ORIGIN POINT
Hackathon-winning AI concept
Senior Product Manager, AI Platform Strategy
Enterprise AI / Emerging Platform Work • 2024–2026
From AI experiments to platform foundations
This work started with a customer-facing AI prototype, then grew into a broader point of view on how AI adoption really scales inside an organization.
CUSTOMER SIGNAL
87% would use again
STRATEGIC SHIFT
From isolated wins to reusable AI thinking
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Where this started
A prototype that proved customers cared
During a company hackathon, I led a team to design and build a functional AI prototype for Guitar Center customers.
The concept addressed a real product discovery problem and was envisioned as an experience that could support customers across ecommerce, in-store, and contact center touchpoints.
In a follow-up UX study, 87% of participants said they would use it again.
That result mattered more than the award. It showed that AI could create real customer value quickly when grounded in a clear use case.
Hackathon winner
The original hackathon photo anchors the story in the real moment the concept gained internal traction.
Functional custom GPT prototype
A product visual of the experience that made the functional prototype tangible beyond the original pitch.
What changed
Validation was the beginning, not the finish line
The prototype created momentum. It also clarified something bigger:
Early customer validation is not the same thing as scalable adoption.
Turning promising AI ideas into durable value requires more than a strong concept. It requires ownership, workflow fit, reusable patterns, and structural support.
This experience also reinforced how important focused go-to-market strategy is for AI products. Early launches are often strongest when centered on one high-value use case, paired with a refined interaction model and a clear adoption path.
That became the bridge from experimentation to platform thinking.
Supporting artifact
Sound Seeker live demo
A public-facing rebuild of the hackathon concept, included here as a working artifact inside the broader platform story.
To make the hackathon concept more tangible, I rebuilt the original idea as a public-facing prototype. The demo shows how an AI assistant can help musicians move from a tonal reference to a concrete rig and signal chain, while making the interaction pattern visible to others exploring what these systems could become.
In this context, the demo serves as a working artifact of the concept that helped spark broader thinking around platform potential.
This is a public-safe rebuild of the interaction pattern, not the original internal implementation.
Try one of these prompts
Platform lens
The ideas that shaped my approach
Platform focus
Reusable AI systems
I became focused on what should be reusable across use cases, not just what worked once.
Adoption pattern
Workflow-based adoption
The best AI opportunities fit naturally into how customers and employees already move through work.
Execution style
Platform-led iteration
Start with a real problem, validate quickly, identify reusable patterns, and use them to shape a stronger foundation.
Customer validation
Users wanted it back
A follow-up UX study showed strong reuse intent and positive sentiment, reinforcing that the concept solved a meaningful customer problem.
UX validation results
The strongest signal was not just positive sentiment, but clear evidence that musicians wanted to use the experience again.
Omnichannel concept
How the experience could extend across channels
Based on the validated prototype, I explored how a unified AI experience could extend across Guitar Center touchpoints.
These visuals represent conceptual experience designs grounded in the prototype’s behavior and capabilities. They show product direction and experience exploration, not final production implementation.
Conceptual omnichannel extension
Conceptual experience designs grounded in the prototype's behavior and capabilities, not final production implementation.
Concept: Ecommerce
A conversational layer embedded into the digital journey to help customers find the right products more naturally.
Concept: In-store
An assisted experience that could help store teams guide customers with more consistency and speed.
Concept: Contact center
An AI-supported interaction model to help agents understand customer intent and respond with tailored guidance.
System design
One recommendation engine, multiple touchpoints
The prototype validated value in a single interface. From there, I explored how the same AI layer could support product discovery across ecommerce, in-store, and contact center workflows.
01
I want to recreate the sound of [song, artist, or genre]. What do I need?
shared engine
02
Sound Synthesist GPT
Underlying recommendation engine
powers
01
Ecommerce experience
Customers can discover gear recommendations inside the digital shopping journey.
02
In-store experience
Store teams can guide customers with a shared recommendation experience.
03
Contact center workflow
AI available to agents
Conversation context stored in Salesforce
outputs
03
- Gear recommendations
- Setup techniques
- Known gear when available
I want to recreate the sound of [song, artist, or genre]. What do I need?
Sound Synthesist GPT
Underlying recommendation engine
Outputs
- Gear recommendations
- Setup techniques
- Known gear when available
powers
01
Ecommerce experience
Customers can discover gear recommendations inside the digital shopping journey.
02
In-store experience
Store teams can guide customers with a shared recommendation experience.
03
Contact center workflow
AI available to agents
Conversation context stored in Salesforce
Influence
What the work helped shape
The concept generated strong internal interest and contributed to broader thinking around AI-enabled customer experiences.
While I did not own the final implementation path, the work sharpened my view of what it takes to move from early momentum to durable organizational value.
Key learnings
What stayed with me
- Real customer value can be validated early
- Fast validation does not guarantee scale
- AI is strongest when tied to real workflows
- Go-to-market focus matters as much as model capability
- Reusable patterns matter more than one-off novelty
- Strong ideas need the right system around them to last
Why it matters
The foundation of my AI platform thinking
This experience shaped how I approach AI product work today: solve a real problem, validate quickly, and design the surrounding system so early value can become something repeatable and durable.
Recommendations
How collaborators described this work
Two adjacent perspectives that reinforce the same pattern: translating early AI momentum into practical systems, stronger guardrails, and credible cross-functional execution.
LinkedIn
Daniel has been a tremendous partner across the many different initiatives we've worked together on.
Sean described Daniel as an insightful, collaborative problem solver who was at the forefront of Guitar Center's early AI efforts while helping craft processes that enabled business efficiency and kept data protected.
Sean Richardson
Information Security Manager
The Guitar Center Company
Direct
Turned ChatGPT integrations and guardrails into clear product strategy.
Technical partner who called out executive alignment and translation of complex AI ideas into practical direction.
Daniel Das
Senior Software Engineer
AI Platform / Enterprise Systems