AI Visibility Podcast with Jason T Wade of BackTier

AI Visibility Podcast · Jason AI Wade × Dan Barkhuff

Dan Barkhuff went from Navy SEAL to emergency physician to building a company that connects the dots in public data.

His company, Civly, started with an unglamorous idea: automate political compliance. When customers showed more interest in what the same tools could uncover, the business moved into research—and beyond politics.

Dan joins Jason AI Wade for a conversation about public information, privacy and influence. They explore how AI changes the effort required to investigate someone, what happens when an AI assistant becomes the source people trust, and where useful research meets uncomfortable questions.

They also get into the business of campaigning: donor lists, endless fundraising calls, and Dan’s belief that cheaper tools could make running for office accessible to more people.

Topics include:

• From military service and medicine to an AI startup.

• How Civly’s first product led to a different business.

• Turning scattered records into a picture of a person or organization.

• Research applications in business, sports and journalism.

• AI visibility, reputation management and the potential for misuse.

• Why campaigns spend so much time raising money.

• The difference practical AI implementation could make.

Daniel “Dan” Barkhuff is founder and CEO of Civly, a research and intelligence company serving political campaigns, businesses, athletic organizations and nonprofits. He is a U.S. Naval Academy graduate, former Navy SEAL and Harvard-trained emergency physician practicing in Vermont. He also founded Veterans for Responsible Leadership.

Jason AI Wade of BackTier hosts the AI Visibility Podcast, exploring how AI systems find information, describe businesses and people, and influence the decisions that follow.

• Civly

• Dan’s bio and the team

• AI Narratives

• Research Books

• Data Coverage

• Client Case Studies

• Contact Civly

AI Visibility Podcast with Jason AI Wade

The information was already public. Finding it—and figuring out what it meant—was the hard part.

Civly founder Dan Barkhuff joins Jason AI Wade to explore how AI is changing that equation. A former Navy SEAL and emergency physician, Dan explains how an idea for automating political compliance turned into a research business serving customers beyond campaigns.

The conversation follows the data: from public filings and old posts to donor lists, background research and the answers AI assistants give about people and organizations. Along the way, Jason and Dan discuss privacy, reputation, the potential for manipulation, and what happens when powerful research tools become more affordable.

Dan also offers a different angle on money in politics: what if running a campaign simply cost less?

Inside the conversation:

• The founder story: SEAL teams, emergency medicine and Civly.

• A compliance product that opened the door to research.

• Public information that becomes more revealing when connected.

• Applications across business, athletics and journalism.

• What AI says about you—and efforts to change those answers.

• Donor data, fundraising calls and campaign costs.

• Why Dan sees practical implementation as AI’s immediate opportunity.

Daniel “Dan” Barkhuff is founder and CEO of Civly, an AI-powered research and intelligence company serving politics, business, athletics and nonprofits. A Naval Academy graduate and former Navy SEAL, he studied medicine at Harvard and practices emergency medicine in Vermont. He also founded Veterans for Responsible Leadership.

Jason AI Wade of BackTier hosts the AI Visibility Podcast, exploring how AI shapes discovery, reputation and the information people use to make decisions.

• Explore Civly

• Meet Dan and the team

• AI Narratives

• Research Books

• Data Coverage

• Client Case Studies

• Contact Civly

Field sales creates useful intelligence all day long.

Most CRM systems capture almost none of it.

Will Hamblin discovered that firsthand after leaving a twelve-year career in education and moving into door-to-door card-payment sales. He could visit a business, learn who handled its payments, discover when the current contract expired, hear exactly why the owner was not interested—and then watch most of that information disappear into a notebook, spreadsheet, or memory.

So he started building FieldSpot.ai.

In this episode of the AI Visibility Podcast, Jason T Wade talks with Will about designing an AI-powered sales platform around what actually happens in the field.

FieldSpot combines territory mapping, route planning, renewal intelligence, competitor tracking, voice notes, business-card capture, and AI-assisted outreach. The objective is not simply to store contacts. It is to preserve the context surrounding every real-world sales interaction and make that information useful later.

A central idea in the conversation is that a failed visit may be one of the most valuable interactions in the sales process.

A business owner who says “not interested” may also tell you which competitor they use, when their agreement expires, who controls the decision, and exactly when to come back. Captured properly, that rejection becomes future sales intelligence.

Will and Jason also discuss the connection between AI and data quality. AI can only personalize outreach based on what it knows. Field notes, territory history, competitor information, renewal timing, and previous conversations can give an AI system far more useful context than a conventional contact record.

They also examine a counterintuitive possibility: AI may increase the value of human, face-to-face selling. As inboxes and digital channels become crowded with automated messages, an actual person walking through the door may become more distinctive.

Will also explains how he went from having no traditional software-development background to vibe coding the first FieldSpot prototype, validating the concept, bringing experienced developers into the company, and expanding beyond the UK payments sector where the idea started.

• Why conventional CRMs miss field-sales intelligence
• Why a rejection can become a future sales opportunity
• Competitor and renewal-date tracking
• Territory intelligence and route planning
• Capturing information without slowing down the salesperson
• Voice notes, business cards, and mobile data collection
• Using field context to improve AI-generated outreach
• Why AI output is constrained by the data underneath it
• Vibe coding a working software prototype
• Building a technology startup as a nontechnical founder
• Recruiting technical partners through equity
• The limits of automated outbound sales
• Why physical sales interactions may become more valuable
• Taking FieldSpot into new industries and markets
• The future of AI-assisted field sales

Will Hamblin is the founder of FieldSpot.ai, an AI-powered field-sales CRM and intelligence platform.

Before building FieldSpot, Will spent twelve years in education and became a vice principal. He later entered field sales, selling card-payment services directly to businesses.

Will used AI tools and vibe coding to build the first FieldSpot prototype, then brought experienced technical partners into the company to develop the platform into a production product.

FieldSpot is built around territory intelligence, renewal timing, competitor tracking, mapping, route planning, mobile data capture, and AI-assisted sales workflows.

Jason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier.

FieldSpot.ai
https://fieldspot.ai

Will Hamblin on LinkedIn
https://www.linkedin.com/in/will-hamblin-182a1064/

Jason T Wade
https://jasonwade.com

NinjaAI
https://ninjaai.com

BackTier
https://backtier.com

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 sales
• Capturing intelligence from unsuccessful sales visits
• Renewal dates as a sales signal
• Competitor tracking in the real world
• Territory mapping and route optimization
• Voice notes and frictionless data capture
• Turning field notes into personalized AI outreach
• Why AI is only as useful as the data underneath it
• Vibe coding a startup prototype without being a developer
• Recruiting technical partners through equity
• Why face-to-face sales could become more valuable in an AI-saturated market
• Designing software around how salespeople actually work
• Expanding a field-sales platform internationally
• Where AI-powered field sales goes next

Will 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.ai
https://fieldspot.ai

Will Hamblin on LinkedIn
https://www.linkedin.com/in/will-hamblin-182a1064/

Jason T Wade
https://jasonwade.com

NinjaAI
https://ninjaai.com

BackTier
https://backtier.com

Why Field Sales Needs Better Data: Will Hamblin on Building FieldSpot.ai

Will 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 teams
• Turning rejected visits into future sales intelligence
• Renewal dates and competitor contract tracking
• Territory mapping and route planning
• Voice notes and automatic field-data capture
• AI-assisted personalized outreach
• Building a startup through vibe coding
• Why better data produces better AI output
• The growing value of face-to-face sales
• Designing software around actual field conditions
• Building a technology company as a nontechnical founder
• Expanding FieldSpot internationally
• The future of AI-powered field sales

Will 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.ai
https://fieldspot.ai

Will Hamblin on LinkedIn
https://www.linkedin.com/in/will-hamblin-182a1064/

Jason T Wade
https://jasonwade.com

NinjaAI
https://ninjaai.com

BackTier
https://backtier.com

How I Used GPT, Claude + Lovable to Build Halden

This 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 · #LovableChallenge

Jason 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 Build
https://winnerjasonwade.lovable.app

Lovable
https://lovable.dev

Jason T Wade
https://jasonwade.com

BackTier
https://backtier.com

Email
[email protected]

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 · @Lovable

Jason 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.

Halden
https://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/

Customer Booking
https://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/book

Text Booking
https://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/text

Autopilot
https://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/autopilot

Owner / Admin
https://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/admin

The One
https://id-preview--c3d214a4-07cd-4ac6-9ada-8c56c2d864bc.lovable.app/one-of-one

Lovable
https://lovable.dev

BackTier
https://backtier.com

Jason T Wade
https://jasontwade.com

For 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.

Jack Oujo — From Minor League Baseball to Financial Peace of Mind

Complete 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 description

What 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 background

As 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.

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

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 workflows

Meta Muse, Grok, Base44, Lovable and Cursor

Turning repetitive ChatGPT work into agent prompts

Testing the same task across multiple AI engines

Why new AI platforms are temporarily giving away significant capability

Alexa+ and the rise of agentic shopping

Copilot and Amazon’s position in AI commerce

Email, connectors, lead research and data gathering

Why agents are easier to use than most people assume

The transition from chatting with AI to delegating work to AI

Jason 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 Wade
jasonwade.com

NinjaAI
ninjaai.com

BackTier
backtier.com

AI Visibility Podcast
Available on Spotify and major podcast platforms

Jason T Wade BioLinks