BUILDERS

Welcome to BUILDERS — the show about how founders get new technology adopted. Each episode features a founder on the front lines of bringing new tech to market, sharing how they broke into their industry, earned early believers, built credibility, and unlocked real technology adoption. BUILDERS is part of a network of 20 industry-specific shows with a library of 1,200+ founder interviews conducted over the past three years. For the full network, visit FrontLines.io. Brought to you by: www.FrontLines.io/FounderLedGrowth — Founder-led Growth as a Service. Launch your own podcast that drives thought leadership, demand, and most importantly, revenue.

BUILDERS is a business podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts BUILDERS, a business show with 0 episodes published.

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  BUILDERS

  The friends-of-friends GTM model in cybersecurity | Elad Ben Meir

  Aug 20, 202618 min

  Onit Security builds AI agents for exposure management, helping security teams cut through the noise of tens of millions of vulnerability scanner findings to identify and prioritize the ones that can actually be exploited. The company is built from the ground up on agentic infrastructure, positioning itself as the fifth generation of exposure management tooling in a category that has evolved from basic vulnerability scanners to AI-native platforms over three decades. In a recent episode of BUILDERS, we sat down with Elad Ben Meir , CEO & Co-Founder of Onit Security , to learn how a cybersecurity founder with a marketing background is building go-to-market motion in one of the most competitive markets in tech. Topics Discussed: How Onit Security's AI agents filter tens of millions of scanner findings to identify the ones that can actually be exploited Why exposure management is still an unsolved problem after three decades and how the category has evolved through five generations Why early GTM in cybersecurity is built entirely on relationships and CISO trust How Elad used channel partnerships at his previous company SCADAfence to scale from early stage to growth, and why he's running the same playbook at Onit Security The "mailbox money" channel model where partner sellers identify opportunities and hand off to Onit Security's sales team Why spreading marketing risk across field marketing, brand, digital, social, and employee evangelism is the right approach at the early stage How a branding agency investment Elad almost didn't make became one of the best decisions in the company's history The VC defensibility question: how Onit Security is building its moat as frontier AI models expand into adjacent markets Why technical founders find storytelling the hardest GTM skill to develop GTM & Technology Adoption Lessons: Trust before pipeline: Elad said early GTM in cybersecurity is "all about relationships." The first customers came from existing CISO relationships built over years. The story wasn't about product features alone -- it was about the founding team's personal track record and direct understanding of the problem. One co-founder had their previous company breached by a nation-state actor, and the forensic investigation traced the cause to an unmanaged vulnerability. Friends of friends compounds without effort: The second phase after direct relationships is peer referrals within the CISO community. Elad said the community is "very well knitted and close to one another," and successful deployments generate organic pipeline through peer trust. Channels are how you move from zero to scale: At SCADAfence, the GTM inflection came from strategic channel partnerships. Partners would identify the opportunity, open the door, and SCADAfence's sellers would close. Elad called it "mailbox money." Spread marketing risk early: At the early stage, a data-driven marketing machine is aspirational, not operational. Elad's approach is deliberate diversification: field marketing, brand building, digital, social, personal brand, and employees as evangelists. Branding is a bet, not a line item: Elad almost didn't sign the branding agency contract. The cost didn't justify itself analytically. His co-founders persuaded him: "We're building something big here. Let's bet on something big." He called it one of the best decisions the company has made. Marketing background as both asset and filter: Elad's time as VP of Marketing at a previous company gives him patience with early-stage marketing economics -- no direct correlation between money and results when you're starting -- and zero tolerance for agency narratives that don't hold up. // Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

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  BUILDERS

  The growth engine hiding in a 28-week order set | Andrea Ippolito

  Aug 20, 202623 min

  SimpliFed is a maternal care at home platform focused on virtual breastfeeding and baby feeding support, delivering insurance-covered care from pregnancy through the first year postpartum. In a recent episode of BUILDERS, we sat down with Andrea Ippolito , CEO and Founder of SimpliFed , to learn how the company built a referral engine that is on track to receive referrals representing about five percent of all births in the US this year. Topics Discussed: How Andrea validated that parents would pay out of pocket for feeding support before pursuing insurance contracts Why SimpliFed enters health plans bottoms up through the provider credentialing page rather than selling as a vendor Why the company moved away from a large commercial team and built a credentialing team instead How integration into the 28-week prenatal order set inside OB electronic medical records became the growth engine The three-legged stool behind SimpliFed's growth: patient demand, health plan coverage, and referral partners Why SimpliFed spent three years on a clinical study with UMass Current coverage: roughly 50% of commercial health plans, Medicaid in 12 states, all 50 states commercially, and two national TRICARE contracts What it would take to grow from referrals representing five percent of US births to fifty percent GTM & Technology Adoption Lessons: Validate willingness to pay before chasing contracts: Andrea knew insurance contracts would take years, so the first test was whether parents would pay out of pocket. She recruited local lactation consultants as 1099 providers, ran the service on commercial off-the-shelf software, and let real payment behavior justify the harder infrastructure investments that followed. Know whether you are a vendor or a provider: SimpliFed originally built a large commercial team, then learned it did not need one. As Andrea said, "We are an in-network provider with a health plan. We're not a vendor." Providers enter health plans bottoms up through the provider credentialing page, so the company replaced its commercial engine with a strong credentialing team. Distribution beats proprietary software: Andrea said that with AI, "having proprietary software and all that is not as exciting as it used to be. It's about distribution." SimpliFed's growth runs on EMR and API integrations, not product novelty. Make the referral structured, not handcrafted: Posters and handcrafted outreach were fine for customer discovery, but growth came from integration into the 28-week prenatal order set template inside the OB's electronic medical record. The provider refers without changing their workflow, and the consented, structured referral data makes the motion scalable, predictable, and high growth. Augment the clinician instead of competing with them: With one in three counties lacking access to OB-GYNs and fewer OBs entering practice, SimpliFed positions itself as taking work off providers' plates. OB adoption depends on showing the company complements their care rather than threatening it. Buy evidence early because it cannot be rushed: SimpliFed's clinical study with UMass took three years and a full IRB process. The results, including 16 weeks longer breastfeeding duration and lower PHQ-9 scores at six months, are what make the ROI case to health plans, whose number one postpartum cost driver is maternal mental health. // Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

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  BUILDERS

  The head of AI who had never heard of an ontology | Rob Buller

  Aug 20, 202619 min

  Cyberhill Partners builds and deploys enterprise AI solutions, including Cerebro, an enterprise AI platform Rob describes as AI in a box, and Wolverine, a digital twin product, drawing on eight years of AI work inside the intelligence community. In a recent episode of BUILDERS, we sat down with Rob Buller , Chief Executive Officer of Cyberhill Partners , to learn how the company is bringing intelligence-community-grade AI to enterprise buyers who are still missing the semantic layer that makes AI work. Topics Discussed: Why the core principles of technology adoption have not changed, but complexity has, splitting the buyer across CIOs, CTOs, and CISOs The AI factory model: a runtime AI fabric that plugs into Snowflake, Databricks, Grok, or Gemini instead of shipping compiled, embedded logic How Cyberhill positions Cerebro against Palantir on cost, vendor lock-in, and implementation speed Why Rob has been shocked that heads of AI at multi-billion dollar companies do not know what an ontology is How large incumbents like Workday and Salesforce try to freeze the market when new technology emerges Why Cyberhill picks verticals by following demand into government, automotive, and healthcare Marketing at the local level in secondary markets like Dallas and Denver with billboards, airports, and quarterly steak dinners Partnerships with ServiceNow and Databricks, and government AI work including a global biosurveillance platform GTM & Technology Adoption Lessons: The market cannot buy what it does not understand: Rob has been on roughly a hundred client calls where heads of AI at multi-billion dollar companies could not define an ontology. Without the semantic layer, he argues, you cannot apply context to AI or get traceability. Until buyers understand that, adoption stalls, so education is now part of the sales motion whether Cyberhill wants it or not. Sell the problem solved, not the underlying technology: "People don't care about ontologies and knowledge graphs, they care about can you solve my problem." Cyberhill leads with the business problem and only opens up the technical underpinnings when a buyer wants to know why the product is different. Expect incumbents to freeze the market: Rob says large companies respond to new AI entrants by telling customers they already have AI covered. He admits it does not really work, but it works a little bit. Plan the GTM knowing buyers are hearing "you don't need AI" from vendors they already pay. Let demand pick your verticals: "I've found that business is a lot like water. It finds the lowest level." Rather than forcing a vertical strategy, Cyberhill follows where demand shows up: government, automotive, healthcare. When healthcare demand grew, the company hired a doctor, because subject matter experts are how it enters an industry credibly. Position against the expensive incumbent on speed to value: Rob calls Palantir a great company and a great platform, but points to cost and vendor lock-in. Cyberhill's counter is malleability and implementation speed: "we can implement it in days, not months." Compete where the playing field is level: Instead of fighting Salesforce for attention in New York, LA, and San Francisco, Cyberhill markets at the local level in secondary markets like Dallas and Denver, with billboards, airport advertising, and quarterly steak dinners. Own the category conversation before the window closes: Rob predicts everybody will be talking about the semantic layer in enterprise AI within three years, if not sooner. He also acknowledges the loudest technology does oftentimes win, which makes owning that conversation early a strategic requirement, not a vanity project. // Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

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  BUILDERS

  Breaking into the twenty-year startup graveyard of mortgage lending | Naren Krishna

  Aug 18, 202622 min

  Balerion AI is an agentic AI platform for mortgage loan manufacturing, automating origination work like document analysis, income review, and underwriting for lenders. The company came out of stealth this spring and is backed by a $6M seed round led by Kleiner Perkins, with its platform in production at FM Home Loans, a lender originating two billion dollars in annual loan volume. In a recent episode of BUILDERS, we sat down with Naren Krishna , CEO and Co-Founder of Balerion AI , to learn how the company is breaking into an industry he describes as a twenty-year startup graveyard. Topics Discussed: Why lending has been a startup graveyard for twenty years, and the three failure buckets Naren sees in past mortgage tech companies Why the cost to originate a loan went from about $4,500 in 2007 to around $13,000 in 2026 even as technology improved Why Naren argues the worst thing that happened to the mortgage industry was its vendors, with lenders using an average of 32 point solutions How 100 pages of guidelines became an 800-page PDF that changes every couple of months, and why people spend, not technology spend, absorbed the complexity The stare and compare problem: multiple roles reviewing the same 800 pages How Balerion recruited by marrying AI talent, infrastructure talent, and lending expertise What made FM Home Loans the right pilot partner, and how the case study opens the rest of the market The launch video that led to hiring a VP of sales off a LinkedIn like His color-coded framework for executive updates How the vision expanded from automated underwriting toward loan quality for capital and secondary markets GTM & Technology Adoption Lessons: Study the graveyard before entering it: Naren puts past mortgage tech failures into buckets: companies beholden to macroeconomic shocks because they served only one loan category, companies that forced their own UI or database onto lenders instead of living where underwriters and processors already work, and companies that never understood the distinct needs of independent mortgage banks, depository institutions, and the secondary market. Sell a generalizable system, not a point solution: Lenders use an average of 32 vendors for one manufacturing process, which means underwriters must learn the process, what each vendor does, and how to fill the gaps between them. Pick a pilot partner who already believes: FM Home Loans wanted a world where a mortgage application works like a credit card application, knew it lacked the in-house AI expertise to build it, and originates two billion dollars in annual volume across a broad mix of loan types. Buy credibility in relationship-based industries: Balerion's VP of sales has been in mortgage for decades and opens doors the technology alone cannot. In an industry where everyone claims AI, relationships get the meeting and the technology has to back up the claims. Marketing bets pay off in unpredictable ways: The launch video cost about ten grand and could not be tied to a tangible outcome, until someone liked the LinkedIn post about it and Balerion recruited them. Simplify communication to match attention spans: Naren color-codes executive updates green, amber, and red. Green means do not even look at it; red means you will get a call from me in four hours. Hire against the bet, not the present: Naren spends a quarter to a third of his time on hiring and posts JDs six months ahead of anticipated need, because finding the right person takes three to four months. // Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

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  BUILDERS

  The 90% trust artifact that opened OPAQUE's market | Aaron Fulkerson

  Aug 17, 202619 min

  In a recent episode of BUILDERS, we sat down with Aaron Fulkerson , CEO of OPAQUE , to learn how the company found its market in sovereign AI deployments after discovering that customers would accept 90% verifiability with known gaps rather than waiting for perfect end-to-end confidential computation. Topics Discussed: Why Aaron joined OPAQUE from ServiceNow only after the founders agreed to apply the technology to language models How generative AI systems leak data by architecture, and why that threatens both enterprises and frontier labs The first customer: a European Union cybersecurity agency running analytics and ML How the focus evolved from high tech to regulated industries to sovereign deployments over roughly sixteen months The 2025 realization that a trust artifact with 90% verifiability and known gaps was good enough for customers Turning down a nation-state-scale sovereign deployment in the UAE, and how that honesty turned the entity into a Series B investor Why OPAQUE handed the Confidential Computing Summit to the Linux Foundation What Apple's private cloud compute expansion signals for enterprise AI adoption GTM & Technology Adoption Lessons : Refuse to commercialize the wrong product: Before joining, Aaron told the founders that if the product did not involve language models, it was probably not interesting and would be too difficult to build a commercial effort around. The replatforming from confidential Spark analytics to language models started essentially on day one. Let customers define good enough: OPAQUE assumed it needed end-to-end confidential computation across CPUs and GPUs, but confidential GPU availability through hyperscalers was slow. The company discovered through customers that a trust artifact delivering roughly 90% verifiability of policies, with known gaps, was acceptable. Aaron called it a we-have-been-overthinking-this moment. Honesty about scale limits can win the deal: When a UAE entity wanted OPAQUE to roll out as part of a sovereign stack for a 13 gigawatt build-out, Aaron told them nation-state scale was not deliverable in 2025, since the company had just put its first customers into production. OPAQUE took on two or three enterprise-scale projects instead, and the entity became an investor in the Series B round. Follow the pain into regulated industries: The initial focus was high tech, but OPAQUE learned that regulated industries with strict requirements had the real urgency. Just over a year before the conversation, the company shifted focus to sovereign deployments: healthcare data, banking data, and high tech that is critical infrastructure. Build the ecosystem, not just the brand: OPAQUE hosts the Confidential Computing Summit, which grew until the Linux Foundation became co-host and took ownership. Aaron's view is that no one entity can own the digital sovereignty conversation; OPAQUE needs Google, Microsoft, and Apple building interoperability for the category to exist. Use the anchor example customers already trust: Aaron points to Apple architecting Siri's Gemini-powered processing to be confidential end-to-end. If a basic chatbot was too great a data leakage risk for Apple, enterprises running far leakier AI agents on far more valuable data have their answer. // Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

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