, structure, flag gaps, and prepare review-ready context.
Governance Role
HeadOffice owns the MWMS Client IP Excavation Framework.
HeadOffice is responsible for:
deciding when excavation is required
ensuring the correct Brain receives the output
preventing premature asset creation
preventing duplicate MCR pages
ensuring source material is not treated as canon too early
ensuring human review occurs before promotion
protecting M’s active development boundaries
ensuring future AIBS client use remains safe and structured
Individual Brains may use the framework for their own work, but HeadOffice governs cross-Brain usage and MCR promotion.
Relationship To Other MWMS Standards
This framework supports and must align with:
MWMS Document Structure Standard
MWMS Course Absorption Operating Rule
MWMS AI Agent Memory And Context Framework
MWMS AI Agent Skill Library Framework
MWMS Messy Input Normalization Framework
MWMS AI Output Validation Standard
MWMS AI Employee Role Card Standard
MWMS AI Employee Capability Stack Framework
MWMS Brain Routing Rule
MWMS MCR Promotion To Brain Protocol
MWMS Page Naming Standard
MWMS Architecture Registry
Content Brain VOC Grounded AI Copy Framework
Research Brain Voice Of Customer Extraction Framework
Offer Brain Offer Structure Framework
Creative Brain Belief Shift Framework
Sales Brain Conversation Structure Framework
AI Business Systems Brain Canon
This framework provides the pre-context extraction layer that helps those standards operate with better source intelligence.
Drift Protection
This framework protects MWMS from:
building AI systems on weak prompts
treating generic course ideas as MWMS canon
inventing founder IP
inventing customer language
over-polishing real voice
mixing multiple offers
creating context libraries too early
building AI Employees without source intelligence
creating duplicate strategy pages
confusing methodology with expert judgment
confusing buyer false beliefs with founder stances
using external course language without MWMS translation
promoting draft insights into MCR without review
Any AI workflow that attempts to create major business assets without a proper context layer should be reviewed before use.
Architectural Intent
The architectural intent of the MWMS Client IP Excavation Framework is to create a reliable intelligence foundation before AI work begins.
MWMS is not intended to become a collection of prompts.
MWMS is intended to become a governed AI business ecosystem.
That ecosystem requires context, memory, skill, governance, and source-of-truth discipline.
The long-term goal is that every serious MWMS or client system can answer:
What does this business believe?
How does this offer work?
What does the buyer misunderstand?
What process creates the transformation?
What judgment does the expert apply?
What language should AI preserve?
What should AI avoid?
What context must future AI Employees read?
What gaps require human confirmation?
When MWMS can answer these questions, AI output becomes more specific, more strategic, more trustworthy, and more useful.
Change Log
v1.0 — Initial Draft
Created the MWMS Client IP Excavation Framework as the structured intake framework for extracting founder beliefs, methodology, expert thinking, customer understanding, offer logic, objections, differentiation, and reusable business intelligence before building MWMS AI systems, client Brains, offer libraries, AI Employees, funnels, content systems, ad systems, or future AIBS client systems.
This framework establishes the three primary excavation layers: Contrarian Stances, Methodology And Process, and Expert Thinking.
It defines excavation modes, source material requirements, workflow rules, output files, quality standards, failure modes, Brain integrations, AI Employee implications, governance role, drift protection, and architectural intent.
Change Impact Declaration
Pages Created:
MWMS Client IP Excavation Framework
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
MWMS Architecture Registry
HeadOffice Page Registry
AI Business Systems Brain Page Registry
Content Brain Page Registry
Offer Brain Page Registry
Canon Version Update Required:
No
Change Log Entry Required:
Yes
Employee Impact Check
Employees impacted:
HeadOffice Manager Employee
Course Absorption Agent
Client IP Excavator
Context Library Builder
Content Planner Employee
Creative Strategist Employee
Offer Strategist Employee
Sales Strategist Employee
Research Analyst Employee
AI Business Systems Architect Employee
Required behaviour updates:
AI Employees must not build major business assets before checking whether a sufficient context layer exists.
AI Employees must extract founder beliefs, methodology, and expert thinking before building full context libraries.
AI Employees must preserve useful real language and avoid inventing founder IP, customer language, testimonials, proof, or expert judgment.
AI Employees must flag missing source material instead of filling gaps with generic AI assumptions.
AI Employees must route approved excavation outputs into the appropriate Brain or future context library workflow.
END MWMS CLIENT IP EXCAVATION FRAMEWORK v1.0