Case Study
Sound Synthesist AI Gear Recommender for Musicians
An AI assistant that helps musicians jump from “I love that sound” to a concrete rig: gear, signal chain, and starter settings. Born in Guitar Center’s first ChatGPT Hackathon, later rebuilt as an independent demo using public data and OpenAI APIs.
Prototype Velocity
4 hours
From maybe too crazy idea to the first working hackathon demo.
Portfolio Rebuild
1 hour
PM-led text-based web demo built via Codex + ChatGPT no dedicated dev time.
Would Use Again
87%
Musicians in usability testing who said they would use Sound Synthesist again.
Working text-based demo embedded below. Rebuilt independently using public data and OpenAI APIs; no proprietary assets.
Hackathon Outcome
1st place — Guitar Center ChatGPT Hackathon
Judged by the C-suite, positioning Sound Synthesist as a flagship AI concept.
Organizational Impact
Inspired Rig Advisor
Sparked the internal Rig Advisor project, later announced publicly by Guitar Centers CEO.
AI Leadership
ChatGPT Champions
Helped establish the ChatGPT Champions group and positioned Daniel as a key AI adoption lead.
The Challenge
Musicians burn hours chasing tones across videos and forum threads. We wanted a faster path from “I love that sound” to “here is how to set up my rig” — without handing creativity to a black box.
Approach
- Ranked hackathon ideas by feasibility vs. impact; Sound Synthesist landed in the "low feasibility / high impact" quadrant and was initially deprioritized.
- After shipping two safer ideas, Daniel tested the concept in GPT-4.1 and found it already suggested believable gear chains.
- Using Codex + ChatGPT, he built a one-hour text demo in a Next.js portfolio app with guided follow-ups (live vs. studio, pickups, DAW vs. amp, budget).
How Sound Synthesist Works
1 · Prompt Input
Musicians describe a tone, song, artist, or vibe — for example, "How do I sound like Olafur Arnalds?"
2 · AI Recommendation
Sound Synthesist suggests amps, pedals, mics, plugins, and starter settings.
3 · Smart Follow-ups
Clarifying questions about context, rig, and budget refine the rig to each player.
4 · Education Layer
Each answer explains why the choices work so players learn sound design, not just settings.
Technology
- Models: GPT-4.1 and GPT-5 with grounded web search for up-to-date gear info.
- Frontend: Next.js, TypeScript, Tailwind, deployed on Vercel.
- Build style: PM-led build using Codex + ChatGPT; text-only experience focused on guidance quality rather than audio rendering.
Learnings
- Big ideas are not always hard: "Low feasibility / high impact" labels disappear once you prototype with AI.
- PMs can ship real demos: AI pair-programming lets Product Managers build and test concepts before engineering commits.
- Explainability wins trust: musicians prefer transparent, explainable help over opaque magic answers.
Strengths
- Guided gear discovery: Moves musicians from vague tonal goals to concrete rigs.
- Built-in teaching: Every recommendation includes a quick "why this works" explanation.
- AI-accelerated prototyping: Shows how AI tools let a PM deliver polished prototypes quickly.
Opportunities
- Personalization: Use each player's rig, genre, and budget to tailor rigs.
- Workflow integration: Bring the assistant into DAWs or plugins so it lives where people create.
- Commerce links: Connect rigs to "buy this gear" and "find an alternative" options.
Whats Next
- Harden the public demo with guardrails, rate limiting, and curated prompts.
- Add presets and good / better / best rigs at different price points.
- Explore DAW or plugin-style integrations and share the best prompt patterns.
Sources
- Public gear specs, manufacturer documentation, and reputable online sources.
- Feedback from working musicians to sanity-check tone suggestions.
- No proprietary Guitar Center data — only public information and OpenAI models.
Summary
Sound Synthesist went from a deprioritized hackathon idea to a 1st-place prototype that helped shift Guitar Center’s AI strategy. The independent demo on this page shows how curiosity plus AI tools let a Product Manager ship meaningful, musician-facing prototypes fast.