Execution Scorecard

GLP-1 pharmacy index

GLP-1 pharmacy index scores 43/100 for execution readiness. The recommended next step is Within 60 days, build a single-metro crowdsourced stock + cash-price tracker for the four brand GLP-1s and run a paid landing-page test to two buyer segments: a 'find my dose cheapest near me' consumer page, and a B2B page pitching the normalized availability/price API to telehealth and employer-benefits teams. Validate if at least 200 consumer stock reports are submitted in one metro and at least two B2B prospects sign a paid pilot or LOI for the feed.

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Bottlenecks

Pharmacy stock data is not openly published — chains like CVS and Walgreens expose availability only in their own apps, so the index must rely on crowdsourced and scraped signals that can be stale, incomplete, or blocked by terms of service.
Regulatory and liability exposure: showing or routing to GLP-1 sources touches HIPAA, state pharmacy law, drug-advertising rules, and the post-shortage crackdown on compounded GLP-1s — surfacing illegitimate or compounded sources could create legal risk.
Incumbents already occupy the niche: Ro (a well-funded telehealth company) offers a free supply tracker as a funnel, and Medfinder offers a paid concierge stock-finding service, so a standalone index must out-execute on data freshness and the B2B feed.
Demand is shortage-driven and could soften further as supply fully normalizes and GLP-1 pills arrive, compressing the consumer pain point and the window for the data business.
A broad AI assistant can flatten differentiation unless the wedge is painfully specific.
The first release can become a generic dashboard if the job is not named tightly.
Needs real buyer access, not only desk research.

Accelerators

Can talk to the buyer before writing much code.
Can ship a narrow first-win demo quickly.
Can use local-first research artifacts to keep validation moving without a large team.
Use specificity as the wedge: one buyer, one workflow, one measurable result.
Show proof earlier than broad competitors with before-and-after examples and small pilot data.
Keep implementation lighter than incumbent suites or generic AI assistants.
Concierge review or paid template

Dated Plan

First 30 days to evidence.

The plan starts from build time and should be re-exported when the founder chooses a real start date.

2026-07-11

1. Frame the wedge

Write the one-sentence promise and test it in the strongest channel.

Proof: Within 60 days, build a single-metro crowdsourced stock + cash-price tracker for the four brand GLP-1s and run a paid landing-page test to two buyer segments: a 'find my dose cheapest near me' consumer page, and a B2B page pitching the normalized availability/price API to telehealth and employer-benefits teams. Validate if at least 200 consumer stock reports are submitted in one metro and at least two B2B prospects sign a paid pilot or LOI for the feed.

2026-07-14

2. Interview 10 people who match the buyer persona.

Create the lead magnet and use it to recruit interviews.

Proof: Problem resonance: 5+ calls or 10+ detailed replies.

2026-07-18

3. Ship a clickable demo or concierge workflow that produces the first useful artifact.

Build the smallest demo that proves the first win.

Proof: Activation: 25% of demo visitors complete the first-win path.

2026-07-25

4. Run one paid pilot or collect explicit pricing objections before automating the rest.

Delete any report section that feels generic before building.

Proof: Commercial pull: 3 paid pilots, LOIs, or concrete procurement next steps.

2026-08-01

5. Promote to a deeper build plan only after the wedge survives validation.

Run the lead magnet and first-win demo tests.

Proof: Fewer than five qualified buyers agree to discuss the workflow after targeted outreach.

2026-08-10

6. Execution checkpoint 6

Promote to deeper implementation only once the wedge survives interviews or paid-pilot outreach.

Proof: Promote to a deeper build plan only after the wedge survives validation.

First actions

Write the one-sentence promise and test it in the strongest channel.
Create the lead magnet and use it to recruit interviews.
Build the smallest demo that proves the first win.
Delete any report section that feels generic before building.
Run the lead magnet and first-win demo tests.
Promote to deeper implementation only once the wedge survives interviews or paid-pilot outreach.

Builder prompt

Create a dated execution plan for "GLP-1 pharmacy index". Keep the first milestone tied to Within 60 days, build a single-metro crowdsourced stock + cash-price tracker for the four brand GLP-1s and run a paid landing-page test to two buyer segments: a 'find my dose cheapest near me' consumer page, and a B2B page pitching the normalized availability/price API to telehealth and employer-benefits teams. Validate if at least 200 consumer stock reports are submitted in one metro and at least two B2B prospects sign a paid pilot or LOI for the feed.. Use these bottlenecks: Pharmacy stock data is not openly published — chains like CVS and Walgreens expose availability only in their own apps, so the index must rely on crowdsourced and scraped signals that can be stale, incomplete, or blocked by terms of service.; Regulatory and liability exposure: showing or routing to GLP-1 sources touches HIPAA, state pharmacy law, drug-advertising rules, and the post-shortage crackdown on compounded GLP-1s — surfacing illegitimate or compounded sources could create legal risk.; Incumbents already occupy the niche: Ro (a well-funded telehealth company) offers a free supply tracker as a funnel, and Medfinder offers a paid concierge stock-finding service, so a standalone index must out-execute on data freshness and the B2B feed.; Demand is shortage-driven and could soften further as supply fully normalizes and GLP-1 pills arrive, compressing the consumer pain point and the window for the data business.; A broad AI assistant can flatten differentiation unless the wedge is painfully specific.; The first release can become a generic dashboard if the job is not named tightly.; Needs real buyer access, not only desk research.. Use these accelerators: Can talk to the buyer before writing much code.; Can ship a narrow first-win demo quickly.; Can use local-first research artifacts to keep validation moving without a large team.; Use specificity as the wedge: one buyer, one workflow, one measurable result.; Show proof earlier than broad competitors with before-and-after examples and small pilot data.; Keep implementation lighter than incumbent suites or generic AI assistants.; Concierge review or paid template. Link the output to the Idea Builder prompt and do not expand beyond the first validated workflow.