Mini Podcasts PRD

Status: Draft
Author: Brainforge Product Team
Created: January 21, 2026
Last Updated: January 21, 2026

1. TL;DR

Mini Podcasts transforms uploaded documents (articles, research, notes) into personalized audio content that business teams can consume on-the-go. The feature addresses information overload by converting written knowledge into 3-15 minute audio summaries tailored to the listener’s role and interests. Expected outcome: 40%+ reduction in time-to-insight for busy executives, sales teams, and ops leaders.

2. Summary

Mini Podcasts is an internal Brainforge feature that enables business teams to convert written content into audio format. Users upload documents (PDFs, articles, notes), and the system generates structured audio summaries using AI-powered text processing and text-to-speech synthesis.

The feature includes a personalization engine that adapts content depth, focus areas, and follow-up recommendations based on the user’s role (executive, sales, ops) and declared interests. This transforms passive document repositories into active, accessible knowledge streams.

3. Background and Context

The Information Overload Problem

Business teams are drowning in written content:

Industry reports and market research
Internal meeting notes and strategy documents
Competitive intelligence and news articles
Training materials and best practices

Current state: Documents pile up unread in shared drives. Teams lack time to consume critical information, leading to:

Duplicated research efforts
Missed strategic insights
Knowledge silos between departments
Decision-making without full context

Why Audio?

Multitasking-friendly: Listen during commutes, workouts, or between meetings
Higher completion rates: Audio content sees 2-3x completion vs. long-form text
Accessibility: Supports different learning preferences and visual impairments
Reduced friction: No need to block dedicated reading time

Why Personalization?

A sales leader needs different insights from the same market research than a product manager. Generic summaries waste time on irrelevant details. Role-based personalization ensures each listener gets the signal without the noise.

4. Problem and Value

Quantified Pain Points

Pain Point	Current Impact	Source
Time spent reading reports	5-10 hours/week for knowledge workers	Industry benchmarks
Document backlog	60%+ of shared documents never opened	Internal assumption
Context switching cost	23 minutes to refocus after reading interruption	UC Irvine research
Knowledge sharing gaps	Teams re-research topics already documented	Stakeholder feedback

Cost of Inaction

Productivity loss: Hours spent on manual document review that could be passive listening
Competitive disadvantage: Slower time-to-insight vs. teams with better knowledge systems
Employee frustration: Information exists but is inaccessible in usable format

Stakeholder Value

Stakeholder	Value Delivered
Executives	Consume briefings during commute; 3-minute summaries of 30-page reports
Sales Teams	Stay current on competitive intel without blocking calendar time
Ops Leaders	Absorb process documentation and best practices passively
All Users	Personalized follow-up recommendations surface relevant content proactively

5. Goals and Non-Goals

Goals

Enable passive knowledge consumption — Convert documents to audio format consumable during otherwise unproductive time
Reduce time-to-insight — Deliver key takeaways in 3-15 minutes vs. 30-60 minute reads
Personalize by role — Adapt content focus, depth, and language to user’s function
Drive content discovery — Surface relevant follow-up content based on listening history and interests
Integrate with Brainforge ecosystem — Leverage existing auth, storage, and AI infrastructure

Non-Goals

Live podcast production — Not creating real-time audio streams or live recordings
Multi-voice conversations — V1 uses single narrator; dialogue format is future scope
External publishing — Podcasts are internal consumption only; no RSS feeds or public distribution
Full audiobook creation — Focus on summaries and key insights, not verbatim document narration
User-generated audio — Users don’t record their own content; system generates all audio
Mobile app — Web-first; native mobile apps are out of scope for V1

6. Staged Milestones

POC (Proof of Concept)

Goal: Validate that AI summarization + TTS produces listenable, valuable audio from documents.

Scope:

Single document upload (PDF or plain text)
Basic summarization via GPT-4o (fixed prompt, no personalization)
Audio generation via OpenAI TTS or ElevenLabs
Simple web player (play/pause/seek)
Internal team testing only

Success Criteria:

Audio quality rated ≥4/5 by 5+ internal testers
Summary accuracy rated ≥4/5 (captures key points without hallucination)
End-to-end processing time ≤3 minutes for 10-page document

Timeline: [TBD, needs technical review] — Estimated 2-3 weeks

MVP (Minimum Viable Product)

Goal: Deliver end-to-end functionality with basic personalization for pilot users.

Scope:

Multi-format document upload (PDF, DOCX, TXT, Markdown)
Role-based summarization (Executive, Sales, Ops presets)
Configurable podcast length (3/5/10/15 minutes)
Audio library with playback history
Basic recommendations (“Related podcasts”)
Integration with Brainforge auth and user profiles

Not in MVP:

Interest tagging beyond role
Custom voice selection
Batch document processing
Sharing/collaboration features
Analytics dashboard

Success Criteria:

20+ pilot users onboarded
50%+ weekly active usage among pilot group
Average satisfaction score ≥4/5
Processing reliability ≥95% (successful audio generation)

Timeline: [TBD, needs technical review] — Estimated 4-6 weeks post-POC

V1 (Production Release)

Goal: Full-featured release with advanced personalization and analytics.

Scope (additions to MVP):

Interest tagging system (user-defined topics of interest)
Smart recommendations based on listening history
Multiple voice options (professional, conversational, etc.)
Batch upload and queue management
Share podcast links with teammates
Usage analytics dashboard (listens, completion rates, popular topics)
Feedback mechanism (rate podcasts, flag issues)
Speed control (0.5x - 2x playback)
Download for offline listening

Success Criteria:

80%+ user retention month-over-month
Average podcast completion rate ≥70%
Time saved per user ≥2 hours/week (self-reported)
NPS ≥50 among active users

Timeline: [TBD, needs technical review] — Estimated 6-8 weeks post-MVP

7. Users and Use Cases

Primary Users

User Type	Description	Primary Need
Executive	C-suite, VPs, Directors	Quick briefings on strategic documents; high-level summaries
Sales Professional	AEs, BDRs, Sales Managers	Competitive intel, market research, product updates
Operations Leader	Ops Managers, Team Leads	Process documentation, best practices, training materials

User Flow

1. User logs into Brainforge
       ↓
2. Navigates to Mini Podcasts section
       ↓
3. Uploads document (drag-drop or file picker)
       ↓
4. Selects role preset (Executive/Sales/Ops) and target length
       ↓
5. System processes document (loading indicator with ETA)
       ↓
6. Audio player appears with generated podcast
       ↓
7. User listens (can adjust speed, skip, seek)
       ↓
8. After completion, system suggests related content
       ↓
9. Podcast saved to user's library for replay

Key Use Cases

UC-1: Executive Briefing

Sarah, a VP of Strategy, receives a 40-page industry report. She uploads it to Mini Podcasts, selects “Executive” preset and “5 minutes.” During her morning commute, she listens to a focused summary covering market trends, competitive threats, and strategic implications. She arrives at the office already briefed.

UC-2: Sales Enablement

Marcus, an Account Executive, needs to prep for a prospect call. He uploads the prospect’s recent earnings report and selects “Sales” preset. The podcast highlights financial performance, stated priorities, and potential pain points—exactly what Marcus needs for his discovery call.

UC-3: Ops Knowledge Transfer

Priya, a new Ops Manager, has a backlog of process documentation to review. She uploads the team’s SOP documents in batch and listens to them during her first week. The summaries help her understand workflows without blocking hours for reading.

UC-4: Personalized Follow-Up

After listening to several podcasts about AI trends, the system recognizes Alex’s interest and proactively surfaces a new research paper on the topic. Alex didn’t know the document existed but now has it in their queue.

8. Functional Requirements

8.1 Document Ingestion

Requirement	Description	Priority
FR-1.1	Support PDF upload (up to 50MB)	P0
FR-1.2	Support DOCX, TXT, MD upload	P0
FR-1.3	Extract text content preserving structure (headings, lists)	P0
FR-1.4	Handle scanned PDFs via OCR	P1
FR-1.5	Support URL input for web articles	P1
FR-1.6	Batch upload (up to 10 documents)	P2

8.2 Content Processing

Requirement	Description	Priority
FR-2.1	Summarize document to target length (3/5/10/15 min)	P0
FR-2.2	Apply role-based focus (Executive/Sales/Ops)	P0
FR-2.3	Structure summary with intro, key points, conclusion	P0
FR-2.4	Preserve factual accuracy (no hallucination)	P0
FR-2.5	Generate natural spoken-word script (not written prose)	P0
FR-2.6	Apply user’s interest tags to prioritize relevant sections	P1

8.3 Audio Generation

Requirement	Description	Priority
FR-3.1	Generate audio from summarized script	P0
FR-3.2	Produce clear, natural-sounding speech	P0
FR-3.3	Support multiple voice options	P1
FR-3.4	Generate audio within 3 minutes for typical document	P0
FR-3.5	Store generated audio securely	P0

8.4 Playback Interface

Requirement	Description	Priority
FR-4.1	Web-based audio player with play/pause	P0
FR-4.2	Seek/scrub functionality	P0
FR-4.3	Playback speed control (0.5x - 2x)	P1
FR-4.4	Progress tracking (resume where left off)	P0
FR-4.5	Download audio file for offline listening	P2

8.5 Library & History

Requirement	Description	Priority
FR-5.1	Store all generated podcasts in user library	P0
FR-5.2	Display listening history with progress	P0
FR-5.3	Search library by title/topic	P1
FR-5.4	Delete podcasts from library	P1

8.6 Personalization

Requirement	Description	Priority
FR-6.1	Store user role preference	P0
FR-6.2	Allow user to set interest tags	P1
FR-6.3	Recommend related podcasts based on history	P1
FR-6.4	Learn from listening patterns (implicit personalization)	P2

8.7 Feedback & Quality

Requirement	Description	Priority
FR-7.1	Rate podcast quality (1-5 stars)	P1
FR-7.2	Flag issues (inaccurate, unclear, etc.)	P1
FR-7.3	Collect feedback for model improvement	P2

9. Technical Approach

9.1 Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                        Brainforge Platform                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌──────────┐    ┌──────────────┐    ┌──────────────────┐       │
│  │  Upload  │───▶│   Document   │───▶│  Summarization   │       │
│  │   UI     │    │  Processor   │    │     Agent        │       │
│  └──────────┘    └──────────────┘    └────────┬─────────┘       │
│                                               │                  │
│                                               ▼                  │
│  ┌──────────┐    ┌──────────────┐    ┌──────────────────┐       │
│  │  Audio   │◀───│    Audio     │◀───│   Script         │       │
│  │  Player  │    │   Storage    │    │   Generator      │       │
│  └──────────┘    └──────────────┘    └────────┬─────────┘       │
│                                               │                  │
│                                               ▼                  │
│                                      ┌──────────────────┐       │
│                                      │   TTS Service    │       │
│                                      │  (OpenAI/11Labs) │       │
│                                      └──────────────────┘       │
│                                                                  │
│  ┌─────────────────────────────────────────────────────────┐    │
│  │              Personalization Engine                      │    │
│  │  ┌─────────┐  ┌─────────────┐  ┌───────────────────┐    │    │
│  │  │  Role   │  │  Interest   │  │  Recommendation   │    │    │
│  │  │ Profiles│  │   Tags      │  │     Engine        │    │    │
│  │  └─────────┘  └─────────────┘  └───────────────────┘    │    │
│  └─────────────────────────────────────────────────────────┘    │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
              ┌───────────────────────────────┐
              │         Supabase              │
              │  ┌─────────┐  ┌───────────┐   │
              │  │ Postgres│  │  Storage  │   │
              │  │  (meta) │  │  (audio)  │   │
              │  └─────────┘  └───────────┘   │
              └───────────────────────────────┘

9.2 Key Components

Component	Technology	Notes
Frontend	Next.js + React	Existing Brainforge stack
Document Processor	Node.js service	PDF.js for PDFs, Mammoth for DOCX
Summarization Agent	Mastra + GPT-4o	Leverage existing Azure OpenAI setup
Script Generator	GPT-4o	Converts summary to spoken-word script
TTS Service	OpenAI TTS / ElevenLabs	[Recommended, pending technical review]
Audio Storage	Supabase Storage	Existing infrastructure
Metadata DB	Supabase Postgres	Existing infrastructure
Audio Player	Custom React component	HTML5 Audio API
Personalization	Postgres + application logic	Role profiles, interest tags, history

9.3 Data Model

-- Users already exist in Brainforge auth
 
-- User preferences for Mini Podcasts
CREATE TABLE podcast_user_preferences (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  user_id UUID REFERENCES auth.users(id),
  default_role TEXT CHECK (default_role IN ('executive', 'sales', 'ops')),
  default_length_minutes INT DEFAULT 5,
  interest_tags TEXT[], -- Array of topic tags
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
);
 
-- Uploaded documents
CREATE TABLE podcast_documents (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  user_id UUID REFERENCES auth.users(id),
  filename TEXT NOT NULL,
  file_path TEXT NOT NULL, -- Supabase storage path
  file_type TEXT NOT NULL,
  file_size_bytes INT,
  extracted_text TEXT,
  word_count INT,
  status TEXT DEFAULT 'pending', -- pending, processing, ready, failed
  created_at TIMESTAMPTZ DEFAULT NOW()
);
 
-- Generated podcasts
CREATE TABLE podcasts (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  document_id UUID REFERENCES podcast_documents(id),
  user_id UUID REFERENCES auth.users(id),
  title TEXT NOT NULL,
  summary_text TEXT, -- The generated script
  audio_path TEXT NOT NULL, -- Supabase storage path
  duration_seconds INT,
  role_preset TEXT,
  target_length_minutes INT,
  voice_id TEXT,
  status TEXT DEFAULT 'generating', -- generating, ready, failed
  created_at TIMESTAMPTZ DEFAULT NOW()
);
 
-- Listening history
CREATE TABLE podcast_listen_history (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  podcast_id UUID REFERENCES podcasts(id),
  user_id UUID REFERENCES auth.users(id),
  progress_seconds INT DEFAULT 0,
  completed BOOLEAN DEFAULT FALSE,
  last_listened_at TIMESTAMPTZ DEFAULT NOW()
);
 
-- Ratings and feedback
CREATE TABLE podcast_feedback (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  podcast_id UUID REFERENCES podcasts(id),
  user_id UUID REFERENCES auth.users(id),
  rating INT CHECK (rating BETWEEN 1 AND 5),
  feedback_text TEXT,
  issue_flags TEXT[], -- 'inaccurate', 'unclear', 'too_long', etc.
  created_at TIMESTAMPTZ DEFAULT NOW()
);
 
-- Indexes for common queries
CREATE INDEX idx_podcasts_user ON podcasts(user_id);
CREATE INDEX idx_listen_history_user ON podcast_listen_history(user_id);
CREATE INDEX idx_documents_user ON podcast_documents(user_id);

9.4 API Endpoints

Method	Endpoint	Purpose
POST	`/api/podcasts/upload`	Upload document for processing
GET	`/api/podcasts`	List user’s podcast library
GET	`/api/podcasts/:id`	Get podcast details and audio URL
POST	`/api/podcasts/:id/progress`	Update listening progress
POST	`/api/podcasts/:id/feedback`	Submit rating/feedback
GET	`/api/podcasts/recommendations`	Get personalized recommendations
GET	`/api/podcasts/preferences`	Get user preferences
PUT	`/api/podcasts/preferences`	Update user preferences
DELETE	`/api/podcasts/:id`	Delete podcast from library

9.5 Processing Pipeline

Upload & Extract
- User uploads document via UI
- Document stored in Supabase Storage
- Background job extracts text (PDF.js / Mammoth)
- Text stored in podcast_documents.extracted_text
Summarize
- Mastra agent receives extracted text + user preferences
- Applies role-based prompt template
- Generates structured summary targeting specified length
- Returns summary optimized for spoken delivery
Script Generation
- Second LLM pass converts summary to natural speech script
- Adds transitions, emphasis markers, pacing cues
- Output is TTS-ready text
Audio Generation
- Script sent to TTS service (OpenAI TTS or ElevenLabs)
- Audio file returned and stored in Supabase Storage
- Metadata updated with duration, status = ‘ready’
Delivery
- User notified podcast is ready (UI update or notification)
- Audio streamed via signed URL from Supabase Storage
- Progress tracked as user listens

10. Assumptions

#	Assumption	Risk if Wrong
A1	Users have reliable internet for streaming audio	Offline mode becomes P0; need download feature earlier
A2	GPT-4o produces accurate summaries without hallucination	Quality issues erode trust; need human review layer
A3	TTS quality is sufficient for professional use	May need premium voice provider; cost implications
A4	3-15 minute summaries satisfy user needs	Mismatch with actual document complexity; need variable length logic
A5	Role-based presets (3 options) cover primary use cases	May need more granular personalization; custom presets
A6	Users will provide feedback to improve recommendations	Cold-start problem; need fallback recommendation logic
A7	Existing Supabase infrastructure handles audio storage load	May need CDN or dedicated media storage at scale

11. Open Questions

#	Question	Owner	Needed By	Status
Q1	Which TTS provider offers best quality/cost ratio for our volume?	Engineering	POC Start	Open
Q2	What is acceptable latency for document-to-audio processing?	Product	POC Start	Open
Q3	Should we support URL input for web articles in MVP?	Product	MVP Planning	Open
Q4	How do we handle documents with images/charts/tables?	Engineering	MVP Planning	Open
Q5	What are the copyright/licensing implications for uploaded content?	Legal	MVP Launch	Open
Q6	Do we need SOC 2 considerations for audio storage?	Security	V1 Planning	Open
Q7	How should we handle multi-language documents?	Product	V1 Planning	Open

12. Tradeoffs

T1: Single Voice vs. Multi-Voice/Dialogue

Option	Pros	Cons	Effort
Single narrator (Chosen)	Simpler pipeline, faster processing, lower cost	Less engaging than dialogue format	Low
Multi-voice dialogue	More engaging, podcast-like feel	Complex script generation, 2x+ TTS cost, harder to coordinate	High

Rationale: Start simple. Single voice delivers core value. Multi-voice can be V2 enhancement if user feedback demands it.

T2: OpenAI TTS vs. ElevenLabs

Option	Pros	Cons	Effort
OpenAI TTS (Recommended)	Native integration, simpler auth, competitive quality	Fewer voice options	Low
ElevenLabs	Premium voice quality, voice cloning potential	Additional vendor, higher cost, separate API	Medium

Rationale: Start with OpenAI TTS for simplicity. Evaluate ElevenLabs if voice quality feedback is negative.

T3: Real-time vs. Background Processing

Option	Pros	Cons	Effort
Background processing (Chosen)	Better UX (no waiting), handles long documents	User waits for notification, more infrastructure	Medium
Real-time streaming	Instant feedback, progressive delivery	Timeout issues, poor UX for long docs	High

Rationale: Background processing with progress indication provides best UX for variable document lengths.

13. Success Metrics

Metric	Target	How Measured
Adoption	50% of eligible users try feature within 30 days	Unique users who generate ≥1 podcast
Retention	40% weekly active users among adopters	Users who listen to ≥1 podcast per week
Completion Rate	≥70% average podcast completion	Progress tracking (listened to ≥90% of duration)
Quality Rating	≥4.0 average rating	User feedback submissions
Time Saved	≥2 hours/week per active user	Self-reported survey
Processing Reliability	≥98% successful generation	Failed jobs / total jobs
Processing Speed	≤3 minutes for standard document	Timestamp: upload to ready
NPS	≥50 among active users	Quarterly NPS survey

14. Dependencies

Technical Dependencies

Dependency	Description	Owner	Status
Supabase Storage	Audio file storage	Platform Team	Available
Azure OpenAI	GPT-4o for summarization	Platform Team	Available
Mastra Framework	Agent orchestration	Platform Team	Available
TTS Service	OpenAI TTS or ElevenLabs	[TBD]	Evaluation needed

External Dependencies

Dependency	Description	Risk
TTS API availability	Service uptime for audio generation	Medium - have fallback provider
OpenAI API limits	Rate limits on GPT-4o calls	Low - existing enterprise agreement

Team Dependencies

Dependency	Description	Owner
UI/UX design for player	Audio player and library interface	Design Team
Security review	Data handling and storage review	Security Team
Legal review	Content licensing implications	Legal Team

15. Risks and Mitigations

Risk	Likelihood	Impact	Mitigation
Summary hallucinations	Medium	High	Implement accuracy scoring; user feedback loop; human spot-checks
TTS quality dissatisfaction	Medium	Medium	Evaluate multiple providers; allow voice selection; gather early feedback
Processing delays at scale	Medium	Medium	Queue management; parallel processing; user expectations setting
Low adoption	Medium	High	Prominent placement in UI; onboarding tutorial; showcase value early
Copyright concerns	Low	High	Terms of service update; user content responsibility; legal review
Cost overruns (TTS)	Medium	Medium	Monitor usage; implement quotas; evaluate pricing tiers
Audio storage costs	Low	Low	Retention policies; user quotas; compression

16. Timeline Summary

Phase	Scope	Duration	Status
POC	Single doc upload, basic summarization, TTS proof	2-3 weeks	Not Started
MVP	Multi-format upload, role presets, library, basic recs	4-6 weeks	Not Started
V1	Interest tags, advanced recs, analytics, sharing	6-8 weeks	Not Started

Note: Timelines are estimates pending technical review and resource allocation.

17. References

Brainforge Platform AGENTS.md — platform technical overview (this monorepo)
PRD standards — see standards/04-prompts/prd/ in the playbook repo (not duplicated here)
Mastra Documentation - AI agent framework
OpenAI TTS API - Text-to-speech reference
ElevenLabs API - Alternative TTS provider

Appendix A: Role-Based Prompt Templates

Executive Preset

Focus: Strategic implications, key decisions, bottom-line impact
Tone: Concise, authoritative, action-oriented
Structure: 
  - 30 seconds: Context and why this matters
  - 2-3 minutes: Top 3-5 strategic insights
  - 30 seconds: Recommended actions or decisions
Exclude: Technical details, methodology, supporting data

Sales Preset

Focus: Customer implications, competitive positioning, objection handling
Tone: Conversational, practical, outcome-focused
Structure:
  - 30 seconds: What this means for our customers/prospects
  - 2-3 minutes: Key talking points and proof points
  - 30 seconds: How to use this in conversations
Exclude: Internal processes, technical architecture

Ops Preset

Focus: Process implications, implementation details, operational impact
Tone: Clear, methodical, practical
Structure:
  - 30 seconds: Overview and relevance to operations
  - 3-5 minutes: Step-by-step breakdown of key processes/changes
  - 30 seconds: Action items and next steps
Include: Specific procedures, timelines, dependencies

Appendix B: Sample User Interface Wireframe

┌─────────────────────────────────────────────────────────────────┐
│  Mini Podcasts                                    [+ New]       │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌─────────────────────────────────────────────────────────┐    │
│  │  Now Playing: Q4 Market Analysis                         │    │
│  │  ━━━━━━━━━━━━━━━━━━━━━━○─────────────────  3:24 / 5:00  │    │
│  │              ◀◀    ▶    ▶▶         🔊  1.0x             │    │
│  └─────────────────────────────────────────────────────────┘    │
│                                                                  │
│  Your Library                                                    │
│  ┌──────────────────────────────────────────────────────────┐   │
│  │ 📄 Q4 Market Analysis          5:00  ●● Listening        │   │
│  │ 📄 Competitor Teardown         8:23  ○○ Not started      │   │
│  │ 📄 Process Documentation      12:45  ●○ 50% complete     │   │
│  │ 📄 Weekly Briefing             3:15  ●● Completed        │   │
│  └──────────────────────────────────────────────────────────┘   │
│                                                                  │
│  Recommended for You                                             │
│  ┌──────────────────────────────────────────────────────────┐   │
│  │ 📄 AI Trends Report 2026       7:00  Based on interests  │   │
│  │ 📄 Sales Playbook Update       4:30  New this week       │   │
│  └──────────────────────────────────────────────────────────┘   │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

End of PRD

Brainforge Knowledge

Explorer

mini-podcasts-prd

Mini Podcasts PRD

1. TL;DR

2. Summary

3. Background and Context

The Information Overload Problem

Why Audio?

Why Personalization?

4. Problem and Value

Quantified Pain Points

Cost of Inaction

Stakeholder Value

5. Goals and Non-Goals

Goals

Non-Goals

6. Staged Milestones

POC (Proof of Concept)

MVP (Minimum Viable Product)

V1 (Production Release)

7. Users and Use Cases

Primary Users

User Flow

Key Use Cases

8. Functional Requirements

8.1 Document Ingestion

8.2 Content Processing

8.3 Audio Generation

8.4 Playback Interface

8.5 Library & History

8.6 Personalization

8.7 Feedback & Quality

9. Technical Approach

9.1 Architecture Overview

9.2 Key Components

9.3 Data Model

9.4 API Endpoints

9.5 Processing Pipeline

10. Assumptions

11. Open Questions

12. Tradeoffs

T1: Single Voice vs. Multi-Voice/Dialogue

T2: OpenAI TTS vs. ElevenLabs

T3: Real-time vs. Background Processing

13. Success Metrics

14. Dependencies

Technical Dependencies

External Dependencies

Team Dependencies

15. Risks and Mitigations

16. Timeline Summary

17. References

Appendix A: Role-Based Prompt Templates

Executive Preset

Sales Preset

Ops Preset

Appendix B: Sample User Interface Wireframe

Graph View

Table of Contents