In this live workshop session from the AI for Business event, Brian walks through two very different ways to build with Lovable: the fast, simple path for a basic app, and his own advanced workflow for building complex, feature-rich tools. He opens with a quick example, rebuilding the home staging app from an earlier session in about 30 seconds and a couple of dollars, before shifting into his real focus: using Claude, specifically Opus 4.6 Extended, to plan and architect a much more complex app before ever touching Lovable itself.

Brian demonstrates his full process live: briefing Claude on Lovable's documentation, describing a real business problem (generating leads for an AI agency), having Claude research competitors automatically, and generating a step-by-step prompt sequence he pastes into Lovable one at a time. He also shows off some of his own advanced builds, including a full custom content management system built entirely inside Lovable, and introduces Push10, his own business selling ready-made app and website templates.

Timeline Summary

[0:01] Recreating the home staging app from an earlier session in under 30 seconds

[1:47] Publishing an app live and connecting a custom domain

[3:19] Why Brian prefers doing complex builds outside Lovable's own chat interface

[6:08] Introducing the advanced method: using Claude instead of Lovable's built-in AI

[7:02] Briefing Claude on Lovable Cloud and Lovable AI documentation before starting

[8:56] Describing a real business problem: generating leads for an AI agency

[10:28] Having Claude master-plan the idea and suggest several app concepts

[11:34] Explaining Opus 4.6 Extended and why "extended" reasoning produces better output

[13:57] Having Claude research competitors automatically before finalizing the app

[14:38] The personal touch: pushing Claude with "is that the best you can do?"

[15:43] Why picking one audience and one problem keeps a build manageable

[16:22] Generating a knowledge file and step-by-step prompts to paste into Lovable

[18:12] Introducing Push10, Brian's business selling app and website templates

[19:37] Touring brianhanson.com, a website built entirely inside Lovable

[20:32] Building a full custom content management system inside Lovable, without WordPress

[23:34] The difference between Lovable AI and Lovable Cloud

[24:19] Showing an AI business readiness assessment tool built as a lead magnet

[26:59] Walking through the knowledge file and prompt-pasting workflow step by step

[29:56] The completed build and post-build checklist

[30:36] Why some issues get fixed just by describing the problem in plain English

5 Key Takeaways

1. Match the Build Method to the Complexity — A simple, single-purpose app can be built directly inside Lovable in seconds for a few dollars. A complex, feature-rich app benefits from planning first with Claude before ever opening Lovable.
2. Brief the AI on Its Own Documentation First — Before describing what you want to build, feeding Claude the actual Lovable Cloud and Lovable AI documentation ensures its understanding is current, since AI models are only trained up to a certain date.
3. Let AI Research Your Competition Automatically — Turning on web search and asking Claude to review what similar apps are doing well or poorly adds real market awareness to a build before a single prompt gets sent to Lovable.
4. Complexity Multiplies Cost and Time — Every added feature makes an app harder to build and more expensive to run. Picking one audience and one problem to solve keeps a build fast, cheap, and actually finishable.
5. You Don't Need to Know How to Code to Fix Bugs — When something breaks, simply describing the problem in plain English to the AI is often enough for it to diagnose and fix the issue itself.

Enjoyed This Episode?

If seeing two completely different build speeds side by side clarified when to keep it simple and when to plan first, try applying that same judgment to your next build: start simple, and only bring in a planning step like this once the complexity actually calls for it. Share this episode with someone who's been overcomplicating their first AI app build, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live demo session from the AI for Business event, Brian builds a complete, working app from a single plain-English sentence, with no coding, no tricks, and no prior Lovable experience assumed. He starts by walking through his own free resource, a 40-plus chapter prompt book and playbook available at Brian's Gift, before diving into a real-time build of a home staging app that lets real estate agents upload a photo of an empty room and generate a fully staged version for their listings.

Brian treats this as a deliberately bare-bones demonstration: no advanced prompting techniques, just describing the idea in plain language and answering a handful of follow-up questions Lovable asks automatically. He also shows off the design-inspiration tools in his stack, Dribbble for style ideas and GoFullPage for capturing entire websites in one screenshot, and highlights that both Gemini and ChatGPT are already built into Lovable at no extra cost, giving access to Google's Nano Banana image generation without needing a separate API.

Timeline Summary

[0:01] Brian introduces his newly updated prompt book with over 40 chapters

[0:49] Where to find it: Brian's Gift, plus a Claude referral link and a Lovable credit bonus link

[1:29] Introducing the app builder playbook and the exact build system Brian uses

[1:52] The core toolkit: Lovable, Claude, a free GitHub account, the playbook, and a knowledge file

[2:17] Dribbble as a free design inspiration site you can feed directly into Lovable

[2:55] GoFullPage, a free Chrome extension for capturing an entire website in one screenshot

[3:16] The first prompt in the playbook, ready to copy and paste to get started

[3:51] Starting the live demo with the simplest possible approach: plain English, no tricks

[4:13] Confirming Gemini and ChatGPT are built into Lovable at no extra cost, unlocking Nano Banana image generation

[4:54] Introducing the example: an AI home staging app for real estate listings

[5:14] Why staging is a strong example: it costs thousands of dollars and many AI alternatives charge per photo

[5:51] Writing the very first prompt live, describing the staging app in plain language

[7:42] Lovable's automatic follow-up questions: room types, number of design themes, and visual style

[9:22] Choosing to require sign-in and submitting the final build request

[9:43] What to do if something doesn't work right away: just tell it and let it fix itself

[10:29] Watching Lovable generate the build plan and approving it live

[11:18] Lovable automatically enabling cloud and database features without manual setup

[11:40] Brian's personal take on Google's long-term position in the AI race

[12:19] Why the Nano Banana image generator was the turning point for Google's user growth

5 Key Takeaways

1. You Can Build a Working App From One Plain Sentence — Brian's entire opening prompt for the staging app was a single, plainly worded description of the idea. No technical language, no advanced prompting technique, just a clear explanation of what the app should do.
2. Let the Platform Ask the Follow-Up Questions — After a simple first prompt, Lovable automatically asks the clarifying questions needed to complete the build, like room types and visual style, removing the need to think through every detail up front.
3. Built-In AI Models Remove a Whole Setup Step — Because ChatGPT and Gemini are already integrated into Lovable, there's no API to configure or account to connect, which also unlocks Google's Nano Banana image generation at no extra cost.
4. Bugs Are Normal, and the Fix Is Simple — When a built app doesn't work as expected, the fix is often as simple as telling the AI exactly what's wrong and letting it correct itself, rather than treating it as a sign something went irreparably wrong.
5. Design Inspiration Doesn't Have to Come From Your Own Niche — Sites like Dribbble let you pull in a look and feel from a completely unrelated industry and have Lovable build toward that aesthetic, rather than starting from a blank page.

Enjoyed This Episode?

If watching a working app get built from a single sentence made this feel a lot less intimidating than you thought, that's the point, just describe the idea in plain English and let the platform guide you the rest of the way. Share this episode with someone who's been putting off building their first app because they think they need to learn to code first, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live demo session from the AI for Business event, the same team member behind Revven pulls back the curtain on two much bigger builds: Core Real Elite, an autonomous real estate investing platform, and Homes Daily, a consumer-facing home-selling platform positioned as a direct challenger to sites like Zillow. Both were built primarily using Lovable, stitched together with outside data partnerships for the pieces Lovable's built-in AI can't reach on its own.

Core Real Elite is designed to work as a fully autonomous acquisitions system: an "AI Scan" feature that uses Google Street View imagery to spot distressed properties at scale, cross-referenced against public records for foreclosure, probate, and tax delinquency signals, then automatically matched against a database of active buyers, sending offers, contracts, and DocuSign signatures without a human needing to touch any of it unless they choose to step in. Homes Daily flips the same buyer-matching engine around for homeowners, showing sellers exactly which buyers in the system are ready to close on their specific property.

The back half of the session gets into the harder, less glamorous parts of building something like this: the real cost of licensing raw data instead of a cheaper API, the legal and compliance guardrails needed to avoid AI practicing real estate without a license, and a monetization strategy built around letting established trainers and influencers white-label the platform to their own audiences instead of selling it directly. The speaker also shares a comparison that sums up the whole session: a tool that took another developer three years and six figures to build was recreated as a working first version in just four weeks.

Timeline Summary

[0:01] Introducing Core Real Elite and the vision of wiring separate real estate tools into one autonomous system

[0:47] The Intel report feature: finding top zip codes by cash buyer activity and launching campaigns instantly

[1:46] The search and buy box feature, similar to Zillow or Redfin but built for investors

[2:03] AI Scan explained: using Google Street View to spot distressed properties in minutes

[2:49] Cross-referencing property condition with public records for foreclosure, probate, and tax data

[4:10] Automatically matching a distressed property against active buyers already in the system

[4:29] The fully autonomous pipeline: scanning listings, scraping Facebook and Craigslist, and making offers automatically

[5:29] Built-in DocuSign, contract tracking, and automatically notifying title companies and lenders

[6:30] Marketing an accepted deal to matched buyers and moving a showing through to close

[7:14] Additional tools in the system: an acquisitions agent, deal calculators, and market analysis

[7:42] Introducing Homes Daily, the consumer-facing platform for matching sellers directly to buyers

[9:08] Why building this without traditional coding knowledge still produced something industry-disrupting

[9:51] Housing every seller communication, across call, text, and email, in one unified hub

[10:11] The AI call-listening and real-time coaching feature for sales reps

[11:11] The white-label monetization strategy: letting trainers and influencers sell it to their own list

[12:16] A look at the deal analysis tool and its automatic offer and creative finance suggestions

[13:19] Why raw data costs multiple six figures a year, and why an API wasn't a viable option here

[14:31] BatchLeads as a more affordable API option for simpler builds

[14:56] The honest reality of protecting an idea in an age where AI can rebuild almost anything

[15:40] Positioning Homes Daily against Zillow, and catching an overly bold AI-suggested headline

[16:48] The compliance guardrails: disclosures, an attorney review, and not letting AI give licensed advice

[18:06] Monetizing through paid agent placements while still serving for-sale-by-owner sellers directly

[19:41] Confirming the data partnership was finalized that same day, unlocking the next build phase

[20:44] Why Lovable alone can't access proprietary data like Zillow's or the MLS without a separate API

[22:36] Introducing Ava, the AI assistant built contextually into every page of the platform

[23:21] Personal builds for fun: an app for a son's Minecraft interest and a princess dress-up app for a daughter

[24:22] The comparison that sums it all up: three years and six figures versus a four week MVP

5 Key Takeaways

1. Autonomous Doesn't Mean Hands Off Forever — Every stage of the system, from scanning for leads to sending offers to closing deals, can be set to fully automated or fully manual, letting the user choose exactly how involved they want to be at each step.
2. Data Costs More Than the Build Itself — For simple apps, tools like ChatGPT or Gemini's built-in capabilities are enough. The moment you need proprietary data, like MLS or Zillow-level information, you're looking at a real data licensing cost that can run into six figures a year.
3. Compliance Has to Be Built In From the Start — Anything that touches real estate advice needs deliberate guardrails to avoid AI practicing without a license, plus clear disclosures and legal review, especially when the tool is designed to directly compete with large, protected platforms.
4. You Don't Have to Sell Your Product to Profit From It — Rather than selling a finished platform outright, letting established trainers or influencers white-label it to their own audience for a revenue share can create profit far faster than direct sales ever would.
5. Speed Is the Real Disruption — A tool that took a traditional developer three years and six figures to build was recreated as a working first version in about four weeks using AI-assisted development, a gap in speed that's reshaping what "moving fast" actually means in this industry.

Enjoyed This Episode?

If this demo made you rethink what's possible to build without a developer, start by naming one repetitive task in your own business that a scan, a match, or an automated follow-up could solve. Share this episode with someone still paying a developer six figures for something that might now take weeks, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live workshop session from the AI for Business event, team member Mike shows off a sales coaching and training platform he built entirely on his own, with zero coding experience, after seeing a similar tool Brian had put together. What started as curiosity turned into an all-night build session, roughly 150 prompts and sixteen planned phases, using Lovable to build the app and Perplexity Comet as an AI agent that could take his ideas, write the prompts, and execute the build in Lovable largely on its own.

The app itself is built for sales teams and covers a lot of ground: a command center tracking calls and appointments, a role-play arena where reps practice against an AI voice trained to simulate different sales scenarios and then grades their performance, an intelligence dashboard tracking conversion trends and best calling times, an objection-handling library, leaderboards, a training academy for onboarding new hires, and a manager's coaching console. Brian and Mike also touch on the bigger lesson underneath the demo: treating AI less like a search engine and more like a collaborator you talk back and forth with until it surfaces ideas you wouldn't have thought of yourself.

Timeline Summary

[0:01] When you actually need to bring in a developer, and why it's often much later than people think

[1:29] Confirming you can build a full multi-user app without any developer help at all

[2:24] Mike's origin story: seeing Brian's build and deciding to build his own version himself

[3:35] The scope of the build: sixteen planned phases, uploading a company-specific knowledge base

[4:27] Realizing the concept could extend far beyond one industry, into collections, customer service, and more

[4:49] The all-night build session: roughly 150 prompts in one sitting

[5:07] Introducing Perplexity Comet as the AI agent handling the build almost entirely on its own

[6:13] Why Brian held back on sharing this approach at first, given the $200 a month cost of the tool

[6:33] The progression from prompting AI yourself to letting an AI agent handle the whole process

[7:14] Mike's own track record building apps in Lovable, including sports betting projects

[7:34] The bigger lesson: talk to AI like a person and keep digging past its first answer

[8:24] Touring the app: the war room command center and call history tracking

[8:44] The role-play arena: text-based for now, with voice role-play already tested separately

[9:33] How the AI-voice role-play grades a rep's pitch and gives improvement feedback

[10:17] The intelligence section: outcomes, best calling times, and conversion trends

[10:55] The objection bot, covering common objections reps can practice against

[11:19] The leaderboard and personal performance report for individual reps

[11:43] The manager's coaching console, including trend-based coaching assignments

[12:05] The training academy, built for onboarding new hires with uploaded product training

[12:38] The coaching console showing rep activity and live call listen-in capability

[13:01] Wrapping the tour with the reporting section

[13:41] What's coming in phase 13: real-time AI listening for objection handling during live calls

5 Key Takeaways

1. You Don't Need a Developer Until You're Scaling — For most single-build apps, you likely won't need to bring in a developer until you're serving somewhere in the range of a thousand users. Before that point, your own time is usually better spent elsewhere anyway once the app is working.
2. Multi-User Apps Are Fully Buildable Without Code — A complete app with individual logins, private databases, and end-to-end functionality can be built without any developer involvement, using tools like Lovable.
3. AI Agents Can Now Build Almost Autonomously — The next stage after prompting AI yourself is letting an AI agent take your idea, write its own prompts, and execute the build in another tool like Lovable with minimal hands-on involvement from you.
4. Talk to AI Like You're Talking to a Person — The single biggest lesson from this demo is treating AI as a real back-and-forth conversation partner. Don't settle for the first answer; keep digging to surface ideas you wouldn't have found otherwise.
5. One Internal Tool Can Apply Far Beyond Its Original Use Case — What started as a sales coaching tool for one specific industry was quickly recognized as applicable to collections, customer service, and virtually any team that handles live conversations with customers.

Links & Resources

• Lovable (the AI app-building platform used to build the demoed app) — https://lovable.dev
• Perplexity Comet (the AI browser/agent used to autonomously drive the Lovable build) — https://www.perplexity.ai/comet
• ChatGPT (referenced as the model powering the voice role-play feature) — https://chatgpt.com
• Claude (referenced as a tool Mike used elsewhere, including for a fantasy baseball draft) — https://claude.ai

Enjoyed This Episode?

If this demo made you realize you could build the internal tool you've been putting off, don't wait for the "perfect" build plan, start talking to AI about the problem and keep pushing past its first answer. Share this episode with a sales leader who's still doing rep coaching manually, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live case study session from the AI for Business event, team member DeMar walks the room through a custom internal tool he built with zero coding experience: a real-time business intelligence dashboard that replaced a $200,000-plus custom-developed system the company had been paying outside developers to build over three or four years. DeMar rebuilt the same functionality himself, working with AI, in under a month.

The dashboard pulls live data from ad accounts, Zoom, payment processors, and CRM systems into one place, something DeMar's team previously had to manually gather from five or six different sources for their morning meetings. He demos a live events dashboard tracking ad spend, revenue, and real-time Zoom attendee counts, a system for comparing performance across different past events, and a custom order processing tool that solved billing limitations neither Infusionsoft nor Stripe could handle. He closes with two of the build's most powerful features: a sales team activity tracker that flags duplicate bills and chargebacks automatically, and a "chat with your data" feature that lets anyone on the team ask plain-English questions and get instant answers from the numbers.

Timeline Summary

[0:01] Opening framing: build the tool that solves the gap you or people close to you are struggling with

[0:33] Introducing the business intelligence system and the problem of pulling numbers from five or six sources

[1:21] DeMar's technical background: zero coding experience, directing developers instead

[1:44] The old system cost over $200,000 and took three to four years to build

[2:16] The core challenge: individually great tools that never talk to each other

[2:41] Live demo of the events dashboard, showing real data flowing in

[3:00] Tying the dashboard to ad accounts to show spend and net profit in real time

[3:29] Solving the Zoom problem: seeing live registration and attendance instead of waiting for a report

[4:07] Comparing current event performance against past events in real time

[4:30] What the system would have cost to build externally versus what it actually cost

[5:16] Tracking every variation of a live pitch during a single event automatically

[5:51] The order processing problem: Infusionsoft's rigid billing cycle limitations

[6:40] Why Stripe's custom plans still required manually chasing clients for payment

[6:59] Building a custom system that bills exactly when and how they want

[7:20] The real cost comparison: roughly $3,000 a month with Infusionsoft, replaced for a fraction of that

[7:59] Why building for your own specific business beats forcing a generic tool to fit

[8:39] The chat bubble feature: going back and forth with AI before committing to a build

[9:35] Building in phases, starting with the dashboard and testing each feature before moving on

[10:18] Audience question: how the system tracks the sales team's calls and messages

[11:08] How the system catches human error: duplicate bills and chargebacks flagged automatically

[12:00] The "chat with your data" feature: asking any question about the numbers in plain English

[12:35] How the internal tool turned into client demand after sharing it at a mastermind

5 Key Takeaways

1. Build What You Already Need — The best tool to build is usually the one solving a problem you or your team already has every day. DeMar's dashboard exists because his team was manually pulling numbers from five or six sources every morning.
2. You Don't Need to Code to Build Real Software — DeMar rebuilt a $200,000-plus, multi-year custom system himself in under a month, with zero coding experience, simply by directing AI tools clearly and testing as he went.
3. Use the Chat Feature Before You Build — Go back and forth with the AI to fully develop an idea before committing to building it. AI tends to agree and start building prematurely, so pushing back and refining first produces a better result.
4. Build in Phases, One Feature at a Time — Rather than attempting the entire system at once, start with one piece, like a dashboard, get it fully working, and only then move to the next feature.
5. Internal Tools Can Become External Products — What starts as solving your own team's problem can turn into real client demand once others see it. DeMar's team wasn't planning to sell the system until they shared it at a mastermind and clients started asking for their own version.

Links & Resources

• GoHighLevel (CRM referenced as part of their existing tool stack) — https://www.gohighlevel.com/9683a6
• Zoom (webinar platform integrated into the dashboard) — https://www.zoom.com/
• Stripe (payment processor referenced for its billing limitations) — https://stripe.com/
• Infusionsoft (legacy CRM/billing platform referenced for its billing limitations)
• AI for Business community (referenced as the mastermind where the tool was first shared) — https://go.aiforbusiness.com/start

Enjoyed This Episode?

If this case study got you thinking about the manual process your own team repeats every week, that might be your version of DeMar's dashboard. Start by naming the actual gap, not a generic idea, and build toward that one problem first. Share this episode with a business owner who's paying thousands a month for tools that still don't talk to each other, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live workshop session from the AI for Business event, the speaker who personally built Revven walks the room through how he did it: using an AI app-building platform called Lovable to create real, working software just by describing what he wanted in plain English. He calls the concept becoming an "app-preneur," building and selling custom apps and websites as a business, without needing to code, design, or hire a developer.

He makes the case that this is the same kind of shift as the SEO wave of the mid-2000s or the social media wave of the early 2010s: a new, low-barrier skill that rewards whoever moves early. His core advice is to look inward first, build something that solves a real problem inside your own business, because if you have that problem, other people in your industry almost certainly do too. From there, he breaks down the profit models available, from selling templates and building custom apps for clients to ongoing hosting and maintenance fees, before getting into his practical playbook: planning before prompting, building in phases instead of one giant request, and using a dedicated knowledge file so the AI never loses context on the project.

Timeline Summary

[0:01] Introducing the topic: becoming an app-preneur and building apps and websites with AI

[1:06] A show of hands: who in the room has already started building with AI

[1:47] The speaker's own credentials as a top 1% builder on Lovable

[2:05] The session agenda: what an app-preneur is, an intro to Lovable, and a live demo

[3:29] What an app-preneur is, and how the speaker became one almost by accident

[3:50] The internal company need that led to building the app that became Revven

[4:41] Coining the term "minimum lovable product" instead of MVP

[5:08] Selling templates for the first time, and how fast they sold out

[5:49] Why looking inward at your own business's problems beats chasing something shiny and new

[7:19] A real internal example: building a tool to pull scattered business numbers into one place

[7:55] Comparing this moment to the SEO wave of 2005 and the social media wave of 2010

[9:22] What Lovable actually is: real, ownable code, unlike some competing platforms

[10:13] How Lovable has evolved: built-in database, built-in AI, no more copying API keys

[11:21] The range of things you can build: landing pages, dashboards, CRMs, chatbots, and more

[12:58] Why planning takes up roughly 60% of the time on any given build

[13:40] Starting with one problem, one audience, and one app to keep things simple

[14:05] Building for a buyer, not just yourself, even when solving your own problem first

[14:45] Agent mode versus planning mode, and why knowing the difference matters

[15:23] Building in phases instead of one giant prompt, and why long prompts confuse the AI

[16:16] Why your knowledge file is the secret weapon behind every consistent build

[16:51] The profit models: template sales, done-for-you deployments, hosting, and maintenance fees

[18:16] SaaS subscriptions and lead generation tools as additional profit angles

[19:32] The playbook gift, prompt build sequences, and auditing before you sell

5 Key Takeaways

1. Solve Your Own Problem First — The best app idea is usually already inside your own business. If something is a headache for you, there's a strong chance everyone else in your industry has the exact same headache, which means a ready market before you've sold a single copy.
2. Plan Before You Prompt — Roughly 60% of a build's time should go into planning, not typing prompts. A weak plan costs more time and money down the line than the extra planning ever would have.
3. Build in Phases, Not One Giant Prompt — Long, everything-at-once prompts confuse the AI and produce worse results. Breaking a build into smaller, sequential steps, sometimes dozens of them, produces a cleaner, more reliable app.
4. A Knowledge File Is Your Secret Weapon — Keeping a dedicated file of everything the AI needs to know about a project means every single prompt pulls from consistent context, instead of the AI forgetting details between requests.
5. There's More Than One Way to Profit — Beyond simply selling an app once, the real money is in template sales, custom done-for-you builds, and ongoing hosting and maintenance fees that turn a single project into recurring monthly revenue.

Links & Resources

• Lovable — https://lovable.dev
• ChatGPT — https://chatgpt.com
• Google Gemini — https://gemini.google.com
• Claude — https://claude.ai
• AI for Business Pro / Revven — https://go.aiforbusiness.com/ai4b-pro?_go=d1xyg5

Enjoyed This Episode?

If this session got you thinking about a problem in your own business that a custom tool could fix, don't overthink it, just start with one problem, one audience, and one simple app. Share this episode with someone who's been putting off learning this stuff because it sounds too technical, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live workshop session from the AI for Business event, the speaker who built much of the platform gives a full walkthrough of Revven, the all-in-one AI toolset bundled into AI for Business Pro. His framing up front is honest: Revven isn't meant to replace every tool you use; it's there to cut down on the pile of separate subscriptions most members were already paying for.

He tours the platform's chat interface, which includes every major frontier model in one place, then digs into the features members most often overlook: Brand Voices and Knowledge Bases for training the AI on a business's specifics, a prompt-building tool that turns a rough one-line request into a fully engineered prompt, and a customer persona generator powered by Claude Opus. From there he demos the content engine, social media posts with auto-generated images, a full ebook generator, a newsletter generator, a photo style-transfer tool, and a video sales letter generator, before handing the stage off to a colleague to cover the Pro program more broadly.

Timeline Summary

[0:01] Introducing Revven and being upfront that it's a helpful tool, not a cure-all

[1:07] Why the speaker still uses standalone Claude alongside the version built into Revven

[2:07] The chat box tour: every major frontier model available in one place

[3:33] Building a knowledge base and creating a custom AI personality for each client

[4:15] The difference between Brand Voices and Knowledge Bases

[5:01] Doing the guided ten-question or eighty-question brand voice interview

[6:53] Uploading documents, including Word, txt, CSV, and Excel files, into a knowledge base

[8:22] Creating multiple brand voices for different clients or businesses

[8:46] The prompt-building tool and why a good prompt is the foundation of every good AI output

[10:42] Live demo: turning a one-line prompt into a fully engineered business plan prompt

[17:45] The shorter text prompt option, and why XML formatting helps structure a request

[18:44] The image and video prompt tools, built in JSON format for cleaner AI output

[19:08] Live demo: generating an image prompt for a real estate business

[22:00] Generating the actual image using Nano Banana, and why AI image hallucinations happen

[24:05] The customer persona generator, demoed live for a chatbot agency business

[27:33] Why more specific industry inputs produce sharper, more useful personas

[27:54] The social media generator, including auto-created captions and matching images

[31:21] The ebook generator, demoed live with a book created minutes before going on stage

[32:44] The newsletter generator, which pulls trending industry news automatically

[33:23] A recap of the tools covered so far, and handing the session over to Alex

[34:05] The photo style-transfer tool, demoed live on the speaker's own headshot

[35:06] The video sales letter generator and the music tool powering it

[35:45] The presentation tool, logo generator, and job posting and interview question generator

[36:22] Additional tools: prompt engineering coach, audio transcription, web scraper, and hook generator

5 Key Takeaways

1. Revven Is a Bonus, Not a Replacement — The tool was built to cut down on the subscriptions members were already paying for separately, not to replace every tool in a business's stack. Use it where it helps and keep using other tools where they work better for you.
2. A Good Prompt Is Still the Foundation — No matter which AI tool you use, a bad prompt produces a bad output. The built-in prompt-building tool exists to turn a rough one-line idea into a fully structured prompt with role, context, and objective already engineered in.
3. Specificity Makes Every Tool Better — Whether it's a customer persona, a business plan, or an image prompt, the more precisely you describe your industry and audience, the sharper and more useful the AI's output becomes.
4. One Platform, Many Content Formats — A single business idea can be spun into a business plan, a customer persona, social posts with images, an ebook, a newsletter, and a video sales letter script, all from tools built into the same platform.
5. AI Image and Video Output Still Needs Iteration — Even strong tools like Nano Banana won't nail every generation on the first try. Regenerating and adjusting prompt length and detail is a normal part of the process, not a sign the tool is broken.

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If you're a member and you didn't know half of what's built into your account, go log in and just click around Brand Voices, the prompt builder, and the persona generator this week. Share this episode with a fellow member who's been paying for five separate AI subscriptions without realizing most of it is already included, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live workshop session from the AI for Business event, longtime AI for Business Pro coach Alex walks a room full of members through exactly how to get real, usable output from the program instead of just consuming more education. Alex has been part of the AI for Business team since 2019, and his whole pitch is simple: don't wait until you've finished every module to start doing something, jump in and learn as you go.

He splits the session into two live exercises. First, everyone builds a customer avatar using the Pro Customer Persona Generator, entering their industry, price point, product or service, budget, and target market, then generating three personas on the spot. From there, Alex walks through the next natural steps, feeding that persona into a three-year business plan and a 90-day marketing plan, so attendees leave with an actual document instead of just notes. Along the way he shares his own story, retiring in 2014, and later taking up marathon running and mountaineering at a point in his life when he decided to try something new instead of "getting drunk like the previous 20 years."

Timeline Summary

[0:01] Alex introduces himself, his 24 hour trip from Romania, and his role in AI for Business since 2019

[1:14] The range of people in the room: business owners, people starting from scratch, and hobbyists

[1:34] The three pillars of AI for Business Pro: education, tools, and support

[2:23] The core idea of AI for Business Pro in one sentence: using AI to optimize marketing and sales

[3:16] A tour of the Pro modules: prompting, business research, productivity, marketing, and mindset

[3:45] The one-on-one coaching calls built into the program after modules four, eight, and twelve

[4:32] Why the one-year track moves faster than the monthly track through the same material

[4:55] How to use the modules selectively if you already have a business with a specific gap

[5:21] Why someone with no business yet should go through the entire plan instead

[6:41] Matching a service you'll actually enjoy, from funnels to copywriting to chatbots

[7:08] The full recipe for starting from an idea: research, content, audience, then a funnel

[9:03] Starting every consultation the same way: establishing the audience first

[9:53] Live exercise: everyone opens the Pro Customer Persona Generator together

[10:19] Filling in industry, price point, and product or service in the tool

[11:07] Alex's personal story: turning 40, a first marathon in Amsterdam, and the Seven Volcano Challenge

[12:44] Filling in budget, target market, and current customer insights

[13:49] Adding geographic focus and generating three personas

[14:47] Saving the three personas into a new document to reference later

[16:29] What comes next: feeding the persona into a three-year business plan and a 90-day marketing plan

5 Key Takeaways

1. Do the Work, Don't Just Watch — Most people leave educational events passive and walk away with nothing built. Alex's whole session is structured around leaving with an actual document in hand, not just notes.
2. Start With the Audience, Always — Before touching a business plan, a funnel, or content, Alex starts every consultation by establishing the customer persona first. Everything downstream references back to that audience.
3. Match the Service to What You Enjoy — If you're building a business from scratch, pick the module that fits a skill you'll actually enjoy doing for clients, whether that's funnels, copywriting, chatbots, or virtual assistant work, since forcing yourself into one you dislike won't last.
4. You Don't Need to Know Everything First — Alex describes himself as someone who jumps in and learns as he goes. His model is research the market, create content to test interest, then build the funnel only once leads start responding.
5. Feed AI Real Context for Better Results — Generating a customer persona early and reusing it in every future prompt, whether for a business plan or social content, produces noticeably better results than starting from scratch each time.

Links & Resources

• AI for Business Pro — https://go.aiforbusiness.com/ai-for-business-pro-sales-page
• ChatGPT — https://chatgpt.com
• Claude — https://claude.ai

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If this session made you realize you've been consuming education without building anything, try Alex's approach: open a document right now, sketch out who your customer actually is, and use that one page to guide your next move. Share this episode with someone who's stuck in learning mode and ready to start doing, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live workshop session from the AI for Business event, Jax builds directly on the earlier "One Big Domino" talk to answer the next question: who exactly are you talking to? His core warning is blunt. When you market to everyone, you market to no one, and every dollar spent on the wrong audience is a dollar you never get back. He backs it up with a $20,000 Facebook lead ad campaign that chased cheap leads, generated thousands of them, and returned zero.

From there he walks the room through a four-step process for using AI to find and validate an ideal audience: feed the AI your positioning statement, build a detailed customer avatar, validate it against what real people are actually searching and saying, and package it all into a one-page audience cheat sheet. He runs it live in Claude using voice dictation, then shows how the same prompt spins up three distinct avatars, each with its own fears, desires, and scroll-stopping hook.

Timeline Summary

[0:01] – Why finding your ideal audience is the next step after your positioning statement and one big domino

[0:24] – The core truth: when you market to everyone, you market to no one

[1:23] – The $20,000 Facebook lead ad story that returned zero

[2:26] – Why 80% of businesses fail, starting with not knowing who they talk to

[3:02] – The validation tools coming up: Answer the Public, Reddit, and Google autocomplete

[3:51] – Why you want the market, not just you, to confirm who your audience is

[4:12] – The four steps, from telling AI about your business to a one-page cheat sheet

[4:35] – The copywriter who wrote an Oprah Magazine ad as if writing to her own mother

[6:23] – Where to find the prompts and slides for the session

[6:51] – Feeding your positioning statement into AI as a free marketing consultant

[7:41] – Running it live in Claude with WhisperFlow voice dictation

[9:40] – Asking AI for direct questions instead of multiple choice, to gut-check the audience

[11:14] – Answering the emotional pain points: being left behind, overwhelmed, and unsure where to focus

[13:27] – The avatar AI returns: the entrepreneur pioneer who built a business the old fashioned way

[14:09] – Demographics versus psychographics, and why the psychographics are the game changer

[15:19] – The prompt that generates three distinct avatars with names, fears, and scroll stoppers

[16:37] – The three avatars, each with a one-sentence hook, and why one message cannot serve them all

5 Key Takeaways

1. Market to Everyone, Reach No One — A broad message lands with nobody. The tighter you define who you serve, their fears, desires, and exact words, the more your marketing feels like it was written for one specific person.
2. The Wrong Audience Is Pure Waste — Cheap leads are worthless if they are the wrong people. A $20,000 campaign optimized for low cost per lead generated thousands of leads and zero revenue, because the audience and the message were both off.
3. Give AI Your Positioning First — AI cannot find your audience until it understands your business. Feed it your positioning statement, then let it ask clarifying questions, so the avatar it builds is grounded in what you actually do.
4. Psychographics Beat Demographics — Age, gender, and location are useful for targeting, but the fears, values, and frustrations are what let you write something that stops the scroll. That emotional layer is where real understanding lives.
5. Validate, Don't Assume — AI will happily agree with a wrong guess, so confirm your audience against reality. Answer the Public, Reddit, and Google autocomplete show you the actual words real people use when they search for what you offer.

Links & Resources

• Answer the Public (audience and keyword validation tool) — https://answerthepublic.com
• Reddit (forums where people discuss every subject, useful for audience research) — https://www.reddit.com
• Claude (used live to build the audience avatars) — https://claude.ai
• ChatGPT (alternative for the same prompts) — https://chatgpt.com
• Gemini (alternative for the same prompts) — https://gemini.google.com
• WhisperFlow (voice dictation tool the speaker uses to prompt AI) — https://wisprflow.ai
• The AI for Business community and private Facebook group — https://www.facebook.com/groups/aiforbusinessowners/

Enjoyed This Episode?

Before you spend another dollar on ads, do the exercise from this session: write your positioning statement, hand it to Claude or ChatGPT, and let it build out the avatar of the one person you are actually trying to reach. Then go validate it against what real people are searching. Share this episode with someone who is burning budget on the wrong audience, and if it helped, hit subscribe and pass it along to a friend or colleague.

In this live workshop session from the AI for Business event, the Jax Core breaks down one of the most powerful ideas in marketing: the "one big domino." Borrowed from Russell Brunson and Alex Hormozi, the concept is simple. Every prospect is holding onto one belief that is blocking the sale, and your job is not to sell your offer, it is to knock down that single belief so every other objection falls with it.

Working the room, Jax walks attendees through the two questions that surface their own one big domino, shares his personal domino ("you are one post away") and the daily-video "infinite feed" system it led to, and even hands the audience a ChatGPT and Claude prompt to draft their domino on the spot. It is a fast, hands-on session that sends everyone home with a one-sentence belief statement they can build their marketing around.

Timeline Summary

[0:01] – What a "one big domino" is: one belief that, when it falls, everything falls with it

[0:25] – Your job is not to sell the offer, it is to knock down one belief

[0:53] – The real question in every prospect's mind: can I trust this person with my money

[1:52] – Question one: what small action do you want your audience to take

[2:49] – Question two: what must they believe in order to take that action

[3:04] – The speaker's own domino, you are one post away

[3:13] – Being an introvert and choosing video to show up everywhere

[4:43] – The domino is not a feature, it is a belief that removes every objection

[5:20] – What showing up daily unlocked, from speaking gigs to a law firm inquiry

[6:09] – The law firm Zoom story and the power of being seen as everywhere

[6:58] – The ChatGPT and Claude prompt to draft your one big domino

[8:33] – Crediting Russell Brunson and Alex Hormozi for the one-sentence framing

[9:14] – Workshop time: craft your own one big domino at your table

[10:15] – Attendees share their dominoes, from real estate to scholarships

[12:42] – What is coming tomorrow, turning one idea into 20 posts

5 Key Takeaways

1. Sell the Belief, Not the Offer — Every prospect walks in with skepticism, wondering if they can trust you with their money. Your job is to knock down that one belief, and every other objection falls with it.
2. Find Your Domino With Two Questions — Decide the small action you want your audience to take, then name the single thing they must believe to take it. If they believe that one thing, everything else becomes easier or unnecessary.
3. Small Actions Lead to Larger Ones — You do not need someone to buy off your website. Start with a tiny yes, like booking a discovery call or filling out a form, and let those small wins build toward the bigger commitment.
4. Consistency Creates Omnipresence — The speaker's domino, "you are one post away," turned daily video into the feeling of being everywhere. When a prospect feels like they see you constantly, trust is built before the first conversation.
5. Let AI Draft Your Domino — Feed ChatGPT or Claude your positioning statement, your desired action, and the biggest objection, then ask it to write your one big domino in a single strong, human sentence with three proof points.

Links & Resources

• Expert Secrets by Russell Brunson (the origin of the One Big Domino concept) — https://marketingsecrets.com
• Alex Hormozi (credited for the framing of the concept) — https://www.acquisition.com
• ChatGPT (used live to draft the one big domino) — https://chatgpt.com
• Claude (used live to draft the one big domino) — https://claude.ai
• The AI for Business community and private Facebook group (referenced on stage; no public URL was given during the session)

Enjoyed This Episode?

If this session made you rethink the way you pitch, try it before your next sales call: write down the one belief your prospect has to accept, then build everything around knocking it down. Share this episode with a business owner who is tired of selling and ready to start attracting, and if you found it useful, hit subscribe and pass it along to a friend or colleague.