BUILDERS

How Parable achieved a 100% POC win rate in enterprise AI sales | Adam Schwartz

Parable is building an end-to-end intelligence platform that quantifies how organizations spend their collective time—the foundation for measuring real AI impact. With a thousand data connectors ingesting activity and log data across the enterprise software stack, Parable constructs proprietary knowledge graphs that size opportunities and measure outcomes in hard dollars, not adoption metrics. In this episode of BUILDERS, I sat down with Adam Schwartz, Co-Founder & CEO of Parable, to explore why 95% of CFOs see no AI ROI, how his decade running profitable businesses under resource constraints shaped his focus on inputs over outcomes, and why 2026 requires moving AI from CapEx experimentation to measured OpEx.
Topics Discussed:
Why the 95% CFO stat on AI ROI matters as an arbiter of truth, despite backlash
Building knowledge graphs from activity data to quantify collective time allocation across hundreds of people
The fundamental problem: enterprises lack quantitative frameworks for operational efficiency pre-AI
Running parallel ICP experiments to achieve sales-market fit before product-market fit
Why Parable has never lost a POC once leaders see quantitative baselines
Market dynamics creating false signals—unprecedented curiosity without buying intent
The demarcation between companies treating AI as product work versus those waiting for vendor solutions
Why AI transformation demands century-old management structures to be questioned
GTM Lessons For B2B Founders:
Engineer disqualification in momentum markets: Market-wide AI enthusiasm creates pipeline illusion. Prospects will engage indefinitely for education without purchase intent. Adam's framework: "How do we get people to say no to us and not drag us along... They want to keep talking because they want to learn and they want to know what's going on and they are genuinely interested." In enterprise sales during category shifts, build explicit qualification gates that force prospects to reveal resource commitment or disqualify. Extended evaluation cycles feel like traction but destroy unit economics.
Use go-to-market as ICP discovery mechanism: Adam intentionally pursued multiple customer segments simultaneously—different company sizes and AI maturity stages—to let data reveal fit rather than rely on hypothesis. His memo to the team: "We're going to go after these three, you know, many different sizes of companies in order for us to decide like, who we like best." The key insight: get to problem-market fit and sales-market fit validation before optimizing product-market fit. This inverts conventional wisdom but works when TAM is massive and the bottleneck is identifying who feels pain acutely enough to buy now.
Qualify on organizational structure, not verbal commitment: Every enterprise claims AI is strategic. Adam's hard filter: "Who in the organization is responsible for AI transformation? And if you don't have a one person answer to that question, you're not serious." Serious buyers have a named owner reporting to C-suite with dedicated budget and team. Buying Gemini, Glean, or other point solutions isn't a seriousness KPI—it's often passive consumption of AI as a byproduct of existing software relationships. Look for companies doing five-year work-backs on industry transformation and cascading effects on their operating model.
Target post-experimentation, pre-scale buyers: Adam discovered the sweet spot isn't companies beginning their AI journey—it's those who've deployed initial programs and now need to prove value. "The market of people that have started to build AI into their operating model or into their strategy in like a coherent way, there's a team, there's an owner, there's budget... those are the people that we really want to be talking to." These buyers understand the problem viscerally because they're living it. They do product work daily—talking to stakeholders, generating use cases, building briefs, triaging roadmaps. They need your solution to professionalize what they're already attempting manually.
Build measurement into your category narrative: The AI tooling market has over-indexed on soft efficiency claims that won't survive renewal cycles. Adam's warning: "There is too much hand waving around soft efficiency gains... you're going to have to renew and you need NRR and I don't think it's going to be that usage of the tool internally by employees and adoption is going to be enough." The last decade over-rotated to "everything drives revenue" due to VC pressure. This decade requires precision: does your product save time, reduce headcount needs, or accelerate revenue? Quantify it. Partner with measurement platforms if needed. Adam's insight on Calendly is instructive—it clearly saves time, but most buyers can't quantify how much, which weakens renewal economics.
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Don't Miss: New Podcast Series — How I Hire
Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.
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How F2 hires only ex-finance professionals for sales instead of traditional salespeople | Donald Muir

F2 is the AI platform for private markets investors, automating due diligence and portfolio monitoring workflows with agentic AI. After building ARK into a digital banking platform that scaled from tens of millions to tens of billions in loan volume, Donald Muir developed AI technology to automate debt placement on ARK's marketplace. When upmarket institutional lenders requested access to the AI for their entire deal flow—not just ARK's marketplace deals—Donald recognized the technology's standalone value. In this episode of BUILDERS, Donald shares how he's commercializing enterprise-grade AI for an industry where he personally spent years in the private equity bullpen, and how F2 is addressing the reliability and trust barriers that prevent AI adoption in high-stakes financial decision-making.
Topics Discussed
How F2 emerged from ARK's internal need to automate debt marketplace screening memos
The technical approach to eliminating hallucination in Excel-based financial analysis
Replicating private equity's "super day" interview format to prove AI capability with live deal data
Sales team composition: hiring ex-finance professionals instead of traditional sales reps
AI's role in evolving private equity analysts from menial tasks to system operators
Product roadmap from due diligence to portfolio monitoring to deal syndication platform
Maintaining operational independence while preserving strategic alignment with ARK
GTM Lessons For B2B Founders
Solve your own hardest problem first, then productize: Donald built F2's core technology to scale ARK's debt marketplace, focusing on the most difficult engineering challenge—reliable financial analysis of unstructured Excel data—because the marketplace required it. This resulted in technology that foundation models still haven't replicated over a year later. The aha moment came when institutional lenders wanted the AI for all their deal flow, not just marketplace transactions. Organic internal development created category-leading capabilities and validated product-market fit before commercialization. B2B founders should identify which internal operational challenges, if solved, could become standalone products serving the broader market.
Design sales processes that mirror how your ICP evaluates talent: Donald replicated private equity's "super day" format where analyst candidates receive a data room, laptop without internet access, and three hours to produce an LBO model and investment thesis. F2 runs identical timed tests—customers send live deal data rooms under NDA, F2 generates investment committee memos using their templates, and presents same-day results. This proves the AI can perform at the standard funds use to evaluate human analysts they hire 18 months before start dates. B2B founders selling into industries with rigorous talent evaluation processes should reverse-engineer those frameworks into product demonstrations that speak to buyer expectations.
Prioritize credibility over sales experience in technical markets: Donald's entire sales team consists of ex-finance professionals who lived in the seat—no traditional salespeople. These reps can screen-share investment memos created that morning and discuss them authentically with MDs and principals using industry-specific language. After 4.5 years running go-to-market at ARK, Donald teaches sales methodology to domain experts rather than teaching domain expertise to salespeople. For deals averaging half a billion dollars flowing through the platform, buyer credibility outweighs sales polish. B2B founders in specialized verticals should evaluate whether domain fluency or sales pedigree matters more for their specific buyer personas and deal complexity.
Engineer for auditability before optimizing for speed: F2 focused on eliminating hallucination and achieving mathematical accuracy—solving what Donald calls the "reliability and trust" gap—before addressing workflow efficiency. The company name references the F2 keystroke used to audit Excel calculations at 3 AM in the PE bullpen. This positioning directly addresses the barrier preventing AI adoption for investment decisions: LLMs hallucinate, can't do math, and lack auditability. Only after proving the AI produces auditable, trustworthy output did F2 layer on speed benefits. B2B founders building for high-stakes decision environments should identify the fundamental trust barrier and make it the core technical focus before feature expansion.
Leverage institutional knowledge as competitive differentiation: Beyond automating existing workflows, F2 enables firms to pipe in decades of institutional knowledge via API—instantly benchmarking new deals against thousands of historical transactions by vertical, revenue size, leverage levels, and management quality. This transforms screening memos from isolated analyses into context-rich evaluations informed by complete firm history. The AI doesn't just work faster; it has comprehensive context that individual analysts manually searching SharePoint folders could never access. B2B founders should identify where accumulated institutional data creates compounding value beyond point-in-time automation.
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Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How Hubble Network overcame the Bluetooth short-range perception | Alex Haro

Hubble Network is redefining what's possible in satellite connectivity by connecting standard Bluetooth chips to satellites over 500 kilometers away using advanced antenna arrays and digital beamforming. Founded in 2021 by Alex Haro (co-founder of Life360, which IPO'd in 2019 and grew to 80+ million monthly active users) and Ben Longmier (whose previous company's protocol became Amazon Sidewalk after acquisition), Hubble has launched seven operational satellites via SpaceX and is serving enterprise customers across intermodal logistics, off-grid construction, and outdoor recreation. In a recent episode of BUILDERS, I sat down with Alex to explore how Hubble is building the infrastructure layer for global IoT—positioning as the "T-Mobile of space" rather than competing in device markets.
Topics Discussed:
The technical architecture behind connecting Bluetooth to satellites: lowering bit rates, optimizing modulation, and deploying hundreds of antennas for digital beamforming
SpaceX's rideshare program mechanics and what it actually takes to book satellite launches as a startup
Why Hubble deliberately chose to be network infrastructure rather than building hardware for specific verticals
The psychology barrier of overcoming Bluetooth's short-range association—even among experienced RF engineers from Google, Amazon, and Starlink
Strategic focus decisions when facing unlimited market opportunity across construction, agriculture, mining, logistics, and defense
Transparent pricing as a developer-first GTM strategy versus traditional enterprise carrier sales models
The transition from Life360's consumer hardware exploration to founding a satellite networking company
GTM Lessons For B2B Founders:
Choose your competitive layer strategically—infrastructure scales differently than applications: Hubble explicitly positioned as network infrastructure, not a device manufacturer. Alex stated: "We're not focused on building the hardware or devices. We very much view ourselves as a networking company." This allows enterprise customers to integrate Hubble connectivity into their existing devices with just a software change to the Bluetooth chip. The result: each B2B customer can deploy hundreds or thousands of devices to their end users, creating exponential reach. For founders building horizontal technology, consider whether competing at the infrastructure layer—even if less immediately tangible—creates superior unit economics and market leverage versus building full-stack solutions.
Developer-first positioning requires operational commitment, not just marketing: Hubble's pricing transparency wasn't a marketing tactic—Alex described it as "hardcore to our ethos" because their goal is connecting billions of devices. They explicitly modeled after Twilio and Stripe rather than Verizon or AT&T, making it possible for engineers to validate unit economics independently and start free trials without sales conversations. This wasn't debated internally because both co-founders and the early team aligned on this approach. For infrastructure companies targeting massive scale, half-measures on developer experience will fail—the entire go-to-market motion must support self-service validation and transparent economics.
Constraint forces clarity—unlimited TAM demands disciplined ICP filtering: Despite viable use cases across construction, oil and gas, mining, agriculture, supply chain, and defense, Alex emphasized: "In the early stages, focus is the most important thing. Every hour matters and being able to focus matters quite a bit and defocusing yourself can really hurt." Hubble's "sexy hook of Bluetooth to space" generates inbound interest across industries, creating constant pressure to expand. Their active debate centers on which industry leaders are "solving important use cases" with existing customer bases of "hundreds, if not thousands of customers." For founders with horizontal technology, resist opportunistic deals—filter aggressively for partners who provide concentrated distribution rather than one-off deployments.
Physical demonstration collapses credibility timelines for counterintuitive technology: Hubble faced skepticism even from sophisticated RF engineers because of hardwired associations between Bluetooth and short range. Alex noted: "Some of the investors that joined our A or B, they passed on our seed and A because they thought, well, I believe in Alex, but is this really physically possible?" Post-launch with working satellites, the conversation shifted from "is this possible?" to commercial terms. The lesson isn't just "show don't tell"—it's that for technically improbable innovations, rushing to demonstrable proof compresses months of explanation into minutes of validation. Founders should potentially sacrifice feature breadth to reach a single, undeniable proof point faster.
Operational domain expertise reveals infrastructure gaps others can't see: Alex spent years as CTO of Life360 attempting to build connected hardware for families—smart pet collars, GPS watches for kids, fall detectors—but existing networks had "super short battery life, very bulky, no global coverage, way too expensive." He invested in Ben's previous mesh network company and became a close advisor before co-founding Hubble. The insight wasn't theoretical—it came from failing repeatedly to solve the problem with existing infrastructure. Founders should treat operational frustrations in previous roles as proprietary market intelligence: you've already paid the learning cost that competitors will need years to acquire.
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Sponsors:
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Don't Miss: New Podcast Series — How I Hire
Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How Plantd identified business process inefficiencies as a competitive wedge in building materials | Nathan Silvernail

Plantd is reinventing engineered lumber by replacing trees with rapidly renewable biomass, scaling manufacturing technology that costs 100x less than traditional OSB production. With customers including DR Horton and growing demand across furniture, RV, and international markets, Plantd has attracted partnerships throughout the building materials industry. In this episode of BUILDERS, I sat down with Nathan Silvernail, Co-Founder & CEO at Plantd, to explore how his decade at SpaceX shaped his approach to building a capital-intensive hardware company that could transform the $65 billion engineered lumber market.
 
Topics Discussed
Building continuous OSB production systems versus $500M batch presses used by incumbents
Securing DR Horton, furniture manufacturers, and building material companies as early customers
Managing the bifurcation between OPEX-intensive manual processes and CAPEX transitions to AI robotic vision systems
Designing machines for 400,000 panels/year output with sub-one-year payback at scale
Navigating opinion-based building inspection processes where "no two blocks in this entire country build a house the same way"
The strategic calculus of positioning away from climate tech to avoid green premium assumptions
Scaling from pilot production to deploying 25-30 machines to meet current demand pipeline
Achieving 70-layer panel construction versus 6-8 layers in timber-based OSB
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Sponsors:
Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io
The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co
 
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Don't Miss: New Podcast Series — How I Hire
Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.
Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How Axenya achieved cash flow positive before Series A by proving recontract capability | Mariano García-Valiño

Axenya is rebuilding healthcare around chronic disease prevention through AI-powered continuous monitoring. Covering 100,000 lives in Brazil and processing 95 million clinical inferences monthly, the company pivoted from clinical technology provider to healthcare broker - achieving cash flow positive status before their Series A. In this episode of BUILDERS, I sat down with Mariano García-Valiño, CEO and Founder of Axenya, to learn how they spent $3 million building the "perfect product" before discovering no one would pay for it, why they acquired a small broker to unlock their revenue model, and their regulatory-constrained approach to geographic expansion.
Topics Discussed:
Axenya's shift from infectious disease to chronic disease management through wearables and AI The 12-month zero-revenue period after spending $3 million on product development
Why doctors, patients, and health plans all failed as buyers despite clinical validation
The broker acquisition that unlocked their business model Performance-based pricing: zero fees upfront, revenue from cost savings only Regulatory barriers determining expansion (Mexico viable, Argentina impossible, Europe requires model redesign)
Field-force-driven GTM with 30+ salespeople for complex, high-ACV enterprise deals Path to cash flow positive before Series A and scaling playbook for 2026
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Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io
The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co
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Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How Turnstile positioned quote-to-cash for founders who don't know the category exists | Michael Babineau

Turnstile is reimagining quote-to-cash for the modern B2B world, where negotiated agreements create operational chaos that standard pricing never does. After selling Second Measure to Bloomberg, co-founders Michael Babineau and Lillian Chou experienced the irony firsthand: running a data analytics company while managing their own revenue operations through spreadsheets and manual processes. That incongruence became the catalyst for Turnstile, a self-serve revenue platform designed to support sales-led B2B companies from their first negotiated deal through tens of millions in ARR. In this conversation, Michael shares how they're solving the structured data problem that plagues B2B revenue operations, why eliminating custom development forced genuine platform flexibility, and how they're collapsing a traditionally 3-6 month implementation into a self-serve onboarding that takes minutes.
Topics Discussed:
Why negotiated B2B agreements create the structured data problem that breaks revenue operations
Turnstile's compound startup approach spanning quote-to-cash to revenue recognition
The internal ban on custom development that forced true configurability into the platform
How supporting non-standard contracts from day one enables earlier market entry than traditional CPQ
Revenue leakage and "truth drift" between contract terms and actual customer relationships
The rippling-style GTM strategy: start with startups, grow into enterprise with your customers
Positioning challenges when your category exists but your ICP doesn't know it yet
Building for human operators and AI agents simultaneously on the same platform primitives
Agentic dunning and the roadmap toward AI-automated revenue operations
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Sponsors:
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The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co
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Don't Miss: New Podcast Series — How I Hire
Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.
Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How Land Life evolved from selling technology to delivering A-to-Z restoration projects | Rebekah Braswell

Land Life is a technology-driven nature restoration company that restores landscapes degraded by wildfire, overfarming, and urbanization. The company combines proprietary remote sensing, machine learning algorithms, and hardware solutions to deliver end-to-end restoration projects spanning 40 years, monetized through voluntary and compliance carbon markets. With seven validated project design documents on Verra, Land Life has built a business model that requires customers to believe the company will exist for decades. In a recent episode of BUILDERS, we sat down with Rebekah Braswell, CEO of Land Life, to explore how the company navigated from global pilots in Saudi Arabia and the Galapagos to focused geographic operations, evolved its customer base from experimental tech buyers to conservative insurance companies, and repositioned its entire value proposition when climate dropped off corporate priority lists in 2024.
Topics Discussed:
Land Life's shift from selling technology components to customer-driven A-to-Z project delivery 
Remote sensing dashboard that assesses ecological, operational, and economic feasibility before land visits 
Securing environmental attributes while keeping land locally owned by landowners 
Machine learning algorithms for determining optimal tree species, placement, and timing 
Evolution from tech company early adopters to asset managers, financial institutions, and energy providers 
The 2024 market standstill: how tariffs and defense spending displaced climate on corporate agendas 
Strategic repositioning from "climate" to "resilience" language that connects to infrastructure and defense 
Targeting biogenic customers in timber and agriculture with supply shed restoration strategies
GTM Lessons For B2B Founders:
Let customer requirements redefine your product scope: Land Life initially sold discrete technology—cocoon hardware and software tools—to corporations. Buyers consistently responded: "great tech, but we sell shoes online for a living. I need a full project, A to Z." Rather than insisting on their original product definition, Rebekah agreed to plant trees and hire contractors despite "knowing very little at the time what it actually took." The company evolved from a technology vendor to a full-service restoration provider because that's what buyers would actually purchase. B2B founders should recognize when customer feedback reveals a larger market opportunity than their initial product scope, even if delivery capabilities don't yet exist.
Target buyers whose operational experience mirrors your delivery complexity: Land Life struggled with tech companies despite strong initial traction because these customers operated on "much shorter term economic cycles" incompatible with 40-year projects. The company found stronger fit with financial institutions, insurance companies, and energy providers—buyers Rebekah described as "familiar with asset management, familiar with physical operations" who could "identify with some of the cycles that we have to manage in terms of planting windows." She told her team: "you know you have a business when an insurance company starts buying your product. These are conservative buyers." B2B founders with long implementation cycles, physical operations, or asset-intensive models should prioritize buyers with analogous operational complexity rather than chasing early adopters who lack relevant mental models.
Build transparency infrastructure as core product, not marketing: For customers committing to 40-year relationships, Land Life addressed the fundamental trust problem through systematic monitoring and data sharing. Rebekah identified the specific perception barrier: "people have this image that people are just going out and planting trees and there's no accountability." The company's response wasn't better sales materials but "a data focused and transparent process" that continuously validates project performance. B2B founders selling long-term commitments should invest in measurement and reporting systems as primary credibility drivers, recognizing that transparency infrastructure is product, not overhead.
Adapt positioning to buyer priority shifts without abandoning core value: When climate investments "came to a standstill for six months" in 2024, Land Life didn't pivot its business model—it reframed its language. Climate "just dropped on the priority list" as corporations focused on "AI, defense and tariffs." The company shifted to "resilience" positioning that "doesn't use the word climate in it" but connects to infrastructure, defense, and supply chain concerns. Critically, this wasn't invented messaging—Land Life had internally called their engineers "resilience engineers" for years because "you can't bet one climate scenario." B2B founders facing external market shifts should mine existing internal frameworks for language that naturally aligns with new buyer priorities rather than forcing artificial repositions.
Expand value proposition beyond primary category benefit to operational impact: Land Life evolved from pure carbon sequestration sales to showing customers how restoration addresses their core operational risks. For biogenic customers—"people who work in timber, food and agriculture"—the pitch became: "if you're surrounded by a degraded ecosystem, it will eventually encroach" on your supply chain. Rebekah explained: "it's not just enough to have a robust supply chain like your field for example. Great that things are healthy there, but if you're surrounded by a degraded ecosystem, you know it will eventually encroach." This connected restoration directly to supply shed stability and de-risking rather than relying solely on carbon credit value. B2B founders should identify how their solution protects or enhances customers' existing operations, not just deliver category-specific benefits.
Pursue partnerships to reach scale thresholds faster than organic growth allows: Rebekah emphasized that achieving buyer-required scale through partnerships is now essential: "buyers are looking for scale and it is hard for us, who are in nature based solutions and physical assets, to achieve that overnight." She advocated for "constructive and innovative partnerships where you can bring that scale to buyers, whether it's organic or just through partnering" as the path to "play at a different level." The sector signal is clear: "they want bigger volumes, they want stronger suppliers, and that path goes a lot more quickly when you partner, as opposed to trying to do it alone." B2B founders in capital-intensive or operationally complex businesses should view partnerships as strategic accelerators to reach minimum viable scale, not just growth tactics.
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Sponsors:
Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io
The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co
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Don't Miss: New Podcast Series — How I Hire
Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How PredictAP transitioned from founder-led sales to repeatable pipeline after hitting the network wall | David Stifter

David Stifter spent 20 years as head of technology at Colony Capital, managing systems for a $60 billion private equity real estate firm. When a longtime AP specialist retired, the company lost its institutional knowledge for coding complex invoices across thousands of entities and tenant relationships. After a year evaluating RPA, template-based approaches, and early OCR solutions, David recognized that structured historical data—invoices paired with their coding—could train AI models to capture implicit business rules. Five years ago, at 40 with young children, he left his executive role to build PredictAP. The company now processes tens of thousands of invoices monthly for firms including Bridge Investment Group, demonstrating how operational expertise combined with AI can solve problems that pure technology approaches miss.
Topics Discussed
Identifying AI use cases with structured annotated data and human feedback loops 
Moving from CTO buyer to vendor founder and discovering which networks actually convert 
Building repeatable sales motion after exhausting warm introductions 
Technology adoption barriers in real estate and the domain expertise requirement for vertical SaaS 
Hiring sales leadership to scale from founder-led to systematic pipeline generation 
Solving complete workflow integration challenges beyond isolated technical problems
GTM Lessons For B2B Founders
Match technical approach to problem structure, not trend: David identified three critical elements for his AI application: structured annotated data from historical invoice coding, recognizable patterns in implicit business rules, and human review as a feedback mechanism. He notes many founders "try to shove AI, the AI hammer to smash any nail, but they're not always the best use case." Six years ago, before modern LLMs, he used historical invoice-coding pairs as training data—solving the annotation problem that plagued early machine learning. Founders should evaluate whether their problem has the structural characteristics that make a given technology approach viable, rather than applying trending solutions to force market fit.
Network quality reveals itself when you need something: David contrasts two early investors: a former acquisitions executive who promised extensive connections but delivered "not a single callback" after leaving their role, versus an asset manager who generated "hundreds" of leads through genuine relationships. The acquisitions person experienced "an existential crisis" realizing "my network was based upon my ability to have a massive checkbook behind me." Founders should recognize that network strength isn't tested until you're asking rather than giving—those who built relationships through consistent helpfulness rather than transactional power will see different response rates when they launch.
Architect the founder-led to systematic sales transition: After two years of founder-led sales, David "hit that wall" and brought in Steve Farrell, prioritizing experience scaling from $3-5M to $20M ARR over industry-specific expertise. He notes warm intro calls are "very to the point" while cold outreach "starts hostile or skeptical"—requiring entirely different trust-building approaches. The shift required adding BDRs, AEs, and systematic content generation. Founders should hire sales leadership with specific stage experience before network depletion forces reactive hiring, and expect to rebuild positioning for skeptical buyers who lack pre-existing trust.
Integrate solutions into existing workflow infrastructure: David emphasizes the failure mode of optimized point solutions: "They have a perfect solution from the technical problem but it's not going to work for this firm because it's not going to fit into their workflow." He maps the complete experience including integration with existing systems, training requirements, user experience, consistency, and speed. Technical superiority in isolation leads to "problems with adoption and retention." Founders should map every system, process, and stakeholder their solution touches, designing for workflow integration rather than isolated problem-solving.
Sequence customer sophistication as you scale beyond innovators: David's initial customers were "leading edge folks" from his technology network who understood AI potential. As PredictAP matured, sales cycles became "much longer" with more conservative firms requiring higher proof thresholds. He learned that "initial sales have to be very successful and you have to have customers that advocate for you" because mainstream buyers need extensive social proof. Founders should recognize that early adopter ICP differs fundamentally from mainstream buyers—what closes innovators (technology potential) differs from what closes pragmatists (proven ROI and references), requiring distinct positioning and sales approaches for each segment.
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Sponsors:
Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io
The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co
//
Don't Miss: New Podcast Series — How I Hire
Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.
Subscribe here:
https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How Datawizz discovered the chasm between AI-mature companies and everyone else shaped their ICP | Iddo Gino

Datawizz is pioneering continuous reinforcement learning infrastructure for AI systems that need to evolve in production, not ossify after deployment. After building and exiting RapidAPI—which served 10 million developers and had at least one team at 75% of Fortune 500 companies using and paying for the platform—Founder and CEO Iddo Gino returned to building when he noticed a pattern: nearly every AI agent pitch he reviewed as an angel investor assumed models would simultaneously get orders of magnitude better and cheaper. In a recent episode of BUILDERS, we sat down with Iddo to explore why that dual assumption breaks most AI economics, how traditional ML training approaches fail in the LLM era, and why specialized models will capture 50-60% of AI inference by 2030.
Topics Discussed
Why running two distinct businesses under one roof—RapidAPI's developer marketplace and enterprise API hub—ultimately capped scale despite compelling synergy narratives
The "Big Short moment" reviewing AI pitches: every business model assumed simultaneous 1-2 order of magnitude improvements in accuracy and cost
Why companies spending 2-3 months on fine-tuning repeatedly saw frontier models (GPT-4, Claude 3) obsolete their custom work
The continuous learning flywheel: online evaluation → suspect inference queuing → human validation → daily/weekly RL batches → deployment
How human evaluation companies like Scale AI shift from offline batch labeling to real-time inference correction queues
Early GTM through LinkedIn DMs to founders running serious agent production volume, working backward through less mature adopters
ICP discovery: qualifying on whether 20% accuracy gains or 10x cost reductions would be transformational versus incremental
The integration layer approach: orchestrating the continuous learning loop across observability, evaluation, training, and inference tools
Why the first $10M is about selling to believers in continuous learning, not evangelizing the category
GTM Lessons For B2B Founders
Recognize when distribution narratives mask structural incompatibility: RapidAPI had 10 million developers and teams at 75% of Fortune 500 paying for the platform—massive distribution that theoretically fed enterprise sales. The problem: Iddo could always find anecdotes where POC teams had used RapidAPI, creating a compelling story about grassroots adoption. The critical question he should have asked earlier: "Is self-service really the driver for why we're winning deals, or is it a nice-to-have contributor?" When two businesses have fundamentally different product roadmaps, cultures, and buying journeys, distribution overlap doesn't create a sustainable single company. Stop asking if synergies exist—ask if they're causal.
Qualify on whether improvements cross phase-transition thresholds: Datawizz disqualifies prospects who acknowledge value but lack acute pain. The diagnostic questions: "If we improved model accuracy by 20%, how impactful is that?" and "If we cut your costs 10x, what does that mean?" Companies already automating human labor often respond that inference costs are rounding errors compared to savings. The ideal customers hit differently: "We need accuracy at X% to fully automate this process and remove humans from the loop. Until then, it's just AI-assisted. Getting over that line is a step-function change in how we deploy this agent." Qualify on whether your improvement crosses a threshold that changes what's possible, not just what's better.
Use discovery to map market structure, not just validate hypotheses: Iddo validated that the most mature companies run specialized, fine-tuned models in production. The surprise: "The chasm between them and everybody else was a lot wider than I thought." This insight reshaped their entire strategy—the tooling gap, approaches to model development, and timeline to maturity differed dramatically across segments. Most founders use discovery to confirm their assumptions. Better founders use it to understand where different cohorts sit on the maturity curve, what bridges or blocks their progression, and which segments can buy versus which need multi-year evangelism.
Target spend thresholds that indicate real commitment: Datawizz focuses on companies spending "at a minimum five to six figures a month on AI and specifically on LLM inference, using the APIs directly"—meaning they're building on top of OpenAI/Anthropic/etc., not just using ChatGPT. This filters for companies with skin in the game. Below that threshold, AI is an experiment. Above it, unit economics and quality bars matter operationally. For infrastructure plays, find the spend level that indicates your problem is a daily operational reality, not a future consideration.
Structure discovery to extract insight, not close deals: Iddo's framework: "If I could run [a call where] 29 of 30 minutes could be us just asking questions and learning, that would be the perfect call in my mind." He compared it to "the dentist with the probe trying to touch everything and see where it hurts." The most valuable calls weren't those that converted to POCs—they came from people who approached the problem differently or had conflicting considerations. In hot markets with abundant budgets, founders easily collect false positives by selling when they should be learning. The discipline: exhaust your question list before explaining what you build. If they don't eventually ask "What do you do?" you're not surfacing real pain.
Avoid the false-positive trap in well-funded categories: Iddo identified a specific risk in AI: "You can very easily run these calls, you think you're doing discovery, really you're doing sales, you end up getting a bunch of POCs and maybe some paying customers. So you get really good initial signs but you've never done any actual discovery. You have all the wrong indications—you're getting a lot of false positive feedback while building the completely wrong thing." When capital is abundant and your space is hot, early revenue can mask product-market misalignment. Good initial signs aren't validation if you skipped the work to understand why people bought.
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How GreenLite discovered architects were the wrong ICP after 6 months of customer interviews | James Gallagher

GreenLite delivers private construction plan review as an alternative to traditional city permitting processes. After spending six months testing both sides of the construction permitting transaction, the company identified owner-developers as their ICP and built a business model around Florida's privatization legislation—legislation that has now expanded to nine additional states including Texas, Tennessee, and California. In this episode of BUILDERS, we sat down with James Gallagher, CEO and Co-Founder of GreenLite, to explore how his fifth startup leveraged regulatory shifts, rejected workflow software in favor of outcomes, and scaled by targeting chief development officers at enterprise retailers struggling with permitting delays.
Topics Discussed:
How GreenLite discovered architects were heavy users but wrong customers due to two-part sales dynamics
Why owner-developers became the ICP after six months of customer discovery across applicants and agencies
The accidental discovery of private plan review through conversations with Fort Worth and Miami-Dade agencies
GreenLite's platform combining regulatory permissions, licensed AEC professionals, and AI-augmented software
How natural disasters and AEC talent shortages are accelerating privatization legislation nationwide
Cold email strategies that converted enterprise retailers by surfacing acute pain points
GTM Lessons For B2B Founders:
Map two-sided markets to find where purchasing authority and pain intersect: GreenLite pitched a CTO at a major architecture firm who responded positively but said "I just need to talk to my client, my customer." This revealed architects required approval from owner-developers despite being the heaviest product users. James pivoted to owner-developers who "carry the land, carry the construction loans" and feel revenue delays most acutely. The lesson: usage intensity doesn't equal buyer authority. In complex ecosystems, systematically test which party controls budget and feels enough pain to sign contracts independently.
Recognize when procurement cycles kill early-stage validation velocity: Cities explicitly told James their "crazy procurement cycles" made early partnership impractical despite genuine interest. State and local education and government sales require specialized expertise and extended timelines that prevent rapid iteration. James chose to prove the model with private sector customers first. For founders: government can be a lucrative eventual market, but unless you have sled sales expertise and 12+ month runway per deal, validate PMF elsewhere first.
Capitalize on regulatory tailwinds before markets realize they exist: Only Florida permitted private plan review when GreenLite launched in July 2022. By late 2024, nine states passed enabling legislation driven by natural disaster reconstruction needs and talent shortages in city building departments. James positioned GreenLite to ride this wave rather than selling transformation to resistant agencies. Founders should monitor legislative and regulatory changes in their verticals—new compliance requirements or permissions can suddenly open massive TAMs with minimal incumbent competition.
Enterprise cold email converts when you surface non-obvious acute pain: GreenLite cold emailed chief development officers at major retail chains and quick-service restaurants with "Are you missing your openings due to permitting?" The response rate validated that permitting delays—not site selection or construction costs—were a critical path blocker for store rollout velocity. James targeted CDOs rather than real estate or design teams because they own the full development timeline. For enterprise sales: identify the executive accountable for the metric your solution impacts, then lead with how you move that specific number.
Validate outcome-based models before building sophisticated workflow tools: GreenLite's customers rejected "another workflow product or system of record" that required API integrations with their ERPs and construction management systems. Instead, they wanted "faster, more predictable, more transparent permits." James built a viable business delivering finished permits through licensed professionals augmented by software, with the AI sophistication coming later. The business was "super viable well before the product was" by early 2023. For founders in industries resistant to software adoption: test whether buyers want tools to operate or outcomes to purchase—outcome-based pricing can achieve PMF faster and command premium willingness-to-pay.
// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io
The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co
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Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. 
Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM