TSN #39 - The AI Context Enhancement Business

Hey! 👋 This week's idea was inspired by Sam Parr's experiment: what happens when you feed deep personal or organizational analysis into AI to create a hyper-personalized decision support system?

Enjoy!

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🎬 The Pitch

Imagine building a service that creates detailed "AI context layers" for individuals and companies. Instead of generic AI responses, you're enabling AI systems that deeply understand the client's background, goals, constraints, and unique circumstances.

Here's the concept: Conduct thorough analysis through stakeholder interviews and documentation review, then transform this information into optimized AI prompts and context layers. Your clients get a powerful AI thought partner that actually understands their specific situation and can provide highly relevant guidance.

The best part? Once you build the methodology, you're not just selling analysis - you're enabling an AI-powered advisor that gets smarter and more valuable every day.

📊 Market Insights

The AI personalization market is booming:

• Companies spend 40% of AI time on context setting

• Personalized AI responses are 3x more effective

• 82% of executives want AI that understands their context

• Average company loses 20 hours/week to poor AI prompting Source: AI Implementation Report 2024

💡 The Concept

Service Components:

1. Initial Analysis:

   • Stakeholder interviews

   • Goals documentation

   • Resource mapping

   • Constraint analysis

   • Decision patterns

2. AI Enhancement Layer:

   • Custom prompt libraries

   • Context databases

   • Decision frameworks

   • Response templates

   • Interaction guides

3. Implementation:

   • ChatGPT training sets

   • Claude optimization

   • Workflow integration

   • Team onboarding

   • Usage guidelines

4. Ongoing Optimization:

   • Response analysis

   • Context updates

   • Prompt refinement

   • Performance tracking

💰 Revenue Streams

• Primary: Initial context building (€5-15k)

• Secondary: Monthly optimization (€1-3k)

• Additional: Team training

• Custom prompt engineering

🛠️ Bootstrappability Score: 8/10

Start with methodology and basic AI tools. Scale with better automation.

💻 Non-Tech Factor: 7/10

While heavily AI-focused, success depends on human insight and analysis.

🚀 Getting Started

1. Build Methodology:

   • Analysis framework

   • Context gathering

   • AI prompt design

   • Implementation process

2. Tool Stack:

   • Interview templates

   • Analysis frameworks

   • AI platforms

   • Prompt libraries

3. Client Process:

   • Initial assessment

   • Data gathering

   • Context building

   • AI optimization

   • Team training

4. Service Delivery:

   • Custom prompts

   • Usage guidelines

   • Performance metrics

   • Iteration process

5. Scale Strategy:

   • Start with 2-3 clients

   • Document results

   • Build case studies

   • Create training materials

💪 Pros & 😓 Cons

• Pro: Creates ongoing value

• Pro: High barrier to entry

• Con: Requires deep AI expertise

• Con: Complex implementation

🔍 Steal Somebody’s Homework & Dive Deeper

Want to explore this idea further? Check out:

• Rosedale - AI context building

• GPT & Claude projects with context docs

Already building AI context layers? Share your approach - always curious to learn from fellow innovators!

- Slavo