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Leveraged Agency Progression Framework

Reusability

A three-phase approach to building AI startups by starting with manual service work, then systematically replacing human labor with AI agents while maintaining service pricing and margins.

How It Works

Phase 1: Manual labor agency (learn edge cases, $1-5K/month). Phase 2: AI agent integration (double margins to 80%, handle 10x clients, $5-25K/month). Phase 3: Multiple tiers with enterprise/self-serve/API offerings ($5M ARR goal).

Components

1

Find extremely boring, painful tasks that everyone needs

2

Start manual agency doing 3-6 months of work yourself

3

Document every edge case and create SOPs

4

Wire AI agents to mirror your SOPs exactly

5

Keep same service pricing (outcome stays same for client)

6

Add enterprise tier, self-serve tier, and API access

7

Reinvest profits into better agents and content distribution

When to Use

When you want to build an AI startup with high probability of success, need to understand domain expertise deeply, or want to be profitable from month one while learning customer needs.

When Not to Use

When you have deep domain expertise already, are building consumer products requiring scale first, or have access to significant capital and can afford long burn periods.

Anti-Patterns to Avoid

Raising pricing when you add AI (clients pay for outcomes, not methods)Skipping the manual phase (you miss critical edge cases)Trying to automate creative or highly variable tasks too earlyBuilding AI-first without understanding the actual workflow

Example

Start a PDF-to-Salesforce data entry service. Spend 4 months manually processing forms for 5 clients at $500/month each. Document every data validation rule and edge case. Build AI agents to replicate your exact process. Keep charging $500/month but now serve 50 clients with 80% margins instead of 40%.

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