AI Visibility: How to Get Your Brand Recommended by ChatGPT, Perplexity, and Google AI

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  The Data Your CRM Never Sees: Will Hamblin on AI, Field Sales & FieldSpot.ai 27.09.2026 6นาที

  Most CRM software assumes selling happens from a desk.Will Hamblin built FieldSpot.ai because his sales job did not.After twelve years in education, including time as a vice principal, Will moved into door-to-door card-payment sales. In the field, he saw the same problem repeatedly: salespeople were learning valuable things every day—who a business used, when its contract renewed, who made the decision, why they said no—but most of that intelligence ended up in notebooks, spreadsheets, or someone’s memory.In this episode of the AI Visibility Podcast, Jason T Wade talks with Will about turning those fragmented real-world signals into structured data that AI can actually use.FieldSpot.ai combines territory intelligence, mapping, route planning, renewal tracking, competitor data, voice notes, business-card capture, and AI-assisted outreach into a CRM designed specifically around field sales.A major idea in the conversation is deceptively simple: a “no” is still data.A prospect who rejects you today may tell you exactly when to return, which competitor you need to beat, and what will matter when the contract comes up for renewal. The problem is not collecting more leads. It is preserving the context surrounding every interaction and making it available at the right moment.Will and Jason also discuss why AI quality depends on data quality. Generic data produces generic automation. But when AI has access to actual notes from the field, account history, timing, competitor information, and local context, outreach can become significantly more relevant.They also explore a potential irony of the AI era: as digital channels fill with automated outreach, showing up in person may become more valuable, not less.Will also explains how he built FieldSpot’s first prototype without being a developer, used AI and vibe coding to prove the concept, recruited experienced technical partners, and started taking the product beyond its original UK payments market.Topics include:Why traditional CRMs break down in field salesCapturing intelligence from unsuccessful sales visitsRenewal dates as a sales signalCompetitor tracking in the real worldTerritory mapping and route optimizationVoice notes and frictionless data captureTurning field notes into personalized AI outreachWhy AI is only as useful as the data underneath itVibe coding a startup prototype without being a developerRecruiting technical partners through equityWhy face-to-face sales could become more valuable in an AI-saturated marketDesigning software around how salespeople actually workExpanding a field-sales platform internationallyWhere AI-powered field sales goes nextWill Hamblin is the founder of FieldSpot.ai, an AI-powered CRM and field-sales intelligence platform designed for teams that sell in person.Before starting FieldSpot, Will spent twelve years in education and became a vice principal before moving into field sales, where he sold card-payment services directly to businesses.That experience exposed how much useful sales intelligence was being lost between visits. Will built the first FieldSpot prototype using AI tools and vibe coding, then brought in experienced technical partners to develop the platform further.FieldSpot focuses on territory intelligence, renewal tracking, competitor data, route planning, mobile data capture, and AI-assisted sales workflows.Jason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier.Jason is also the host of the AI Visibility Podcast, where he examines how AI search, recommendation systems, autonomous agents, and emerging interfaces are changing how businesses and information get discovered.FieldSpot.aihttps://fieldspot.aiWill Hamblin on LinkedInhttps://www.linkedin.com/in/will-hamblin-182a1064/Jason T Wadehttps://jasonwade.comNinjaAIhttps://ninjaai.comBackTierhttps://backtier.com
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  Why Field Sales Needs Better Data: Will Hamblin on Building FieldSpot.ai 26.09.2026 21นาที

  Why Field Sales Needs Better Data: Will Hamblin on Building FieldSpot.aiWill Hamblin went from vice principal to door-to-door card terminal sales—and eventually built the software he wished he had while working in the field.In this episode of the AI Visibility Podcast, Jason T Wade talks with Will about what traditional CRM systems miss when sales happens face-to-face rather than behind a desk.FieldSpot.ai started as a simple AI-assisted prototype and evolved into a field-sales intelligence platform built around territory mapping, renewal timing, competitor intelligence, route planning, voice notes, business-card capture, and AI-assisted outreach.One of the central ideas is that field sales creates valuable data constantly, but much of it disappears. A rejection today may contain the information needed to close the account six months from now: the incumbent provider, contract expiration date, decision-maker, or reason the prospect was not ready.Will explains how FieldSpot captures that context and turns it into usable intelligence for future visits and outreach.Jason and Will also discuss the relationship between data quality and AI output. AI can generate better follow-up and more relevant outreach when it has access to actual field notes, conversations, territory information, and account history instead of generic CRM records.They also explore whether face-to-face selling could become more valuable as email, LinkedIn, and other digital channels become increasingly saturated with automated AI outreach.Will shares how he built FieldSpot's first prototype without being a developer, used AI and vibe coding to turn an idea into something tangible, found technical partners willing to join the company for equity, and began expanding the platform beyond its original UK payments market.Topics include:Why traditional CRMs often fail field-sales teamsTurning rejected visits into future sales intelligenceRenewal dates and competitor contract trackingTerritory mapping and route planningVoice notes and automatic field-data captureAI-assisted personalized outreachBuilding a startup through vibe codingWhy better data produces better AI outputThe growing value of face-to-face salesDesigning software around actual field conditionsBuilding a technology company as a nontechnical founderExpanding FieldSpot internationallyThe future of AI-powered field salesWill Hamblin is the founder of FieldSpot.ai, an AI-powered CRM and field-sales intelligence platform built for teams that sell in person.Before founding FieldSpot, Will spent twelve years in education, eventually becoming a vice principal. He later moved into field sales, selling card-payment services directly to businesses.That experience exposed a gap in traditional sales software. Field agents were still relying heavily on spreadsheets, notebooks, memory, and manual route planning, while valuable information about competitors, conversations, territories, and renewal dates was frequently lost.Will built the first FieldSpot prototype using AI tools before bringing in experienced developers to develop the platform further.Jason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier.His work focuses on how AI systems discover, understand, classify, cite, include, and recommend people, companies, products, and ideas.He works across AI SEO, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity architecture, structured data, and AI discovery systems.Jason hosts the AI Visibility Podcast, exploring how AI search, recommendation systems, autonomous agents, and emerging interfaces are changing discovery, business, and the web.FieldSpot.aihttps://fieldspot.aiWill Hamblin on LinkedInhttps://www.linkedin.com/in/will-hamblin-182a1064/Jason T Wadehttps://jasonwade.comNinjaAIhttps://ninjaai.comBackTierhttps://backtier.com
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  How I Used GPT, Claude + Lovable to Build Halden 25.09.2026 2นาที

  How I Used GPT, Claude + Lovable to Build HaldenThis is the process behind my Lovable Built It for Small Business Challenge entry.I didn’t start with:“Build me a detailing website.”I started with the actual challenge brief, the business problem, and the experience I wanted to create.I pasted the requirements into GPT, developed the concept, refined the copy and customer flow, and then moved the build into Lovable.From there, the process became a constant loop.Build → inspect → copy the site back into GPT or Claude → critique it → improve the prompt → rebuild.I would often copy nearly every word from the site back into the models and ask what was missing, confusing, inconsistent, or not aligned with the original vision.I also researched what other service businesses and software companies were doing — current customer experiences, automation, checkout patterns, and where agentic commerce appears to be heading — and brought those ideas back into the project.That research helped push Halden beyond a normal appointment website into something designed for both people and AI assistants.The biggest lesson from the process:Don’t stop when the build works. Analyze it again.Even after the product looks finished, run it back through the models, test the assumptions, refine the experience, and keep iterating.That back-and-forth between human direction, AI analysis, research, and Lovable implementation is how I built Halden.Halden Detail Co. was built in Lovable for the Built It for Small Business Challenge.The challenge is centered on reducing the friction between an inbound customer inquiry and a confirmed appointment.Halden handles that booking process while also exploring what happens when an AI assistant becomes another interface into the same business.@Lovable · #LovableChallengeJason T Wade is the founder of BackTier and works on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity architecture, and emerging agentic experiences.His work focuses on how businesses can become easier for AI systems to discover, understand, recommend, and interact with.Halden / Lovable Buildhttps://winnerjasonwade.lovable.appLovablehttps://lovable.devJason T Wadehttps://jasonwade.comBackTierhttps://[email protected]
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  I Built an AI-Bookable Car Detailing Business in Lovable 25.09.2026 2นาที

  What happens when you design a small-business website not just for humans, but for AI agents?For the Lovable Built It for Small Business Challenge, I created Halden Detail Co., a fictional mobile detailing company in Scottsdale built around one problem: eliminate the manual work between a customer asking, “Can I book?” and receiving a confirmed appointment.Customers can choose a vehicle, select a detailing package, see the exact price and duration, view available appointment slots, pay a deposit, and complete the booking without waiting for the owner to respond.The system also handles the operational side: cancellations, waitlist recovery, scheduling, customer communication, and an owner dashboard.But the core experiment goes further.Halden is designed for agentic commerce.Instead of requiring an AI assistant to read a website and guess what is available, the business exposes structured capabilities for service discovery, quoting, availability, booking, and booking status.That means a customer can eventually tell an AI assistant:“Book my Escalade for a detail Friday afternoon.”The assistant can retrieve the real service, real price, real availability, and create the booking directly.One business. One pricing system. One calendar.Two interfaces: human and machine.This prototype was created in Lovable for the Lovable Built It for Small Business Challenge.The challenge brief was to redesign the moment an inbound customer inquiry becomes a confirmed booking while reducing as much manual work for the business owner as possible.Halden addresses both sides:Front door: customers can quote, schedule, and book without back-and-forth.Follow-through: deposits, scheduling, cancellation recovery, customer status, and owner operations are handled inside the system.The additional experiment is making those same capabilities accessible to AI assistants so the booking experience can evolve from traditional checkout toward agentic commerce.#LovableChallenge · @LovableJason T Wade is an AI Visibility Architect and founder of BackTier. He works on AI discovery, entity architecture, Generative Engine Optimization, Answer Engine Optimization, and the infrastructure that allows AI systems to accurately discover, understand, cite, recommend, and increasingly transact with businesses.His work focuses on the transition from websites built primarily for human search and browsing toward systems that also expose structured information and capabilities directly to AI agents.Haldenhttps://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/Customer Bookinghttps://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/bookText Bookinghttps://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/textAutopilothttps://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/autopilotOwner / Adminhttps://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/adminThe Onehttps://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/one-of-oneLovablehttps://lovable.devBackTierhttps://backtier.comJason T Wadehttps://jasontwade.comFor the contest submission itself, I’d use “From ‘Can I Book?’ to ‘You’re Booked’ — With AI Agents” because it mirrors their brief while immediately exposing your differentiator.
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  Jack Oujo — From Minor League Baseball to Financial Peace of Mind 25.09.2026 32นาที

  Jack Oujo — From Minor League Baseball to Financial Peace of MindComplete show notes from the transcript, including the episode description, chapters, takeaways, quotes, clip ideas, and follow-ups. Timestamps are approximate and should be checked against the final edit.Episode descriptionWhat happens when the career you built your identity around ends at 30—with no money and a baby on the way?Jack Oujo joins Jason Wade on BackTier to discuss his transition from professional baseball umpire to building a tax-focused wealth management business, which he later sold to two employees. His story connects career reinvention, a supportive marriage, calculated risk, and the question he says sits behind almost every retirement conversation: “Am I gonna be okay?”Jack explains why financial advice is fundamentally coaching, why a useful plan considers difficult markets, and why success involves more than accumulating money. The conversation also covers his daily use of AI to develop speaking material from his memoir, the limits of automated podcast editing, and how technology can free people to pursue more meaningful work.The episode closes with personal observations about entrepreneurship, travel, and economic opportunity.Guest backgroundAs described by Jack in the conversation:Spent eight years in professional baseball as an umpire before being released at 30.Started a business with a partner using credit cards for financing.Built an accounting practice that evolved into tax-focused wealth management.Eventually focused on clients who used the firm for wealth management.Sold the business to two employees, effective January 1; the year is not specified in the transcript.Wrote a memoir and began appearing on podcasts to promote it.Is developing corporate speaking engagements around reinvention, resilience, and lessons from his career.Uses AI daily, including to extract stories and lessons from his book.Says he is 68 and lives in Fort Lauderdale.His book title, business name, website, and preferred contact links are not supplied in the transcript.
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  AI for Community Organizing: Research Faster, Build Tools, and Get People Moving 24.09.2026 2นาที

  In this solo episode of the AI Visibility Podcast, Jason T Wade looks at a more practical use case: using AI as infrastructure for community organizing.The idea came from preparing for an upcoming event and from a local cemetery issue that affected Jason’s family. Instead of stopping at complaints, he used AI to pull together city budgets, reports, state information, and other public records, then combined that research with old-fashioned fieldwork: visiting the cemetery and talking directly with the people involved.Jason then walks through a simple toolkit for civic and community projects: research with Perplexity, analysis and presentations with Claude and Gamma, rapid websites and forms with Lovable or Base44, drafting press materials with ChatGPT or Claude, and organizing public events or petitions through platforms such as Meetup, Eventbrite, and Change.org. Topics- AI for community organizing- Researching public records and local issues- Combining AI research with in-person fact finding- Using agents for rapid research and communications- Building forms and civic tools without traditional development- Creating presentations and public information- Press releases and outreach- Organizing events and petitions- Turning complaints into documented action- Why AI should augment—not replace—real community engagementAbout Jason T WadeJason T Wade is an AI Visibility Architect and founder of BackTier.His work focuses on how AI systems discover, understand, classify, cite, include, and recommend people, companies, products, and ideas.Jason hosts the AI Visibility Podcast where he explores AI search, agents, emerging interfaces, and practical ways people and organizations can use AI systems.LinksJason Wadehttps://jasonwade.comBackTierhttps://backtier.comPerplexity — research and cited web answers https://www.perplexity.aiClaude — research, writing, analysis and creationhttps://claude.comGammahttps://gamma.appLovablehttps://lovable.devBase44 — AI application builder https://base44.comMeetuphttps://www.meetup.comEventbritehttps://www.eventbrite.comChange.org — petition and community-action platform https://www.change.org#backtier
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  AI Agents Are Exploding — Use Them Before the Free Ride Ends 24.09.2026 2นาที

  AI agents sound complicated until you realize what they actually do: they take the next step for you.In this episode of the AI Visibility Podcast, Jason T Wade breaks down why 2026 is becoming the year agents move from interesting experiments into practical everyday tools. From Meta’s Muse and Grok to Base44, Lovable, Cursor, Alexa+, and Copilot, the major platforms are rapidly expanding what autonomous AI systems can accomplish.Jason explains the simplest way he has found to start using agents: whenever you catch yourself doing something in ChatGPT and thinking, “I never want to do this manually again,” turn that workflow into an agent prompt.He also discusses why now is an unusually good time to experiment. New AI products are frequently being offered free or heavily subsidized while companies learn how people use them, making this a window to test multiple systems against the same task and understand where each one performs differently.The conversation also moves into agentic commerce. Amazon Alexa+ is emerging as an important interface for AI-driven shopping, where consumers can increasingly describe what they want conversationally instead of searching through product listings manually.The larger point is simple: agents are no longer just developer tools. They can work across inboxes, connectors, research, lead generation, data gathering, shopping, and repetitive workflows. The best way to understand them is not to study them endlessly. Give them real work.Topics include:AI agents and autonomous workflowsMeta Muse, Grok, Base44, Lovable and CursorTurning repetitive ChatGPT work into agent promptsTesting the same task across multiple AI enginesWhy new AI platforms are temporarily giving away significant capabilityAlexa+ and the rise of agentic shoppingCopilot and Amazon’s position in AI commerceEmail, connectors, lead research and data gatheringWhy agents are easier to use than most people assumeThe transition from chatting with AI to delegating work to AIJason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier. He works at the intersection of search, generative AI, entity architecture, AI SEO, Generative Engine Optimization and Answer Engine Optimization.His work focuses on how AI systems discover, understand, classify, cite, include and recommend people, companies, products and ideas.Jason also hosts the AI Visibility Podcast, where he examines how AI search, recommendation systems, autonomous agents and emerging interfaces are changing discovery, commerce and the web.Jason T Wadejasonwade.comNinjaAIninjaai.comBackTierbacktier.comAI Visibility PodcastAvailable on Spotify and major podcast platformsJason T Wade BioLinks
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  Technology Stress Is Not a Technology-Ability Problem 23.09.2026 2นาที

  Eighty-two percent of this show's audience is 45 or older — so this one is for you, and for everyone who loves someone in that group.There's a moment that repeats in millions of households. A login fails. An update moves a button. And within ninety seconds it stops being a technology problem and becomes a fight.This episode makes one argument: that escalation isn't evidence of low technology ability. It's a stress response. And once you treat it as one, it mostly stops happening.What's covered:Why usage and confidence are two different things — 34% of older internet users report little or no confidence with electronic devices (Pew)Why needing setup help is the median experience, not a deficiency — 48% of seniors say they usually need someone to show them a new device (Pew)The study of 630 adults ages 18–68 that found anger, resignation, and venting across the entire adult range (Heliyon) — this isn't generationalDigital stress as a measurable, rising condition: 9% to 20% of employees over one study period (JMIR)Why more instructions make it worse: working memory narrows under stress, so the first job is never the screenThe STOP–BREATHE–LOOK reset, step by stepThe two-sentence family rule: I will help. I will not be yelled at. — and what each side actually commits toThe line that ends the episode: the goal is not zero frustration, the goal is zero abuse during frustrationSources: Pew Research Center, Tech Adoption Climbs Among Older Adults; Heliyon, end-user frustrations and failures in digital technology; Journal of Medical Internet Research, impact of digital stress on negative emotions and physical complaints.Note: statistics provide context. They don't diagnose anyone, and they don't excuse yelling. If there's chest pain, severe shortness of breath, or you feel medically unwell — that's not technology stress. Address the health concern first.BioJason T Wade is an AI Visibility architect and the founder of BackTier, where he works on how AI systems discover, classify, cite, and recommend people and organizations. He's the author of AI Visibility: How to Win in the Age of Search, Chat & Smart Customers and the host of the AI Visibility Podcast. He's based in Lake Wales, FL and Orlando, Florida.
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  When AI Gets You Wrong: Identity, Ambiguity & Who Controls the Answer 23.09.2026 11นาที

  What happens when AI knows your name—but doesn't actually know who you are?In this roundtable episode of the AI Visibility Podcast, Jason T Wade is joined by Jason Barnard, Jodi Koch, and Su Belagodu for a wide-ranging conversation about identity, ambiguity, trust, human judgment, and the growing influence of AI recommendations.Jason Barnard starts with one of the fundamental problems of AI visibility: entity ambiguity. People share names, companies have inconsistent descriptions, and AI systems have to decide which facts belong to which entity. Su shares her own example of AI incorrectly attributing a Dubai speaking appearance to her because it confused her with another person working in AI governance. Roundtable on AI, Identity, and Ambiguity.docxDOCX Roundtable on AI, Identity, and Ambiguity.docxDOCXThe discussion moves into a larger question: if Google once gave users ten links to evaluate, what changes when an AI system increasingly makes the recommendation itself?Jason Barnard argues that businesses need to deliberately educate AI systems about who they are, what they do, and who they serve. Su adds an important counterpoint: AI outputs remain probabilistic, and human judgment still matters—especially when agents and automated systems begin making decisions at scale. Roundtable on AI, Identity, and Ambiguity.docxDOCXJodi brings the conversation into the physical world. As an interior designer with more than two decades of experience, she uses AI to rapidly visualize ideas with clients—but points out that an AI-generated room can still ignore structural reality. The technology can accelerate the process, but it does not replace the experience required to know whether the proposed design actually works. Roundtable on AI, Identity, and Ambiguity.docxDOCXA recurring theme emerges: AI visibility begins with being correctly understood. If a machine cannot reliably resolve who you are, it cannot reliably evaluate, recommend, or select you.Name and entity ambiguityWhat AI gets right—and wrong—about peopleCorrecting machine-generated identity errorsDigital consistency and corroborating sourcesAI as recommender instead of search engineProbabilistic AI outputsHuman-in-the-loop systemsAI agents and automationUsing AI in interior designTrusting versus verifying AI outputPersonal brands and company brandsControlling how AI understands an entityWhy human expertise becomes more important alongside AIJason Barnard is founder and CEO of Kalicube, a digital brand engineering company focused on helping people and companies control how Google and AI systems understand and represent them. His work spans entity identity, Knowledge Panels, digital brand intelligence, and AI-era recommendation systems. Kalicube - Digital Brand EngineersJodi Koch is the founder of Elizabeth Erin Designs and host of the Designing in 5D podcast. A nationally recognized interior designer with more than two decades of experience, she works with homeowners, investors, and hospitality clients using her Designing in 5D process. Elizabeth Erin DesignsSu Belagodu is an AI adoption and executive advisor and creator of the HITL Maturity Model™. Her work focuses on designing AI systems with meaningful human oversight, helping organizations move AI projects into production, and determining where humans need to remain in the loop. SublagoduJason T Wade is an AI Visibility Architect and founder of BackTier. His work focuses on how AI systems discover, resolve, understand, cite, include, and recommend people, companies, products, and ideas. He is the host of the AI Visibility Podcast. Jason AI WadeJason Barnard / KalicubeKalicube.comJason Barnard BioJodi Koch / Elizabeth Erin DesignsElizabeth Erin DesignsDesigning in 5D PodcastSu BelagoduSuBelagodu.meSu Belagodu on LinkedInJason T WadeJasonWade.comBackTier
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  The Knowledge Graph 22.09.2026 1นาที

  The Knowledge Graph
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  What AI Knows About You: Research, Reputation & Influence with Dan Barkhuff 22.09.2026 5นาที

  What happens when AI makes scattered public records easy to connect?Dan Barkhuff—a former Navy SEAL, emergency physician and founder of Civly—joins Jason AI Wade to discuss how AI is changing research, reputation and access to information.Dan shares how Civly began as an attempt to automate political compliance, then expanded into opposition research and applications beyond politics. The conversation explores public data, what AI assistants say about people and organizations, the boundaries of reputation management, and whether cheaper research could lower the cost of political participation.It’s an open conversation about what becomes possible when information that once took weeks to assemble can be researched much faster—and the questions that come with that access.In this episode:Dan’s path from the Naval Academy and SEAL teams to medicine and entrepreneurship.Why compliance automation led Civly into political research.Connecting financial filings, public records and social history.Applications beyond politics, including business research and athlete vetting.AI visibility and the line between accurate representation and manipulation.Donor data, fundraising calls and the economics of campaigning.Dan’s argument that practical AI implementation could make participation more affordable.Daniel “Dan” Barkhuff is the founder and CEO of Civly, an AI-powered research and intelligence company serving politics, business, athletics and nonprofits. A U.S. Naval Academy graduate and former Navy SEAL, he earned his medical degree at Harvard and trained in emergency medicine. He practices in Vermont and also founded Veterans for Responsible Leadership.Jason AI Wade is the host of the AI Visibility Podcast and is with BackTier. His work explores how AI systems discover, describe and recommend people and businesses, alongside the practical use of AI agents.CivlyDan Barkhuff and the Civly teamAI NarrativesResearch BooksData CoverageCivly BusinessClient Case StudiesContact Civly
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  1% Taxes? Income and AI and touching on property taxes and Government management and revenue 21.09.2026 12นาที

  1% Taxes? Income and AI and touching on property taxes and Government management and revenue
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  How AI Decides YOU - infrrence and LLM resolution and choices - external evidence - Jason T AI WADE 20.09.2026 1นาที

  How AI Decides YOU - infrrence and LLM resolution and choices - external evidence - Jason T AI WADE
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  Field Sales Is Broken — Will Hamblin on AI, Territory Intelligence and FieldSpot 19.09.2026 8นาที

  Will Hamblin went from vice principal to door-to-door card terminal sales, then built the software he wished he had while working in the field.In this episode of the AI Visibility Podcast, Jason T Wade talks with Will about the problems traditional CRM systems miss when sales happens physically rather than behind a desk.Will explains how FieldSpot.ai grew from a simple vibe-coded prototype into a field-sales platform built around territory intelligence, renewal timing, route planning, competitor tracking, voice notes, business-card capture, AI-assisted outreach, and real-world context.A major theme of the conversation is that field sales generates valuable data constantly, but most of it disappears. A rejection today may actually contain the most important information for a future sale: who the current provider is, when the contract expires, and when the salesperson should return.They also discuss why AI output depends heavily on the quality of the underlying data, how FieldSpot uses agent notes to make outreach more personal, and why face-to-face sales may become more valuable as inboxes become saturated with automated AI outreach.Will also shares how he built the first prototype without being a developer, found technical partners willing to work for equity, and began expanding FieldSpot beyond its original UK payments market.Topics include:Why traditional CRMs do not fit field salesTurning rejected visits into useful sales intelligenceRenewal tracking and competitor contract dataTerritory mapping and route planningVoice notes and automatic data captureAI-assisted personalized outreachBuilding the first FieldSpot prototype through vibe codingWhy better data produces better AI outputThe possible resurgence of face-to-face salesDesigning software around real field conditionsBuilding a startup without being the technical founderExpanding FieldSpot internationallyThe future of AI-powered field salesWill Hamblin is the founder of FieldSpot.ai, an AI-powered CRM and field-sales intelligence platform designed for teams that sell in person.Before founding FieldSpot, Will spent twelve years in education, eventually becoming a vice principal. He later moved into field sales, selling card-payment services directly to businesses.That experience exposed a gap in traditional sales software: field agents were still relying heavily on spreadsheets, notebooks, memory, and manual route planning while valuable information about competitors, customer conversations, and renewal dates was frequently lost.Will built the first FieldSpot prototype using AI tools before bringing in experienced developers to turn the concept into a production platform.FieldSpot is designed around territory mapping, renewal intelligence, competitor tracking, route planning, mobile data capture, and AI-assisted sales workflows.Jason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier.His work focuses on how AI systems discover, understand, classify, cite, include, and recommend people, companies, products, and ideas.He works across AI SEO, Generative Engine Optimization, Answer Engine Optimization, entity architecture, structured data, and AI discovery systems.Jason is also the host of the AI Visibility Podcast, where he explores how AI search, recommendation systems, autonomous agents, and emerging interfaces are changing discovery, business, and the web.FieldSpot.aihttps://fieldspot.aiWill Hamblin on LinkedInhttps://www.linkedin.com/in/will-hamblin-182a1064/Jason T Wadehttps://jasonwade.comNinjaAIhttps://ninjaai.comBackTierhttps://backtier.comAbout Will HamblinAbout Jason T WadeLinks
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  Digital Marketing -- Early adopters -- Bubbles - and AI marketing 19.09.2026 1นาที

  Digital Marketing -- Early adopters -- Bubbles - and AI marketing
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  The Jason AI Wade Experiment: Can You Deliberately Change How AI Understands a Person? 19.09.2026 5นาที

  I changed my name on the internet.Not legally. I changed the public identity I present to the web from Jason T Wade to Jason AI Wade, and I'm using the change as a live AI Visibility experiment.The question is bigger than a rebrand: Can a person deliberately change how AI systems identify, classify, cite, include, and eventually recommend them?For more than 20 years, we optimized digital identities primarily for humans and search engines. Generative AI adds another observer. ChatGPT, Gemini, Claude, Perplexity, and other systems now have to resolve people and organizations from scattered evidence, determine relationships between entities, evaluate competing claims, retrieve sources, and decide which entities belong in an answer.I'm deliberately changing that evidence environment and documenting what happens.The experiment follows five stages:Recognition → Classification → Citation → Inclusion → SelectionRecognition asks whether an AI system knows Jason AI Wade exists. Classification tests whether it understands who I am and what I actually do. Citation measures whether my work becomes evidence supporting answers. Inclusion asks whether I appear when the prompt does not already contain my name. Selection is the hardest test: when an AI system has several plausible people or sources available, does it choose me?The distinction matters because asking ChatGPT, “Who is Jason AI Wade?” is an easy test. The entity has already been supplied. Asking an AI system who created a particular framework, who researches AI Visibility, or which sources it should use to understand machine-mediated discovery forces it to retrieve and select entities independently.Over the coming weeks and months, I'll document changes to the public information environment around Jason AI Wade — canonical identity, structured data, author entities, terminology, publications, podcast metadata, company relationships, citations, external references, and independent corroboration — and compare those interventions with what different AI systems actually return.Some systems will probably recognize the change quickly. Others may continue using Jason T Wade. Some may incorrectly create two people. Others may resolve the identity correctly while attaching outdated professional information. Those failures are part of the experiment because they expose where retrieval, entity resolution, classification, citation, and selection diverge.The larger hypothesis is that every person and company now effectively has two identities: the identity they say they have and the identity machines reconstruct from available evidence.AI Visibility exists partly in the gap between them.Jason AI Wade is the test subject.Now we see what the machines do with him.Jason AI Wade is an AI Visibility architect, researcher, author, and founder of BackTier. His work focuses on how AI systems discover and resolve entities, interpret evidence, retrieve and cite sources, construct recommendations, and make decisions.Drawing on more than 20 years across search, ecommerce, marketplaces, publishing, and digital growth, Wade studies the transition from traditional search ranking toward machine-mediated discovery and selection. He is the creator of the Entity Lock Protocol™ and BackTier Visibility Path™, and host of the AI Visibility Podcast.His current research examines Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity resolution, structured data, machine-readable authority, and the infrastructure determining which people, companies, and sources AI systems understand, cite, include, select, and recommend.Jason AI Wade — Research & Writinghttps://jasonwade.comBackTier — AI Visibility Architecture & Implementationhttps://backtier.comNinjaAI — AI SEO, GEO & AEOhttps://ninjaai.comAI Visibility PodcastSearch “AI Visibility Podcast” on Spotify and major podcast platforms.BioLinks
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  I Tried to Explain AI. I Got It Wrong. So I Learned How It Actually Works. 18.09.2026 2นาที

  What actually happens inside AI?After asking a podcast guest to explain AI—and then realizing my own explanation wasn't quite right—I went back to the basics.In this short episode, I break down AI in plain English: training data, data preparation, model weights, prediction, error, and how a trained model generates an answer from a prompt.I also look at where concepts like ontologies, relationships, probabilistic outputs, and modern AI search fit—and where they don't.No Stanford degree required. I do, however, own the shirt.Why saying “AI is data” doesn't tell the whole storyHow training data is cleaned and preparedWhat model weights actually arePrediction → error → weight adjustment → repeatHow models learn statistical patterns at scaleTraining versus inferenceWhat an ontology actually describesWhy LLMs are probabilisticHow AI search differs from traditional searchWhy modern systems can understand much longer, messier questionsJason T Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast. His work focuses on AI Visibility—how AI systems discover, understand, cite, include, and recommend entities.BackTier — AI Visibility strategy and systemsNinjaAI — AI SEO, GEO, and AEOOpenAI — AI research and modelsStanford HAI — Stanford Institute for Human-Centered Artificial IntelligenceIn this episodeAbout Jason T WadeRelevant Links
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  Jason T ai Wade - Hating AI and tech Revolution and managing transformation - models and agents 18.09.2026 1นาที

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  Life transitions, goals and affluent clients - AI - BackTier 18.09.2026 12นาที

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  why press mentions matter in ai engines 18.09.2026 1นาที

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