AI Product Assistant - Augmenting PM Workflows

The Hypothesis

Product managers spend 60% of their time on repetitive tasks that could be augmented by AI:

Synthesizing user research
Writing documentation
Analyzing competitive landscape
Creating status updates
Prioritizing backlogs

What if AI could handle the heavy lifting, letting PMs focus on strategy and creativity?

Current Experiments

1. Research Synthesis

The assistant can process hours of user interviews and automatically:

Extract key themes and pain points
Identify patterns across interviews
Generate insight summaries
Create persona descriptions

[!success] Early Results Reduced research synthesis time from 10 hours to 3 hours for a typical project

2. PRD Generation

Feed the assistant:

User research notes
Business requirements
Technical constraints

It generates:

Complete PRD first draft
User stories with acceptance criteria
Edge cases and error states
Success metrics

3. Competitive Intelligence

The assistant continuously:

Monitors competitor product updates
Analyzes feature gaps
Tracks pricing changes
Summarizes market movements

Technical Approach

Architecture

User Input → LLM Processing → Context Retrieval → Output Generation
     ↓              ↓                ↓                    ↓
  Voice/Text    Fine-tuned      Vector DB          Structured
   Interface      Models         Search            Documents

Key Technologies

LLMs: GPT-4 for generation, Claude for analysis
Vector Search: Pinecone for semantic search
Workflow: LangChain for chaining operations
Interface: Next.js for web app

Challenges & Learnings

What's Working

✅ Summarization and synthesis tasks
✅ First draft generation
✅ Pattern recognition across data
✅ Structured data extraction

What's Hard

❌ Understanding nuanced business context
❌ Making strategic decisions
❌ Handling ambiguity
❌ Creative problem solving

Future Roadmap

Phase 1: Research Assistant (Current)

User interview synthesis
Survey analysis
Sentiment extraction

Phase 2: Documentation Helper

PRD generation
User story creation
Release notes

Phase 3: Strategic Advisor

Market analysis
Prioritization recommendations
Risk assessment

Phase 4: Integrated Platform

Slack/Jira integration
Real-time collaboration
Custom fine-tuning per organization

Ethical Considerations

Building AI for product management raises important questions:

Bias: How do we ensure AI doesn't perpetuate biases in product decisions?
Transparency: Teams need to understand AI recommendations
Human-in-the-loop: AI augments, never replaces, human judgment
Data Privacy: Handling sensitive user research data

Get Involved

I'm looking for PMs interested in:

Testing prototypes
Sharing workflow pain points
Contributing training data
Defining ethical guidelines

This is an exploration at the intersection of AI and product management. The goal isn't to replace PMs but to give them superpowers.

Join the research →

Technologies