The Conversational Customer Acquisition 2.0 System
A complete customer-acquisition operating system — from deep research to sponsored readiness — designed to make trustworthy businesses understandable, verifiable, recommendable, and usable inside AI conversations.
Deep Research
Understand the real customer, the real business, and how AI currently perceives it.
Core question
Who is the customer, what do they actually ask AI, and how does AI see us today?
Main work
Customer and money-question research, competitive landscape, and an AI perception review across major assistants.
Why it matters to AI
Reveals the gaps and ambiguities that make AI hesitate to recommend the business.
Why it matters to the customer
Ensures everything built next is anchored to the questions real customers actually have.
Money Dialogs
Map the high-stakes conversations customers have with AI right before they buy.
Core question
What is the customer really trying to decide when money is on the line?
Main work
Document decision moments, objections, comparisons, and the language customers use with AI.
Why it matters to AI
Gives AI the context to match your business to a specific customer situation.
Why it matters to the customer
Meets customers inside the exact decisions they're trying to make.
Trust Foundation
Make identity, services, pricing, locations, and proof consistent and verifiable.
Core question
Is our core information clear, consistent, and structured everywhere it appears?
Main work
Standardize facts, remove marketing fluff, add structured data, and reconcile listings.
Why it matters to AI
Removes the Trust Tax so AI can verify and safely reference the business.
Why it matters to the customer
Gives customers clear, honest answers instead of vague marketing language.
Hub of Truth & DSA
Create the canonical, machine-readable source of truth — and a callable Decision Support Asset.
Core question
Where is the single source of truth AI and software can rely on and call?
Main work
Build the Hub of Truth and design a Decision Support Asset AI can use to guide a decision.
Why it matters to AI
Gives AI something to reference and, when appropriate, actively call.
Why it matters to the customer
Turns static information into help — a tool that guides a real next step.
Authority Expansion
Strengthen the third-party signals and citations that build AI confidence over time.
Core question
What credible, external evidence supports our claims?
Main work
Secure citations, directory accuracy, and authoritative mentions aligned to the Trust Foundation.
Why it matters to AI
Improves the consistency of authority signals AI weighs before recommending.
Why it matters to the customer
Reassures customers with independent validation, not just self-promotion.
Conversational Content
Publish scenario-based content and distribute it where AI systems learn and cite.
Core question
Does our content answer the specific situations customers ask AI about?
Main work
Create context-rich, question-driven content mapped to Money Dialogs and distribute it.
Why it matters to AI
Helps AI understand context and match you to nuanced customer questions.
Why it matters to the customer
Delivers genuinely useful answers to real scenarios, not keyword filler.
Proof Loop & AIR
Measure AI Recommendation Readiness (AIR) and improve it on a continuous loop.
Core question
Is our recommendation readiness improving — and where should we focus next?
Main work
Track readiness dimensions, document changes, and prioritize the next improvements.
Why it matters to AI
Creates a feedback loop so readiness keeps improving instead of stagnating.
Why it matters to the customer
Ensures the experience keeps getting clearer and more helpful over time.
API, OpenAPI & MCP
Make the business machine-usable so AI systems can query and call it directly.
Core question
Can software and AI agents access our information and tools programmatically?
Main work
Expose a canonical data model, an OpenAPI description, and MCP-ready architecture.
Why it matters to AI
Lets AI agents retrieve accurate data and call approved tools with confidence.
Why it matters to the customer
Enables faster, more accurate help wherever the customer is talking to AI.
Sponsored Readiness
Prepare chat-native creative and conversion paths for sponsored amplification.
Core question
Are we ready to show up as a helpful, sponsored recommendation when relevant?
Main work
Draft conversational creative and clear conversion paths for when ad inventory applies.
Why it matters to AI
Prepares placements that feel like helpful suggestions, not interruptions.
Why it matters to the customer
Presents timely, relevant options at the moment of decision.
Betweener Engineering™
Betweener Engineering is TBFA's advanced identity, semantic-positioning, and category-creation methodology. It is applied within the larger CCA 2.0 system when a business needs differentiated category authority — not as a replacement for it.
It may be used when a business needs:
See Where Your Business Stands Today
The AI Recommendation Readiness Audit shows you exactly what AI can — and can't — understand about your business.
Take the Audit