AI only creates value when you can move from an idea to a working product, fast, with guardrails. This episode walks through a compact, real-world build that reveals a repeatable pattern any marketing leader can use to prototype AI-powered experiences without a big team or budget.
- Start with a narrow, human-centered problem and a real local context before you use any AI tools.
- Use one tool for deep research (NotebookLM), another for orchestration and instructions (ChatGPT), and a third for building the working prototype (Replit).
- Turn your research into structured data and written instructions before you generate a line of code.
- Design revenue and contribution models (free, self-serve, paid portals) at the same time you design the product.
- Spin up agents (like a Gobii.ai outreach bot) that support distribution and partnerships, not just content creation.
- Think in terms of reusable workflows: research → spec → prototype → distribution → iteration.
- Use AI to reclaim time, then deliberately reinvest it in learning, relationships, and time outdoors away from screens.
The Reno Live Music Loop: A 6-Step AI Product Workflow
Step 1: Anchor the Use Case in a Specific Human Gap
Before choosing tools, define a concrete, local problem. In my case, it was the lack of a single reliable source for nightly live music in Reno. That specificity drives every decision: what data you need, how the experience should work, and who will pay for it.
Step 2: Use NotebookLM to Build a Focused Research Corpus
NotebookLM becomes your research brain. Feed it targeted queries such as “live music venues in Reno, Nevada,” and refine until you have a high-quality, tool-friendly list of venues and sources. Treat this as your first dataset, not just loose notes.
Step 3: Turn Research into a Structured Asset and Instruction Set
Export the venue list to a Google Doc, then to a PDF so that it can be attached as a reference file. In parallel, prompt ChatGPT to generate detailed instructions for a custom GPT to catalog events. You’re converting messy research into structured data plus a clear operating manual.
Step 4: Build a Custom GPT as Your Domain Specialist
Create a custom GPT model tailored to the domain (e.g., “Reno, Nevada music venues”) and load it with the PDF and instructions. Its job is to understand the geography, event types, and data schema you care about so it can reliably help you architect the next step: the actual app.
Step 5: Use the Custom GPT to Generate a Replit-Ready App Specification
Ask the custom GPT, “As a genius Replit developer, draft a prompt for an app,” with precise requirements: crawl the web, build a daily event calendar, categorize by genre, date, time, venue, and cost, and support both free and fee-based postings. This gives you a robust prompt you can paste directly into Replit’s AI coding assistant.
Step 6: Prototype the Product in Replit and Support It with an Outreach Agent
Drop the generated prompt into Replit to quickly spin up a working multi-tenant site: landing page, submission forms for bands and venues, and a crawler scheduled for daily runs. Then complement the build with a Gobii.ai agent that finds event planners and venue managers, populates a contact sheet, and emails them about the new calendar. You’ve now gone from idea to live prototype plus a basic go-to-market motion.
From Manual Hustle to AI-Augmented Flow: A Practical Comparison
Stage | Traditional Approach | AI-Augmented Workflow Used Here | Strategic Advantage |
|---|---|---|---|
Discovery & Research | Manual Google searches, scattered bookmarks, ad-hoc notes. | NotebookLM organizes sources into a focused corpus and generates tool-friendly lists. | Faster, more complete domain understanding that can be reused across tools. |
Product Spec & Build | Write specs by hand, brief developers, and perform multiple back-and-forth cycles. | Custom GPT converts research into instructions and a Replit-ready prompt; Replit generates code and UI. | Dramatically shorter time-to-prototype and easier iteration for non-technical marketers. |
Distribution & Partnerships | Manually hunt for contacts, build lists in spreadsheets, and send individual outreach. | Gobii.ai agent finds target contacts, fills a sheet, and conducts outreach based on a clear playbook. | Scalable, ongoing partner outreach that runs alongside product development. |
Leadership Takeaways: Turning One Build Into a Repeatable AI Playbook
How should a CMO think about the role of a “custom GPT” in their marketing stack?
Treat custom GPTs as domain specialists that sit between raw models and your business problems. You load them with your research, taxonomies, and guardrails so they can consistently generate briefs, code prompts, messaging, or campaign structures that conform to your standards. Over time, you can maintain a fleet of these specialists—one for events, one for product marketing, one for sales enablement—each tuned to a slice of your GTM motion.
What is the key leadership behavior that makes this kind of workflow possible?
The critical behavior is the willingness to “ship ugly” prototypes quickly. In the Reno example, the goal was not a pixel-perfect site; it was a functioning system that crawls, categorizes, and lets humans submit events. Leaders who insist on polish before proof slow AI learning loops. Leaders who push for working prototypes within days create organizational confidence and uncover real constraints faster.
How can marketing leaders keep AI tools from turning into a fragmented tool zoo?
Define the “highest and best use” of each tool up front and document it in your operating playbook. NotebookLM is for research and corpus building. ChatGPT (and custom GPTs) are for orchestration, instructions, and transformation. Replit is for code and interactive experiences. Gobi is for agents who do outreach and list-building. When every tool has a clear job, teams know where to go for each task and avoid redundant or conflicting workflows.
Where does monetization thinking fit in this kind of AI prototyping?
Revenue design should be baked in from the first prompt. In the Reno project, the plan included: a free portal for bands and musicians to submit events; a paid portal for casinos and venues to promote listings; and a multi-tenant architecture that enables expansion to other cities. When you architect pricing tiers and user roles early, you avoid rework and ensure your AI experiments are tied to real commercial outcomes.
How can leaders keep AI adoption human-centered instead of purely efficiency-driven?
Intentionally earmark reclaimed time. In the episode, I talk about using AI to reclaim time—and then consciously spending that time on upskilling, with family and friends, and in nature. Leaders should mirror that by setting expectations that efficiency gains fund learning budgets, deep work, and authentic human connection, not just more busywork.
Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing
Contact: https://www.linkedin.com/in/b2b-leadgeneration/
Last updated:
- Marketing in the Age of AI podcast – “NotebookLM, Custom GPTs, Replit, and Gobii: My End-to-End Build Workflow” (episode transcript provided).
- Google NotebookLM – used for initial research and corpus creation in this workflow.
- OpenAI ChatGPT – used to generate instructions, custom GPT behavior, and Replit prompts.
- Replit – AI-assisted coding environment used to prototype the live music event platform.
- Gobbi.ai – an agent platform used to automate contact discovery and outreach to event planners and venue managers.
About Strategic eMarketing: Strategic eMarketing helps growth-minded CEOs and marketing leaders design, deploy, and scale measurable AI-enabled marketing systems that drive real pipeline and revenue.
https://strategicemarketing.com/about
https://www.linkedin.com/company/strategic-emarketing
https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai
https://open.spotify.com/show/marketing-in-the-age-of-ai
https://www.youtube.com/@EmanuelRose
Guest Spotlight
Guest: Emanuel Rose
LinkedIn: https://www.linkedin.com/in/b2b-leadgeneration/
Company: StrategyNavigator.ai (founder; focused on helping CEOs, founders, and marketing leaders transform for the AI-accelerated economy).
Podcast episode: Marketing in the Age of AI – “NotebookLM to Replit: How I Prototype AI Products End-to-End.”
About the Host
Emanuel Rose is a senior marketing executive and author who helps CEOs, founders, and marketing leaders transform their organizations for the AI-accelerated economy, blending AI integration, generative strategy, neuromarketing, and story-driven brand development. Connect with him on LinkedIn: https://www.linkedin.com/in/b2b-leadgeneration/
From Concept to Calendar: Your Next 30 Days
Pick one narrow, fundamental problem in your market, then run the exact loop outlined here: research with NotebookLM, structure and instruct with a custom GPT, prototype with Replit, and support distribution with an agent. Commit to shipping a working prototype in 30 days, then measure where it creates or saves the most value—and use those insights to inform your next, bigger AI bet.