# Passport.Me Investor Memo (Draft)

## Company
- Name: Passport.Me
- Category: Personal Context Infrastructure for AI
- Tagline: Permissioned personal data to model-ready context, powered by MDT.

## Executive Summary
Passport.Me is building the context infrastructure layer that makes AI outputs personally useful, verifiable, and portable across models. Today, users rely on generic AI without trusted access to their real personal data (email receipts, banking transactions, schedules, message context, and activity data). Passport.Me solves this by converting user-authorized data scopes into a portable MCP profile that can connect to multiple AI models. Every context pull is metered via MDT and represented with auditable usage receipts.

The result is a product that combines immediate productivity gains (reports, summaries, planning artifacts) with a blockchain-native trust and metering layer. We monetize through subscriptions, MDT usage/top-ups, and enterprise/API plans.

## Problem
AI quality now depends less on model intelligence and more on context quality. Users face three persistent issues:
1. Context friction: manually moving personal data into AI prompts.
2. Trust gap: uncertainty about what data was accessed and when.
3. Portability failure: re-integrating data for each AI product and model.

These failures are costly in high-frequency workflows like expense reporting, weekly planning, subscription management, tax prep, and travel coordination.

## Solution
Passport.Me provides a personal context control plane:
- Data connectors: email, banking, calendar, fitness, messages, browsing.
- Permission ledger: explicit scopes, revocable access, source-level controls.
- MCP context layer: normalized, model-ready personal context.
- Skill execution: repeatable workflows with structured outputs.
- MDT metering: per-run context cost estimation, consumption, and receipt.

## Product Experience
A new user can get first value in one session:
1. Connect 1–2 sources.
2. Authorize minimal scopes.
3. Run a signature skill (e.g., Expense Report Generator).
4. Receive structured output plus MDT consumption and usage receipt.

### Signature outcomes
- Expense report with weekly totals, missing-field flags, CSV export.
- Subscription overlap detection with estimated monthly savings.
- Manager-ready weekly brief with blockers and owner mapping.
- Tax packet draft with deduction categories and document gaps.

## Why Passport.Me Wins
### Differentiation
- Cross-model portability via one MCP profile.
- Permission + receipt trust architecture.
- Usage economics tied to measurable context value.

### Moat
- Connector depth + normalization pipelines.
- Skill library tuned to high-value personal workflows.
- Behavioral and economic data from MDT usage loops.

## Market
### Beachhead
- Individual power users: freelancers, operators, creators.
- Small teams using AI for finance/operations planning.

### Expansion
- API/SDK for AI applications needing permissioned user context.
- Enterprise controls (policy, audit, residency options).

## Business Model
1. Subscription
- Pro and Team plans for core access + included MDT.

2. Usage
- MDT consumed by context pulls and skill executions.
- Top-up model for high-intensity users.

3. Enterprise/API
- Platform fees for app builders and org deployments.

## Token Utility (MDT)
MDT is not a speculative add-on; it is the utility rail for context usage.
- Pre-run estimate: user sees expected MDT before execution.
- Post-run receipt: user sees exact MDT consumed with context details.
- Monthly value statement: MDT spent vs outcomes delivered.

This ties spending to measurable value and increases trust for both users and integrators.

## GTM Plan
### Phase 1: Design Partner Loop
- 20–30 design partners in finance/productivity-heavy roles.
- Focus on 2–3 skills with strongest time-saved ROI.

### Phase 2: Product-Led Expansion
- Viral artifacts: shareable reports/briefs/checklists.
- Fast onboarding to first successful run.

### Phase 3: Platform Distribution
- Integrate with AI apps via MCP profile connectivity.
- Expand connector + skill ecosystem.

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

Commercial:
- Subscription MRR
- Top-up conversion rate
- Gross margin
- Revenue per run

## Risks and Mitigations
1. Data sensitivity risk
- Mitigation: least-privilege scopes, revocation defaults, clear receipts.

2. Model/platform dependency risk
- Mitigation: multi-model support and MCP portability.

3. Adoption complexity risk
- Mitigation: signature skills, prebuilt templates, short onboarding paths.

4. Token UX confusion risk
- Mitigation: fiat + MDT dual display, pre-run estimates, monthly summaries.

## 12-Month Roadmap
- Q1: harden core connectors, improve signature skill quality, expand receipts.
- Q2: team controls, policy templates, deeper financial workflows.
- Q3: API/SDK release for third-party AI apps.
- Q4: enterprise governance and marketplace-style skill distribution.

## Fundraise
### Ask
- Raise objective: growth + platform hardening.
- Focus areas:
  - Connector coverage and reliability
  - Skill quality and outcome consistency
  - Trust infrastructure and governance controls
  - GTM engine for high-value user segments

### Outcome Target
Build Passport.Me into the default context layer for personal AI workflows, where trust, portability, and usage economics are first-class.