Case Studies

Extractions We've Done

Real examples of expertise extraction - what we built, how it works, what the clients use it for.

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Case Study 01

Dr. Joe Vitale

Author, Speaker, Teacher - 40+ years in personal development

The Situation

Four decades of teaching across consciousness, manifestation, and spiritual entrepreneurship. From The Attractor Factor to Zero Limits to modern integration of Eastern and Western philosophy.

Joe's thinking evolved significantly from the 1990s to the 2020s - from basic manifestation principles to consciousness-based creation to integrated spiritual practice. This evolution was valuable, but scattered across 80+ books, hundreds of recordings, and decades of teaching.

The Extraction

408
Canonical Entities
545
Relationships
21
Custom Node Types
72.8%
More Connections via Cross-Source Discovery

Temporal properties tracking how his thinking evolved across decades. The graph understands evolution - it knows that 'Ho'oponopono (2005)' evolved into 'Radical Forgiveness (2015)' which integrated with 'Zero Limits Consciousness (2020).'

[ Master Graph ]

Loading Joe Vitale's Master Graph...

408 nodes • 535 relationships

What It's Used For

Personalized Guidance

An AI system that delivers teaching based on where each person is in their journey. The graph detects context and delivers appropriate frameworks - not generic advice.

Content Generation

Newsletter drafts, social posts, book chapter outlines - all grounded in Joe's actual philosophy and written in his voice. He edits and approves; the structure does the drafting.

Assessment & Recommendation

A guided conversation that diagnoses each person's situation, then generates personalized recommendations, audio content, and action plans tailored to their specific needs.

Methodology Preservation

Joe's life work now exists as reasoning intelligence, not just static content. It can answer questions he never explicitly addressed - but which are consistent with his thinking.

This captures how I think - not just what I've said. It can answer questions I didn't know I knew, because it understands the logic beneath my teaching.

Dr. Joe Vitale

Case Study 02

Dan Hackett

Financial Consultant - 20+ years in business diagnostics

The Situation

Decades of pattern recognition compressed into instant analysis. Dan could look at any business's financials and spot issues in under a minute - the subtle patterns, the hidden inefficiencies, the opportunities others missed.

This wasn't textbook knowledge. It was thousands of diagnoses over decades, building instinct that couldn't be taught through conventional training. Dan's genius was trapped in his intuition. Every business that needed his eyes required his time.

The Extraction

667
Entities Mapped
734
Relationships
100+
Decision Heuristics
30s
Initial Analysis Time

A diagnostic knowledge graph that encodes his heuristics into structured, executable logic. Every conditional. Every exception. Every pattern he recognized unconsciously - mapped into queryable structure.

[ Master Graph ]

Loading Dan Hackett's Master Graph...

667 nodes • 734 relationships

What It's Used For

Diagnostic Automation

An AI system that performs initial financial analysis using Dan's pattern recognition - doing the work of a junior analyst with the precision of a veteran. Reduces his time per client by roughly 60%.

Training Infrastructure

New analysts learn Dan's methodology by interacting with his graph. They see how he thinks, why he looks where he looks, what patterns he recognizes. Training accelerated from years to months.

Client Self-Service

Clients can receive preliminary analysis using Dan's heuristics - creating a tier of service that doesn't require his direct involvement.

Exit Documentation

Dan's methodology is now documented, systematized, and transferable. Not tribal knowledge - intellectual property that increases business valuation.

Results

97%
Accuracy Rate
40+
Hours Saved/Week
Years to Months
Training Reduction
It captures the instinct. It sees what I see, in the order I see it. I'm not explaining myself over and over anymore - the structure holds my reasoning.

Dan Hackett

The Pattern

What Both Cases Share

1

Infrastructure, Not Tools

Neither project produced a simple chatbot or FAQ system. Both created structured knowledge graphs that can reason through the expert's logic - not just search their words.

2

Ownership, Not Subscription

Both experts own their graphs permanently. The files are portable, platform-agnostic, and buildable. No vendor lock-in. No monthly fees to access their own expertise.

3

Multiple Uses from One Extraction

Both extractions power multiple applications simultaneously. The same structured foundation serves different purposes depending on what the expert needs.

4

Fidelity, Not Approximation

Both experts validated that the systems reason like they do - not just echo what they've said. When the graph doesn't know something, it acknowledges uncertainty rather than making things up.

Interested in What This Could Look Like For You?

Every extraction is different - shaped by the expert's domain, their existing materials, and what they want to build. If you're curious whether this makes sense for your situation, let's talk.