# Execution Scorecard: Grammarly for lawsuits

Score: 44/100

Tier: Research first

Grammarly for lawsuits scores 44/100 for execution readiness. The recommended next step is Run a landing page for 'attorney-quality demand letters, citation-verified, $25' targeting small-business owners with unpaid invoices via search ads on 'how to collect unpaid invoice / demand letter' keywords; measure email signups and pre-orders, then hand-fulfill the first 20 letters manually (concierge MVP) to confirm willingness to pay and intake feasibility before building automation.

## Bottlenecks
- Unauthorized practice of law (UPL) exposure: drafting filings and flagging legal sufficiency can be construed as legal advice, creating bar-regulatory and liability risk that varies by state.
- Citation hallucination / accuracy liability: a single fabricated citation can get a user sanctioned, so the verification layer must be near-perfect or the product actively harms its buyer and reputation.
- Incumbents and free public tools: court self-help portals, Prosei AI, and broad assistants like CoCounsel/Clio compete, and courts themselves are launching free guided chatbots (e.g. NDNY 'Pro Se Pal').
- Low/episodic purchase frequency for individual litigants makes CAC payback hard; most users have one dispute and churn, pushing the model toward SMB/legal-aid recurring buyers.
- 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 Launch Plan
- **2026-06-25 / Frame the wedge**: Write the one-sentence promise and test it in the strongest channel. Proof: Run a landing page for 'attorney-quality demand letters, citation-verified, $25' targeting small-business owners with unpaid invoices via search ads on 'how to collect unpaid invoice / demand letter' keywords; measure email signups and pre-orders, then hand-fulfill the first 20 letters manually (concierge MVP) to confirm willingness to pay and intake feasibility before building automation.
- **2026-06-28 / 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.
- **2026-07-02 / 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.
- **2026-07-09 / 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.
- **2026-07-16 / 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.
- **2026-07-25 / 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.

## Builder Prompt
Create a dated execution plan for "Grammarly for lawsuits". Keep the first milestone tied to Run a landing page for 'attorney-quality demand letters, citation-verified, $25' targeting small-business owners with unpaid invoices via search ads on 'how to collect unpaid invoice / demand letter' keywords; measure email signups and pre-orders, then hand-fulfill the first 20 letters manually (concierge MVP) to confirm willingness to pay and intake feasibility before building automation.. Use these bottlenecks: Unauthorized practice of law (UPL) exposure: drafting filings and flagging legal sufficiency can be construed as legal advice, creating bar-regulatory and liability risk that varies by state.; Citation hallucination / accuracy liability: a single fabricated citation can get a user sanctioned, so the verification layer must be near-perfect or the product actively harms its buyer and reputation.; Incumbents and free public tools: court self-help portals, Prosei AI, and broad assistants like CoCounsel/Clio compete, and courts themselves are launching free guided chatbots (e.g. NDNY 'Pro Se Pal').; Low/episodic purchase frequency for individual litigants makes CAC payback hard; most users have one dispute and churn, pushing the model toward SMB/legal-aid recurring buyers.; 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.