BMAD-METHOD/bmad/bmm/agents/hand-off/PRD.md

PRD: AI Journaling → LinkedIn Posts (Mobile-first MVP)

Version: 2025-10-07 Author: Business Analyst (Mary)

Purpose

Provide a clear product requirements document for an AI-driven mobile-first journaling app that helps professionals capture text and voice entries, transcribe audio, and convert entries into LinkedIn-ready posts using OpenAI. The product is privacy-first (local-first default), integrates with Whisper for transcription and LinkedIn for publishing, and is designed to be built by a single operator with $5k for API/infra costs over a 3-month MVP timeline.

Scope

MVP includes:

• Local-first journaling (text), tags, search, offline support
• Audio recording and Whisper transcription (basic pipeline)
• OpenAI-driven post generation with 3 tone presets (professional, thought‑leadership, casual)
• Draft editor with redaction/anonymize toggle
• LinkedIn OAuth + publish endpoint; fallback share flow for demos
• Basic analytics: entries, conversions, publish counts
• Privacy & retention settings (default: 90 days) and consent logs

Out of scope (MVP):

• Full cloud sync by default (opt-in only)
• Advanced analytics/engagement metrics
• Scheduled posting, multi-language support

Success metrics (for MVP validation)

• Product usage: 100 active users generating 1–3 posts/week within 1 month of launch
• Cost control: Average generation + transcription cost ≤ $0.50 per active user per week
• Retention: At least 25% of users use the app weekly after 30 days
• Time to post: From entry capture to publish ≤ 5 minutes (happy path)
• Privacy: No raw audio retained server-side beyond TTL; users can export and delete data

Personas

Primary

• Senior Product Manager at a mid-size SaaS company. Busy, wants to turn daily learnings into LinkedIn posts to build credibility.

Secondary

• Individual contributors (engineers/designers) who want to document wins and publicize insights.
• Consultants/freelancers who want to showcase work and build networks.

User Journeys

1. Quick Text Capture

• User opens app → taps + → writes a short entry → tags → saves locally.
• User taps Convert → selects tone → app generates variants → user edits → Publish (LinkedIn or fallback).

2. Voice Capture → Transcribe → Convert

• User records audio (20–60s) → uploads via signed URL → Whisper transcribes → client displays transcript → user edits/anonymizes → Convert → Publish.

3. Search & Reuse

• User searches by tag/date → selects past entry → converts/adapts → publish or schedule.

4. Privacy Flow

• Default: everything stored locally. For cloud features, user explicitly opts-in and gives consent. Processing to OpenAI/Whisper occurs only after user confirms.

Functional Requirements

• FR1: Create/Edit/Delete journal entries locally (offline-first). (MUST)
• FR2: Tagging and full-text search across entries. (MUST)
• FR3: Audio recording with playback & file persist. (MUST)
• FR4: Signed upload flow for audio and serverless transcription job (Whisper). (MUST)
• FR5: OpenAI proxy endpoint to generate 1–3 post variants with hashtags and CTA. (MUST)
• FR6: Draft editor with inline edit and redaction, anonymize toggle. (MUST)
• FR7: LinkedIn OAuth + UGC publish endpoint + fallback share flow. (MUST)
• FR8: Settings for retention (default=90 days), anonymize behavior, telemetry opt-in. (MUST)
• FR9: Processing logs (transcribe/generate/publish) and consent records. (MUST)

Non-functional Requirements

• NFR1: End-to-end TLS for all network traffic. (MUST)
• NFR2: Raw audio deleted from server storage within TTL ≤ 1 hour. (MUST)
• NFR3: At-rest encryption for optional cloud sync. (MUST if cloud sync enabled)
• NFR4: Scalable serverless functions with rate-limiting and quota enforcement. (MUST)
• NFR5: Token and cost monitoring with alerts (monthly & per-day). (MUST)
• NFR6: Client works offline; queue jobs for transcription when online. (MUST)

Data model (simplified)

• Entry
  • id, user_id, text, tags[], created_at, updated_at, status (draft/published), audio_ref (optional), transcription_id
• Transcript
  • id, entry_id, text, language, confidence, created_at
• Variant
  • id, entry_id, text, hashtags[], tone, created_at
• PublishEvent
  • id, entry_id, variant_id, provider, provider_id, status, timestamp
• ConsentLog
  • id, user_id, action (transcribe/generate/publish), timestamp, consent_given

API summary (serverless)

Refer to SERVERLESS-API-SPEC.md for endpoints. Key endpoints:

• /api/signed-upload (POST)
• /api/transcribe/start (POST)
• /api/transcribe/callback (internal)
• /api/generate-post (POST)
• /api/publish-linkedin (POST)
• /api/usage (GET)

Privacy & Legal

• Store minimal personal data and only with explicit consent for cloud sync or publish actions.
• Provide export (JSON/MD) and account delete routes. Ensure backups respect retention rules.
• Provide clear privacy policy in app and during onboarding.

Constraints & Assumptions

• Single operator builds/operates app; $5k budget for APIs & hosting. Use low-cost models and limit variants to control spending.
• LinkedIn API access may require app review; implement fallback share flow for MVP.

Timeline & Milestones

• M0: Prepare dev accounts, serverless skeleton (1 week)
• M1: Core journaling + audio + signed upload (weeks 1–4)
• M2: Transcription + generation + LinkedIn integration (weeks 5–8)
• M3: Privacy, retention, testing & store prep (weeks 9–12)

Acceptance Criteria (overall)

• All P0 FRs implemented and demoable in staging with sample LinkedIn publish (or fallback).
• Processing logs and retention settings work as specified.
• Cost per active user remains within estimated budget after 30 days of sample usage.

Open questions

• Monetization specifics for paid tiers and pricing (deferred until validation).
• Full cloud sync encryption approach for production-grade privacy (need architect input).

---

Document created for handoff and PRD review. Update owners and timelines as needed.