BUILDERS

Brandlight is building an AI visibility platform that helps enterprise brands, most of them Fortune 500 companies, monitor and influence how they show up in AI engines across AI search, AI ads, and agentic commerce.

In a recent episode of BUILDERS, we sat down with Imri Marcus, CEO & Co-Founder of Brandlight, to learn how the company won Fortune 500 customers from day zero and turned AI visibility into a CMO priority instead of an SEO project.

Topics Discussed:

• Why calling AI visibility "SEO 2.0" is the industry's most prevalent misconception
• Who owns AI visibility inside enterprise marketing departments, and why Brandlight always starts from the CMO
• How Brandlight answers skeptics who claim AI engine results cannot be influenced
• The advisory board of Fortune 50 CMOs and agency CEOs that opened enterprise doors from day zero
• Why enterprise AI visibility programs require orchestrating close to a hundred stakeholders
• How the education burden in first CMO conversations disappeared between late 2024 and today
• What happens to SEO, brand, legacy search, and Google as search shifts into AI engines
• Reddit's real role in an AI visibility strategy

GTM & Technology Adoption Lessons:

• Refuse the frame that shrinks your category: Imri said the most prevalent misconception is that AI visibility is SEO 2.0. Brandlight positioned it as a completely new marketing channel where traffic just happened to be the first KPI that got hit. The framing decides who owns the problem and how big the budget conversation can be.

• Start the sale where the orchestration lives: The SEO team is involved in every account Brandlight works with, but Imri called a single-department home the wrong place for a big enterprise. Brandlight always tried to start from the CMO and preach a holistic orchestrated approach, because PR, content, partnerships, and even legal teams need to get involved.

• Build the door-opening system before the market exists: Brandlight went after the Fortune 500 from day zero and built an advisory board of over a dozen advisors, people who are or were Fortune 50 CMOs and CEOs of some of the biggest agencies. The advisors made the initial connections, and the first iterations of the platform were seen by Fortune 50 CMOs.

• Treat early education as pipeline, not lost deals: At the end of 2024, the first 15 minutes of every CMO conversation was education. Not all of those CMOs purchased in 2024 or 2025, but it made sense to all of them, and Imri said the vast majority became customers later on.

• Answer skepticism with proof at the largest possible scale: Against the claim that AI answers cannot be influenced, Brandlight presented on the ANA's big stage with its customer Kimberly-Clark: over 12 months, the program took 12 out of 12 brands to lead their categories.

• Anchor urgency in behavior data, not predictions: Imri said that when Brandlight started, less than 1% of search happened in AI engines, and now it is over 50%. Brandlight analyzes billions of data points daily and can show traditionally successful marketing organizations losing share of voice while smaller companies overtake them.

• Position the new channel as additive, not substitutive: Imri argued SEO foundations still matter and brand still matters. AI visibility is an addition, not a substitution, which lowers the perceived risk of adopting it.

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Odynn is an AI-powered, fully modular platform that helps fintechs, banks, card issuers, and travel companies launch embedded travel, loyalty, and rewards programs. The company's flagship product, Awayz, gives financial institutions a white-label travel portal where cardholders can search, plan, and book hotels and flights with side-by-side points, miles, and cash pricing.

In a recent episode of BUILDERS, we sat down with John Taylor Garner, Founder & CEO of Odynn, to learn how the company is taking on a market where Booking, Expedia, and Hopper collectively hold 95% market share by offering financial institutions a personalized, modular alternative to their monolithic competitors.

Topics Discussed:

• How John's background as a points and miles enthusiast led him to identify the gap in embedded travel
• Why John shut down his first startup, Card Curator, and how that experience led to founding Odynn
• How Built Rewards became Odynn's first customer and what they saw that other banks had not yet recognized
• How the market shifted from requiring heavy customer education to prospects who arrive already understanding the problem
• Why US customers immediately understand the cardholder retention problem while European markets require more education
• Where the term "embedded travel" came from and how Odynn uses it to define the category
• The critical marketing decisions John made to reach six different types of buyers
• How Odynn structures its approach across personal banking, business banking, BaaS, credit card affiliates, card issuers, and corporate travel management
• What selling to tier-one banks actually looks like in terms of sales cycle length
• What John learned transitioning from direct-to-consumer to enterprise financial institution sales
• Tactics that have helped shorten long enterprise sales cycles
• Advice for founders selling technology to banks

GTM & Technology Adoption Lessons:

• Solve a problem before customers know they have it: Odynn launched in January 2022 with the hypothesis that airlines and hotels moving toward dynamic pricing would cause points to become less valuable, which would hurt cardholder retention. As John put it, "fixing a problem that nobody really knew that they had was a hard thing to do." The company had to wait for market timing to catch up with its thesis before the sales motion became straightforward.

• Let customers tell you what product to build: Odynn originally focused on the loyalty layer of the problem. Customers like Built Rewards pushed them toward building a full travel portal. "It wasn't just the loyalty space that was broken," John said. "It is the entire experience of redeeming points or just booking travel with cash, either way, on the embedded side was really bad." The product pivot came from listening to customers, not from an internal strategic decision.

• Land a lighthouse customer who already sees what is coming: Built Rewards was Odynn's first customer and remains one today. John credited Built with being "one of the few customers that were savvy enough to know that this was going to be a big problem and ultimately in their favor, because they got ahead of it." Built had team members from airline and bank loyalty backgrounds who could read the trajectory of dynamic pricing before most banks could.

Cents builds software, payments, and hardware for the laundry industry, serving laundromat operators and other commercial laundry businesses. The company is the only venture-backed company operating at scale in a market that most technology companies had overlooked, and has reached approximately one in six U.S. laundromats.

In a recent episode of BUILDERS, we sat down with Alexander Jekowsky, CEO & Co-Founder of Cents, to learn how the company grew to serve roughly one in six U.S. laundromats by building technology for operators that most software companies had written off.

Topics Discussed:

• How Alex discovered the laundromat industry while looking to buy a small business after selling his previous company, a payment system for college campuses
• Why laundromats generate durable cash flows -- 30% margins, 20-plus year equipment lifespans, and leases that can outlive their operators
• How Cents became the only venture-backed company in the laundry software space and what Alex means by "first executor advantage"
• Why 70% of Cents's early sales were inbound -- and what that revealed about how badly the market was underserved
• How trade shows became Cents's "Super Bowl" and why they staffed booths 25 to 50% heavier than planned
• The tension between brand building and product credibility in SMB tech, and the question operators ask when they see a high-profile marketing spend
• Why the laundromat business is "a highly services-based business" despite appearing commoditized on the surface
• How AI and robotics fit into laundry -- and why improving efficiency without improving service quality is "net worse"
• Why Cents describes its role as digitizing, not transforming, the laundromat industry

GTM & Technology Adoption Lessons:

• Build in markets where buyers are already searching. Alex said 70% of Cents's early sales were inbound. The market was ready -- operators were actively looking for product.

• Know who you're actually selling to. Laundromat operators are not the unsophisticated buyers that investors and technology companies assume. Alex said they are often "more cash generative than any of the portfolio companies of a seed or series A investor." The insult embedded in that assumption had left a massive gap -- and Cents walked into it with 70% inbound demand.

• First executor advantage is more durable than first mover advantage. Cents was not first to try selling software to laundromats. But Alex described the company's edge as "first executor advantage" -- being the only company willing to raise the capital and build the balance sheet to actually execute at a level operators were searching for.

• Use events as trust infrastructure, not just brand exposure. Trade shows were Cents's "Super Bowl." The company staffed booths 25 to 50% heavier than planned because Alex believed the people behind the brand were what converted attention into trust.

• Earn the right to innovate before leading with transformation. Alex described Cents's job as "to not transform or change" the laundromat business -- it's to "digitize, create optionality, and earn the opportunity to drive innovation over time."

• Understand why the business looks commoditized but isn't. Two laundromats can use the same equipment, detergent, and labor pool and still deliver entirely different customer experiences. The laundromat business is actually "a highly services-based business." Adoption required understanding that operators cared deeply about how customers felt in the store, not just about technology features.

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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

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

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

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

In a recent episode of BUILDERS, we sat down with Courtney Brigham, Head of Communications and Senior Director at Typeface, to learn how a startup comms team used original research to reach the number two share of voice against competitors in a noisy quarter.

Topics Discussed:

• The big game report: how Typeface inserted itself into the Super Bowl marketing conversation without spending millions
• Why entering the conversation in January, well before the game, mattered more than launch-week tactics
• The Business Insider sponsored partnership and executive LinkedIn campaign behind the report's reach
• How postcards at an executive dinner series revealed that research opens customer conversations, not just media coverage
• Getting executives to post: why one wildly successful leader makes the rest want in
• The one-report-a-quarter cadence, and doing five reports in the first year
• Why Typeface made its research ungated, and how that decision played out
• Running a research project in six to eight weeks instead of several months and several hundred thousand dollars
• Avoiding the state-of-the-industry report format and finding white space instead
• Embargoes, two-week reporter lead times, and measuring impact a month after launch

GTM & Technology Adoption Lessons:

• Find the white space before you publish: Courtney studied the existing report landscape and deliberately stayed away from state-of-the-category formats, because plenty of them already exist. The goal was research that gives media and customers a catalyst for making sense of change, not another crowded franchise.

• Enter cultural conversations early: The big game research was done before the holidays and shared starting in January, well before Super Bowl week when the arena is noisy and everyone is publishing. Timing was one of the top reasons the report became the most successful to date.

• Give media the unexpected story, keep the practical detail for customers: The finding reporters ran with was that the last-mile approval problem at big brands was causing marketing teams to burn out. The same report carried operational detail that fed customer conversations, dinners, and fireside chats. One dataset, spliced into stories per channel.

• Make research a sales asset, not just a PR asset: A team member put designed postcards of the report cover at each place setting of an executive dinner. Executives now request the research ahead of customer meetings, and it gets peppered into their prep. It opened the door for customer conversations well beyond media coverage.

• Seed executive distribution with one success: Typeface started with one marketing leader whose posts drew podcast and media interviews and a stage slot at an Ad Week event. Other leaders then came asking to get their LinkedIn going. Success recruits the rest of the leadership team better than mandates.

• Ungate the research: Making the report ungated opened doors to far more people, and it aligns with how journalists work: they link to findings pages, not landing pages.

• Be patient on measurement: Courtney waits about a month after launch to run a coverage and impact report, measuring share of voice for the quarter, website traffic, email open rates with customers, and media conversations. Research value lands for months, not on launch day.

// 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

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.

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In a recent episode of BUILDERS, we sat down with Gerardo A. Dada, former CMO of Catchpoint, to learn how the company created the internet performance monitoring category and got customers, press, and analysts to adopt its language.

Topics Discussed:

• Why category creation only matters when it actually grows the business
• How Gerardo found the category by studying what Catchpoint did better than anybody else: monitoring the internet itself
• The three factors that made internet performance monitoring stick: a real market problem, full CEO support, and a disciplined language architecture
• How Catchpoint validated the category with interviews with ten of its best customers before launching, and how SAP renamed internal teams using the category language
• Why Catchpoint told buyers their two options were Cisco or Catchpoint
• The newsjacking system behind Catchpoint's best traffic days of the month
• The three-phase analyst relations sequence, from accessible analysts up to Gartner and Forrester
• Giving journalists free survey data and direct access to the same outage alerts customers received
• Where category creation money gets wasted
• Gerardo's book OutPosition, and why strategy, competitive differentiation, and positioning must work as one system

GTM & Technology Adoption Lessons:

• The market decides whether your category exists: Gerardo said, "What matters is the market needs to say that category exists."

• Anchor the category in a real problem, not product uniqueness: "It was not the company making up a category just to try to differentiate, or making a category based on what we think makes our product unique. It was made on a real need in the market."

• Validate the language before you launch it: Catchpoint ran interviews with ten of its best customers and asked how they would react if the company explained itself in the new language. The feedback: "I think that describes exactly what the company does and what is unique." Some customers renamed their teams using the proposed language, with SAP calling teams application and internet performance monitoring, AIPM.

• Evangelize the category, not the product: Catchpoint told customers their two options for solving the problem were Cisco or Catchpoint.

• Build newsjacking as a system, not a reflex: Pre-approved talking points, an analysis team, and executive calendars were ready by six in the morning after a five a.m. wake-up call. Gerardo's filter: "We should only talk to the market when it has something unique and interesting about the news that is happening." Major outages became Catchpoint's best traffic day of the month, with spikes of five to ten times.

• Think about the headline first: Ballpark financial impact estimates gave journalists what they needed. "You need to be prepared with those zingers for the press so that they actually pay attention to what you're saying and increases your likelihood of being quoted if you say something that's short, bold, and interesting to the market. So think about the headline first."

• Sequence analysts by path of least resistance: Start with analysts who are paid to write articles that validate the category, then bring that evidence to mid-tier analysts, and only then to Gartner and Forrester, who by that point are seeing market evidence of a trend.

• Treat journalists as customers to serve: Catchpoint handed the press its full survey data to use freely and gave journalists the same outage alerts that IT teams at Amazon received, instead of watered-down quotes that survive three rounds of legal review.

• Concentrate resources behind fewer concepts: Overdoing category creation wastes money that could promote the brand or product.

// 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