Product Discovery Dossier Record

Template Overview

Jumping straight into building features without validating customer demand is the primary reason software projects fail. The Product Discovery Dossier provides a structured framework to evaluate customer pain points, quantify total addressable market, assess technical architecture feasibility, and define minimum viable scope in plain Markdown.

Markdown Template Structure

Copy the block below or click Download .md File to add this template directly to your personal vault:

---
title: "Discovery Dossier: [Opportunity / Feature Name]"
date: [YYYY-MM-DD]
product_manager: "[[Lead PM Name]]"
tech_lead: "[[Staff Engineer / Tech Lead]]"
opportunity_size: "$1.2M ARR / High Retention"
recommendation: "Proceed to PRD / Pivot / Kill Opportunity"
confidence_score: "85%"
tags: [product-discovery, product-management, opportunity-sizing, prd, strategy]
---

# Product Discovery Dossier Notes — [Opportunity Name]

## 1. The Problem Space & Evidence of Pain
- **The Core User Pain:** Knowledge workers in legal, medical, and defense sectors are strictly banned by corporate compliance from using cloud meeting assistants.
- **Evidence of Pain:** 45 in-depth user interviews revealed that professionals spend 6+ hours weekly manually transcribing audio recordings into Markdown notes.
- **Current Workarounds:** Typing manual notes while listening to recordings at 1.5x speed; using disconnected offline dictation tools that lack meeting-specific formatting.

## 2. Customer Cohorts & Value Proposition
| Cohort | Primary Constraint | What They Crave | Willingness to Adopt |
|---|---|---|---|
| **Corporate Legal Counsel** | Zero data egress permitted | 100% offline local transcription on Mac | Very High ($300+/yr) |
| **Engineering Managers** | High context switching | Clean Markdown notes committed to Git | High ($120/yr) |
| **Medical Researchers** | HIPAA compliance | De-identified clinical summaries | High ($250+/yr) |

## 3. Technical Feasibility & Architectural Risks
- **Hardware Viability:** Evaluated whisper.cpp running on Apple Silicon Neural Engine. 
  - *Result:* M2 base model achieves 18x real-time transcription with under 1.2GB RAM usage.
- **Sandboxing Risk:** Can a macOS app read system audio without kernel extensions?
  - *Mitigation:* Validated using native ScreenCaptureKit audio stream entitlements.

## 4. Minimum Viable Product (MVP) Scope Boundary
### What MUST Be in the MVP
- Local microphone and system audio capture.
- On-device Whisper transcription with zero network calls.
- One-click CommonMark note export with YAML frontmatter.

### What is EXCLUDED from MVP (V2+)
- Automated speaker labeling (requires complex clustering models).
- Multi-language translation (English acoustic models only for v1).
- Cloud backups or web synchronization.

## 5. Strategic Recommendation & Next Gate
> [!IMPORTANT] Recommendation: PROCEED TO FORMAL PRD
> Strong customer validation, clear technical feasibility on Apple Silicon, and an unaddressed market vacuum in zero-cloud transcription justify full engineering allocation.

## 6. Action Items
- [ ] Draft formal PRD and architecture RFC.
- [ ] Build interactive prototype for user feedback testing.

Filled Real-World Example

Here is a realistic example demonstrating how this template captures actual deliberations:

---
title: "Discovery: Local Voice Vault"
date: 2026-10-06
product_manager: "[[Elena Rostova]]"
tech_lead: "[[Devin Zhao]]"
recommendation: "Proceed to PRD"
confidence_score: "90%"
tags: [discovery, voice-vault, strategy]
---

# Product Discovery Dossier Notes — Local Voice Vault

## 1. Executive Summary
Elena Rostova and Devin Zhao completed a 3-week discovery sprint exploring on-device voice transcription for privacy-focused knowledge workers.

## 2. Findings
Validated that users will happily pay a premium for software that guarantees absolute data sovereignty. Technical feasibility confirmed on M-series chips with zero CPU throttling.

When to Use and When to Avoid

When to Use

• Use during the earliest discovery phases of new product ideas or major feature epics.
• Use to decide whether an opportunity warrants engineering allocation or should be killed.
• Use to align design, product, and engineering around verified customer evidence.

When to Avoid

• Avoid using for routine minor bug fixes or small UI styling adjustments.
• Avoid using when engineering execution has already begun.

On-Device AI Integration

Summarize your recording in Oyma with this local instruction:

Analyze the customer research notes and market opportunity data:
1. Synthesize the validated customer pain points and current user workarounds.
2. Evaluate technical feasibility risks and define a tight MVP scope boundary.
3. Generate a structured Markdown Product Discovery Dossier with clear go/no-go recommendation.

Your data remains securely on your Mac with zero cloud exposure.