# Passport.Me Investor Pack

## Quick Links
- Memo draft: [/Users/heatherm/Documents/Codex/PPME/strategy/07_investor_memo_draft.md](/Users/heatherm/Documents/Codex/PPME/strategy/07_investor_memo_draft.md)
- Pitch outline: [/Users/heatherm/Documents/Codex/PPME/strategy/02_pitch_deck_outline_10_slides.md](/Users/heatherm/Documents/Codex/PPME/strategy/02_pitch_deck_outline_10_slides.md)
- Financial tabs folder: [/Users/heatherm/Documents/Codex/PPME/strategy/financial_model_tabs](/Users/heatherm/Documents/Codex/PPME/strategy/financial_model_tabs)

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## 1) Executive Summary
Passport.Me is building personal context infrastructure for AI. The product connects permissioned personal data sources, normalizes them into a portable MCP profile, and delivers high-value AI skill outputs across major models. Every context pull is measured via MDT with transparent usage receipts.

Core thesis:
- AI quality is now constrained by context quality.
- Users need trusted, portable, measurable access to personal context.
- Passport.Me is the context layer that combines AI utility with blockchain-grade accountability.

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## 2) The Problem
Current AI workflows break in three ways:
1. Context friction: users manually copy information from inboxes, banking apps, calendars, and notes.
2. Trust gap: users can’t verify what personal data was read or how much was consumed.
3. Portability failure: each AI app requires separate integrations and settings.

Result: low trust, low repeat usage for high-value tasks, and poor operational reliability.

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## 3) The Solution
Passport.Me provides:
- Connector layer: email, banking, calendar, fitness, browsing, messages.
- Permission ledger: scoped, revocable access.
- MCP profile: portable personal context endpoint.
- Skill execution layer: repeatable, output-focused workflows.
- MDT metering: pre-run estimates and post-run receipts.

Product loop:
`Connect -> Authorize -> Query -> Output -> Receipt`

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## 4) Product “Wow”
Users experience value quickly by running signature skills:
- Expense Report Generator
- Subscription Optimizer
- Weekly Brief Composer
- Tax Packet Builder

Outputs are concrete artifacts (CSV, summaries, checklists, ranked recommendations), not vague chat responses.

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## 5) Why Now
- MCP-style model connectivity is becoming mainstream.
- AI users are increasingly multi-model.
- Users need governance and proof for personal data usage.
- Token rails can meter and settle usage in a transparent way.

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## 6) Business Model
Revenue stack:
1. Subscriptions (Pro, Team, Enterprise)
2. MDT usage/top-ups
3. Enterprise/API contracts

Commercial design principle:
- Charge for measurable context value, not opaque AI seat inflation.

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## 7) MDT Utility Model
MDT is consumed for:
- Context pulls
- Skill executions
- Premium connector sync cycles

User-facing transparency:
- Before run: estimated MDT
- During run: consumption tracking
- After run: receipt with source + scope + cost

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## 8) GTM Plan
Phase 1: design partners (high-frequency operators)
- Validate 2–3 signature skills with quantified ROI.

Phase 2: product-led loop
- Fast onboarding to first output.
- Shareable artifacts for organic growth.

Phase 3: ecosystem expansion
- API/SDK for AI apps needing permissioned user context.
- Enterprise governance layer.

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## 9) 10-Slide Storyline (Investor Deck)
1. Title: Passport.Me as context infrastructure
2. Problem: context friction + trust gap
3. Solution: permissioned MCP + MDT
4. Product demo: first-value workflow
5. Why now: standards + behavior shifts
6. Market: B2C wedge, B2B expansion
7. Business model: subscription + usage + API
8. Token utility: measurable, transparent, auditable
9. Traction + roadmap
10. Ask + use of funds

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## 10) Financial Snapshot (Base Case)
Assumptions:
- Pro subscribers: 2,500
- Team seats: 1,800
- Avg skills per Pro user/month: 40
- Avg skills per Team seat/month: 55
- Avg MDT per run: 2.2

Outputs (monthly):
- Subscription MRR: $94,200
- Top-up revenue: $12,250
- Total MRR: $106,450
- Total run volume: 199,000
- Total MDT consumed: 437,800
- Gross margin (illustrative): 85.26%

Model files:
- assumptions.csv
- usage_and_revenue.csv
- costs_and_margin.csv
- scenarios.csv
- kpi_dashboard.csv

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## 11) KPI Framework
Core KPIs:
- Time to first value (minutes)
- Weekly skills per active user
- MDT per successful outcome
- 30-day retention

Commercial KPIs:
- Top-up conversion
- Revenue per run
- Gross margin by cohort
- NRR (for teams/enterprise)

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## 12) Risks and Mitigations
1. Data sensitivity risk
- Mitigation: least privilege scopes + revocation + receipts.

2. Platform dependency risk
- Mitigation: multi-model support and MCP portability.

3. Token UX complexity risk
- Mitigation: fiat-first UX with visible MDT accounting.

4. Adoption risk
- Mitigation: signature skills with hard ROI proof.

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## 13) Fundraise Narrative (Template)
Ask:
- Capital to expand connectors, skill quality, trust infrastructure, and GTM.

Use of funds:
- 45% product + connector reliability
- 25% trust/governance + security
- 20% GTM + partnerships
- 10% operations + compliance

Milestone outcomes:
- 2x active skill runs/user
- <6 min time to first value
- 70%+ successful skill outcome rate
- proven top-up and margin profile

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## 14) Appendix: Suggested Next Deliverables
1. Investor one-pager (PDF)
2. 10-slide deck content draft (speaker notes included)
3. Live financial model in Google Sheets with scenario selector
4. 3 quantified case studies (hours saved, cost saved, MDT spent)