July 2025

Status: Beginner

Experimental Project:

MeetingToRoadmap (PartyRock App)

Transforming Chaotic Stakeholder Meetings into Strategic Product Decisions

Try MeetingToRoadmap →

Built on Amazon Bedrock PartyRock - requires AWS account for access

🎯 Problem Statement

Product managers spend countless hours after stakeholder meetings trying to extract actionable insights from messy transcripts and notes. The manual process of:

  • Identifying explicit requests vs. implicit needs

  • Prioritizing features based on business impact

  • Cross-referencing against existing roadmaps

  • Surfacing technical risks and dependencies

...often takes 2-3 hours per meeting and frequently results in missed requirements or misaligned priorities.

The core challenge: How do we accelerate the translation from raw stakeholder input to strategic product decisions?

💡 Solution Approach

I built an AI-powered analysis tool using Amazon Bedrock's PartyRock platform that automatically processes three key inputs:

  1. Meeting transcripts/notes

  2. Current product roadmap

  3. Technical stack specifications

The tool generates structured outputs:

  • Explicit requests and implicit stakeholder needs

  • Prioritized feature backlog with effort estimates

  • Roadmap gap analysis and conflict identification

  • Technical risk assessment and follow-up questions

Technical Implementation

Details

Platform: Amazon Bedrock PartyRock

Approach: Prompt engineering with structured input/output formatting

Key Features:

  • Multi-format input handling (file upload + text)

  • Context-aware analysis using roadmap and tech stack

  • Standardized output format for immediate actionability

Prompt Engineering Strategy

I iteratively refined the prompt to:

  • Specify exact input formats and widget types

  • Ground all analysis in provided context (meeting + roadmap + tech stack)

  • Generate consistent table structures for easy scanning

  • Balance comprehensive analysis with concise, actionable outputs

Results & Validation

Test Scenario: Sales team meeting with 6 stakeholder requests + existing Q2/Q3 roadmap Output Quality:

  • ✅ Captured all explicit requests with supporting quotes

  • ✅ Identified implicit performance and UX needs

  • ✅ Correctly prioritized features based on deal impact

  • ✅ Flagged realistic technical risks for React/Node.js stack

  • ✅ Identified roadmap conflicts (mobile priority mismatch)

Time Savings: Reduces 2-3 hour manual analysis to 5-minute structured output

🔗 Live Demo

Try MeetingToRoadmap →

Built on Amazon Bedrock PartyRock - requires AWS account for access

Future Vision: Multi-App Product Suite

While PartyRock served as an excellent prototyping platform, the real potential lies in orchestrated multi-step workflows:

Planned App Ecosystem:

1. Visual Roadmap Parser

  • Upload roadmap images/PDFs → Extract structured feature data

  • Integration: Feeds into current Meeting Analyzer

2. Competitive Intelligence Analyzer

  • Input: Competitor feature lists, pricing pages

  • Output: Gap analysis vs. your roadmap

3. User Feedback Synthesizer

  • Input: Support tickets, user interviews, NPS comments

  • Output: Prioritized user pain points

4. Technical Feasibility Scorer

  • Input: Feature requirements + current architecture

  • Output: Detailed effort estimates and implementation paths

Technical Implementation for Multi-App Orchestration:

Since PartyRock doesn't support multi-app workflows, I'd build this using:

LangChain/LangGraph Pipeline:

Visual Parser → Feature Extractor → Meeting Analyzer → 
Competitive Intel → Final Roadmap Recommendation

Architecture:

  • Frontend: React dashboard for file uploads and workflow management

  • Backend: Node.js orchestrator calling different LLM models

  • Data Flow: Structured JSON between each processing step

  • Storage: PostgreSQL for feature tracking and historical analysis

🎓 Key Learnings

Prompt Engineering:

  • Specificity in input formats dramatically improves output quality

  • Grounding analysis in multiple contexts (roadmap + tech stack) increases relevance

  • Table structures need explicit formatting instructions for consistency

Platform Limitations:

  • PartyRock excellent for single-step analysis but limited for complex workflows

  • File upload capabilities enable real-world PM workflows

  • Need to balance comprehensive analysis with concise, scannable outputs

Product Insights:

  • Real PM workflows require orchestration between multiple analysis types

  • Context awareness (existing roadmap + tech constraints) is crucial for actionable output

  • Speed vs. depth tradeoff: 5-minute analysis vs. 3-hour manual deep dive

💭 Reflection

This project demonstrated the power of AI for structured analysis while highlighting the need for thoughtful prompt engineering and workflow orchestration. The single-app PartyRock version proves the concept, but the real value lies in building interconnected analysis tools that can handle the full complexity of modern product management workflows