Validate the problem before you build the solution.
A PM toolkit for AI-powered problem validation. Give it a problem statement, it does the research, surfaces what's actually known, and generates a structured, evidence-backed problem brief — before you commit to building anything.
Built in October 2025. MVP works end-to-end. Paused — may become part of a larger PM specialist toolkit.
Most teams commit to building something before they've truly validated the problem. ProblemExplorer flips that — AI does the research first, so you're working from evidence, not assumptions.
make problemexplorer PROBLEM="Managing AI-first teams is challenging"What it does:
- Takes your problem statement
- Generates research queries automatically
- Executes searches and synthesizes findings
- Validates evidence quality
- Outputs a structured, research-backed problem statement document
Why this matters for PMs: A problem statement you can defend — with sources, evidence dimensions, and quality validation — is worth 10 built-on-assumptions ones.
| Tool | Status | Description |
|---|---|---|
| ProblemExplorer | ✅ MVP complete | AI-research-first problem validation |
| ProblemSpace v1 | 📦 Archived | First attempt — solved the wrong problem |
On ProblemSpace v1: The first version asked users to provide evidence and had AI format it. User testing revealed the flaw immediately — people haven't done the research yet, that's the whole point. "People are lazy — AI should do the work." That insight became ProblemExplorer. ProblemSpace v1 is kept as a learning artifact.
Prerequisites: Python 3.9+, Amplifier (github.com/microsoft/amplifier)
git clone https://github.com/cpark4x/visioncaster
cd visioncaster
pip install -e amplifier/.Note: ProblemExplorer MVP uses mock search data. Production use requires a real web search integration (SerpAPI or similar).
- MVP = working end-to-end — mock data is fine, complete workflow matters
- Test the assumption, not the polish — v1 looked good and solved the wrong problem entirely
- User insight beats architecture — one round of testing revealed more than weeks of design
- Code for structure, AI for intelligence — reliable pipeline + intelligent AI > trying to make AI reliable
Working MVP. Paused. Will return when integrating into a broader PM specialist toolkit — likely alongside Outcomist and canvas-specialists research pipeline.
Chris Park — Senior PM, Microsoft Office of the CTO, AI Incubation group. Engineering degree from Waterloo. 17 years shipping product.