BUILDERS

How Qualytics Knew it had found product-market fit | Gorkem Sevinc

Qualytics is redefining enterprise data quality by positioning it as a collaborative business function rather than an isolated data engineering problem. Founded at the start of the pandemic by Gorkem Sevinc - a former CTO and CDO who spent years managing reactive data quality firefights - Qualytics emerged from a clear practitioner pain point: writing endless custom rules to catch data issues after they'd already broken dashboards and KPIs. The company raised pre-seed and seed rounds while building with beta customers, then closed a Series A as repeatability patterns emerged in their POC process. Now, as enterprises scramble to operationalize AI initiatives, Qualytics is experiencing explosive inbound demand from organizations realizing their data foundations aren't ready for democratized data access.
Topics Discussed
The practitioner insight that sparked Qualytics: reactive rule-writing doesn't scale
Leveraging existing CTO/CDO networks and PE portfolio connections for beta customers
The evolution from free POCs to paid POCs as a mutual commitment mechanism
Identifying repeatability through week-by-week POC conversion patterns
Building practitioner credibility into the sales motion while hiring for enterprise sales grit
The decision to hire sales and marketing leadership simultaneously post-Series A
Tracking in-product engagement metrics (DQ operations frequency, anomaly detection, rule editing) as churn prevention
Positioning data quality as vertical-specific business problems (premium leakage, regulatory compliance)
The timing advantage: AI adoption forcing enterprises to treat data governance as mandatory infrastructure
GTM Lessons For B2B Founders
Talk to 100 prospects before writing code—even with deep domain expertise: After burning 18 months building a radiology second opinion product that patients didn't want (they didn't even know radiologists were doctors), Gorkem adopted a hard rule: validate with 100 conversations before building. His advantage as a former CTO who lived the data quality problem created false confidence. Practitioners often assume their pain is universal, but buyer awareness and willingness to pay are separate questions. Start with NSF I-Corps-style problem validation: show rough sketches, probe what happened when they hit the pain point, understand how it hurt them financially or operationally.
Repeatability appears in micro-conversions during trials, not just closed-won rates: Gorkem didn't declare product-market fit when deals closed—he declared it when he could predict POC behavior by week. "Week two, I'm expecting this. Week three, I'm expecting this." That predictability enabled ROI calculators and internal champion enablement materials. For technical founders, this means instrumenting your trial or POC to track leading indicators: specific features activated, data volumes processed, number of team members engaged, frequency of logins. When those patterns stabilize across prospects, you have a repeatable motion.
Use paid POCs as a procurement front-loading mechanism, not a revenue play: Qualytics charges nominal amounts for some POCs—not for the revenue, but to get the MSA signed and force both parties through legal/security review upfront. This eliminates the pattern where free POCs succeed technically but die in procurement. Large enterprises often refuse to pay for POCs, which Gorkem accepts—but only if they commit equivalent effort (executive time, cross-functional teams). The paid POC is a qualification tool: if they won't commit anything, they're not a real opportunity.
Hire sales and marketing leadership in parallel and hold them to unified GTM metrics: Gorkem regrets hiring early sales reps before leadership and delaying marketing investment. Post-Series A, he hired both leaders simultaneously and holds them jointly accountable to pipeline generation and velocity—not siloed MQL counts or quota attainment. This structural decision forces collaboration on messaging, ICP definition, and campaign strategy from day one. For technical founders who "figured out" founder-led sales, resist the urge to replicate your motion with more SDRs. Bring in strategic leadership that can build a scalable system.
Instrument product engagement as your earliest churn signal—then intervene immediately: Beyond quarterly NPS and executive QBRs, Gorkem tracks granular product usage: how many data quality operations users run, how many anomalies they discover, how actively they're editing rules. When engagement drops, he doesn't wait—he jumps into the customer's existing weekly meetings to diagnose and course-correct. For B2B founders building complex products with long time-to-value, passive health scores aren't enough. You need active usage telemetry and a low-latency intervention process.
Translate technical capabilities into vertical-specific business outcomes: Gorkem doesn't pitch "data quality for data engineers." He talks about premium leakage with insurance companies and OCC/SEC data controls with banks. This reframing works because buyers recognize their problem, not a vendor category. The shift requires research: understand each vertical's regulatory environment, operational pain points, and the business metrics executives care about. When you walk in speaking their language about their P&L impact, you're not another vendor—you're someone who gets it.
Time your market entry to when "nice-to-have" becomes "must-have": When Qualytics launched, some enterprises called data quality a "nice-to-have." AI adoption changed that calculus overnight. Organizations planning to let 20,000 employees interrogate data through AI interfaces suddenly realized they need robust data governance, quality controls, and cataloging first. Gorkem's timing wasn't luck—he built during the "nice-to-have" phase so he'd be ready when AI budgets made it mandatory. Technical founders should identify the external forcing function (regulation, technology shift, economic change) that will transform their solution from vitamin to painkiller.
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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

Behind the Scenes: How This Healthcare Founder Prepared to Testify Before Congress | Brian Whorley

Brian Whorley, Founder and CEO of Paytient, is rebuilding healthcare's broken payment infrastructure. Paytient enables employers and insurers to front healthcare costs for members who repay over time, interest-free. The company now serves 6,000 employers and powers payment solutions for nearly half of America's 50 million Medicare seniors. In this episode of BUILDERS, Brian reveals his counterintuitive GTM pivot from employers to insurers, why he testified before Congress on healthcare affordability, and how to build in highly regulated markets without fighting the system.
Topics Discussed:
Why healthcare lacks functional buyer-seller dynamics and transparent pricing
The World War II tax quirk that prevents employers from giving healthcare dollars directly to employees
Cash market case studies: Why LASIK prices decreased in real terms since 1998 while maintaining quality improvements
Paytient's unexpected discovery that insurers were better strategic partners than employers
Congressional testimony before the House Committee of Oversight and Government Reform on December 10th
The company's evolution from founder-led employer sales to insurance-first distribution strategy
Launching self-serve for sub-200 employee companies while closing Fortune 100 accounts
How Medicare regulations requiring prescription payment flexibility created a 50-million-person market
GTM Lessons For B2B Founders:
Test enterprise distribution earlier than your assumptions suggest: Brian assumed Paytient needed a million users before insurers would engage. Instead, one of the nation's largest insurers partnered early because they recognized out-of-pocket costs as a critical experience gap they couldn't solve internally. The insurer's product team understood the problem but lacked control over member finances. When building in complex ecosystems, large strategic partners may engage earlier than expected if you solve a problem outside their core capabilities.
Prioritize partners with longer planning horizons: Brian discovered insurers planning 2027-2029 health plans in early 2025, while employers focused on last month's challenges. This planning horizon difference fundamentally changed Paytient's GTM strategy. Insurers became the majority of their business because they could "invest and reshape for the long term" as part of broader strategy. When choosing between customer segments, prioritize buyers who think strategically over those managing tactical, short-term needs—they'll invest in solutions before acute pain points emerge.
Regulatory tailwinds can create massive distribution overnight: A law passed four years after Paytient launched required all Medicare insurers to offer exactly what Paytient provides—prescription cost flexibility with insurer-fronted payments. This regulation instantly created a 50-million-person addressable market. Brian now powers this for "almost half the country." When building in regulated industries, track pending legislation that could mandate your solution category, creating instant distribution through compliance requirements.
Build different GTM engines for concentrated vs. fragmented markets: Healthcare is "a very concentrated industry" at the top 40 insurers, where Paytient focuses enterprise efforts. For the fragmented small business market (under 200 employees), they launched a self-serve platform at patient.com this month, immediately gaining traction with venture-backed employers seeking simple subscriptions. The dual-motion approach—high-touch for concentrated markets, self-serve for long-tail—maximizes coverage without burning capital on inefficient sales motions.
In trust-based sales, delivery quality drives expansion velocity: When Paytient launches with a Fortune 100, "tens of thousands of people have access to patient now." The benefits stack is "sacred and sacrosanct"—a trust-based, relationship-driven sale. Brian emphasizes the product must work "exactly how you said, even better" because performance creates referrals through benefit brokers and consultants. In high-stakes enterprise deployments, your product quality directly determines sales velocity through partner and customer networks.
Navigate regulatory constraints as creative boundaries, not barriers: Brian's core advice for healthcare founders: "You have to work with the system as it is." Many founders approach healthcare "as antagonist" with solutions "too foreign or too different" that threaten the status quo. Instead, innovate within existing regulatory and operational frameworks. There are "plenty of space" and "data requirements for how healthcare can work today" to build billion-dollar businesses while respecting industry structure. Fighting the system guarantees slow adoption; working within it enables scale.
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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:
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How Trener Robotics partnered with 3 of the 5 largest robot OEMs | Asad Tirmizi

Trener Robotics is solving a fundamental problem in industrial automation: the 5 million robotic arms deployed globally operate without intelligence, relying on 60-year-old procedural programming methods. With $38 Million in total funding—including a just-closed $32 Million Series A—the company compressed an 18-month journey from pre-seed to Series A by focusing ruthlessly on CNC machine tending. In this episode of Category Visionaries, I sat down with Asad Tirmizi, Founder of Trener Robotics, to unpack how 14 years of research in robotics and AI converged with market timing to create what judges recognized as this year's biggest innovation in machining—despite the founding team having zero machining expertise.
Topics Discussed:
Why Trener Robotics chose CNC machine tending over higher-visibility applications like airplane cleaning
The capital efficiency trade-offs between sales cycle length, development complexity, and runway
Partnering with three of the five largest robot OEMs controlling 4.3 million of 5 million deployed units
Expanding to six countries (Norway, Denmark, Sweden, Portugal, Spain, US) through integrator networks
Converting technical curiosity into closed deals in a risk-averse industry with 60-year-old workflows
Building training materials in Portuguese for markets the founding team has never visited
GTM Lessons For B2B Founders:
Sales cycle length determines survival, not TAM size: Trener Robotics rejected compelling applications with massive TAM like airplane cleaning because sales cycles would burn through runway before reaching scale. Asad was explicit: "If your sales cycle is too long, your funding is too less and your development time is too much, that's it, you're out of business." They chose CNC machine tending specifically because manufacturers already budget for robots, understand ROI calculations, and have existing vendor relationships. Calculate your actual time-to-close from first meeting to signed contract, multiply by customer acquisition cost, and build your runway model around that reality—not the TAM slide in your deck.
Niche dominance beats horizontal expansion every time: Despite having technology capable of 100+ applications, Trener Robotics committed to machine tending exclusively. Asad's framework: "Making 100 skills is easy. Distributing 100 skills, maintaining 100 skills, marketing hundred skills—that's where most startups break when scaling, not when incubating." The constraint forced them to become the definitive solution for one workflow, enabling repeatable sales motions and concentrated marketing spend. Most founders intellectually agree with focus but fail operationally—they take revenue from adjacent use cases "just this once." Don't. Pick your beachhead, win it completely, then use that cash cow to fund expansion.
Industry awards are underutilized credibility hacks: Trener Robotics won the Machine Tool Innovation Award—the machining industry's most prestigious recognition—despite being roboticists with no machining background. This wasn't luck. They studied what innovations historically won, trained their models on data that would produce award-worthy results, and positioned the submission around industry pain points. The award opened OEM partnership conversations that would have taken years otherwise. Identify the 2-3 awards that matter in your category, reverse-engineer what wins, and build your product roadmap accordingly. Third-party validation converts skeptical enterprise buyers faster than any sales deck.
Channel partner economics need structural win-win design: Trener Robotics secured partnerships with three of the five largest robot OEMs (controlling 86% of deployed units globally) by solving a specific problem: OEMs sell hardware but lose recurring revenue to system integrators who program robots. Trener Robotics' AI models let OEMs capture software subscription revenue while reducing integrator programming costs. Asad acknowledged they're still learning: "I would not by any stretch of imagination say we have proven how good we are in managing channel partners. It's a journey we are on." But the structural economics work because both sides make more money. When designing channel programs, don't just offer margin points—restructure the value chain so partners access new revenue pools they couldn't capture before.
Interest signals are worthless without conversion timeline mapping: Asad's painful admission: "Interest does not mean sales. Pilots do not mean sales. Even letter of interest or contracts to test your equipment does not mean sales." As a technical founder, he initially conflated technical validation with buying intent. The fix: obsessively measure time between interest signal and closed deal, then segment by customer type, deal size, and decision-maker level. Only after mapping this could they accurately forecast and avoid the "too much time in the gray area of interest turning to sales" trap. Build a conversion funnel that tracks days-in-stage, not just stage progression percentages.
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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:
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How Autonomize AI built credibility with healthcare buyers | Ganesh Padmanabhan

Autonomize AI is transforming healthcare infrastructure by eliminating administrative waste and reimagining how health enterprises operate. Covering 150 million of the 330 million lives in the United States and powering three of the five largest health enterprises, Autonomize AI has found traction by solving healthcare's hardest problems first. In this episode of BUILDERS, we sat down with Ganesh Padmanabhan, Founder & CEO of Autonomize AI, to explore how he built an AI platform from the ground up for healthcare—not by retrofitting existing technology, but by immersing himself in the industry's unique challenges and building solutions that address the fundamental inefficiencies plaguing the system.
Topics Discussed:
The origin story of launching during COVID with conviction around unstructured data Landing the first enterprise customer with a PowerPoint and prototype before writing production code The evolution from clinical trial patient matching to powering major health enterprises Why solving the hardest problems first created faster traction than targeting easy wins Building credibility as an outsider by leveraging past successes and being honest about failures The distinction between building AI for healthcare versus building AI from within healthcare Scaling from a $10,000 pilot to multi-million dollar ARR with deep customer immersion Why healthcare is fundamentally a trust equation, not a technology problem The future vision of an AI-native health enterprise operating system
GTM Lessons For B2B Founders:
Don't write code until you have a signed deal: Ganesh didn't write production code until securing his first enterprise customer. He used a compelling pitch deck and an expensive prototype stitched together from cloud solutions to demonstrate feasibility. Once the deal was signed at $150,000 annually, they built the sustainable version while delivering value with the prototype. This approach validated real demand before significant investment.
Solve the hardest problem, not the easiest one: Counterintuitively, Autonomize AI found faster traction by tackling the most difficult challenges in healthcare. Ganesh explains, "The simplest way to actually get traction, solve the hardest problem that's out there. If you do that and you can actually solve it...if the problem is big enough for them to move, they will." Hard problems often have fewer competitors and more desperate buyers.
Wait for pattern recognition before scaling: Ganesh knew he had a business when the second and third customers requested exactly what the first customer bought. He waited for this repeatable pattern before raising a seed round, ensuring he wasn't just solving one customer's unique problem but addressing a genuine market need.
Immerse deeply in one customer before broad expansion: Autonomize AI spent 12 months becoming better experts on their first major enterprise customer's systems than the customer's own internal teams. This deep penetration transformed a $10,000 pilot into millions in ARR and provided invaluable learning that shaped their entire platform approach. The investment in one relationship paid exponential dividends.
Build from the industry, not for the industry: Ganesh's advice is clear: "Don't build AI and bring it into healthcare. Come into healthcare and build the AI." Most companies fail by retrofitting technology into healthcare's nuanced environment. Success comes from immersing yourself in the specific industry, understanding its unique constraints and trust requirements, then building solutions from that foundation.
Leverage past credibility through specific storytelling: As an industry outsider, Ganesh built trust by sharing concrete past successes: growing Dell's convergent infrastructure business from zero to $1.3 billion in five years, working with major healthcare clients in previous roles. He also shared failures openly, creating authentic credibility. He notes, "People learn more from their successes than from their failures...you learn what to do then what not to do."
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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 Heka Global positioned web intelligence as a fourth fraud detection layer to avoid vendor comparison | Idan Bar Dov

Identity fraud spiked 148% in 2025 as AI democratized identity fabrication. Financial institutions now face a fundamental question: Are you dealing with a real human? Heka Global is addressing this with web intelligence—analyzing digital footprints like connected applications rather than traditional signals. In this episode of BUILDERS, I sat down with Idan Bar Dov, Co-Founder & CEO of Heka Global, to explore how his company created a fourth layer in the anti-fraud stack and why legacy identity verification systems are becoming liabilities rather than assets.
Topics Discussed: 
The emergence of "fraud as a service" and why consumer-facing attacks replaced traditional enterprise breaches 
How web intelligence works: validating identity through connected applications and digital footprints 
The anti-fraud tech stack: credit bureaus, biometrics, transaction analytics, and web intelligence as distinct layers 
Why heads of fraud expand budgets rather than replace vendors, and what causes solutions to get kicked out 
The partnership sales model: navigating vendor management complexity and red tape in financial institutions 
Why 10-person dinners and fraud simulations outperform traditional enterprise marketing 
How Barclays and Cornerback backing solved the chicken-and-egg problem for a data product 
Why specific fraud prevention messaging (account takeover, synthetic identities) beat investor credibility
GTM Lessons For B2B Founders:
Target ICP based on liability exposure, not just industry fit: Heka narrowed beyond "financial institutions" to lenders who bear immediate losses from fraud—companies like LendingPoint, Avant, and Upstart. These buyers feel the pain acutely versus institutions with reimbursement terms who can deflect liability. Idan's insight: "We need the client to feel the pain just as much as we see it. That means we want them to see the liability." Map your ICP not just by vertical or size, but by who internalizes the economic impact of the problem you solve.
Frame your product as a new stack layer, not a competitive replacement: Heka positioned web intelligence as the fourth distinct layer after credit bureaus, biometrics, and transaction analytics. This became their second pitch deck slide, showing logos of each category. The result: buyers stopped comparing Heka to existing vendors and started evaluating complementary value. When entering mature markets, resist the urge to claim you're "better than X"—instead, define where you fit in the existing architecture and why that layer didn't exist before.
Abandon spray-and-pray for sub-1,000 TAM markets: Heka tested Lemlist flows with targeted LLM personalization and saw zero pipeline from it. Idan's take: "When you're selling to maybe a thousand financial institutions, that's it. You can be super specific when you target them." For enterprise plays with small addressable markets, allocate zero budget to automated outbound. Focus entirely on warm introductions, relationship nurturing, and becoming known to every relevant buyer through content and community.
Leverage investor networks to break data product cold-starts: Data products face a critical barrier—you need customer data to prove value, but need proven value to get customers. Heka solved this by bringing on Barclays and Cornerback as investors who vouched for the team's capability to "do magic and create a new layer." Their backing convinced risk-averse financial institutions to pilot. If building a product requiring customer data for training or validation, prioritize strategic investors who can credibly de-risk early adoption for target buyers.
Build trust through teaching, not pitching: Heka hosts dinners and fraud incident simulations with ~10 heads of fraud per session. Critical detail: they never pitch Heka in these forums. Idan explained the approach focuses on "building a community around Heka and how people engage with your product and you being a thought leader while listening." In high-trust categories, educational forums where you facilitate peer learning without selling create stronger pipeline than direct pitching.
Structure partnerships with active enablement and incentive alignment: Idan's key lesson: "Partnerships are not synonymous to distribution channels." Heka requires partner sales teams to join early customer conversations to learn the pitch, provides detailed API and output training, and ensures partners get extra compensation for selling non-core products. Without this, partners lack motivation to prioritize your solution. Structure partnerships as true collaborations requiring ongoing enablement investment, not passive referral channels.
A/B test credibility signals versus technical specificity: Idan assumed messaging around Barclays backing would crush, while specific fraud prevention content (account takeover, synthetic identity detection) was an afterthought. The data showed 10x better response to technical specificity. The lesson: sophisticated buyers in technical categories respond to precise problem-solving over brand credibility. Test whether your audience values "who backs us" or "exactly what we do" before defaulting to investor logos and validation.
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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 AskElephant achieved 400% growth with zero marketing spend | Woody Klemetson

Woody Klemetson scaled sales from 100 people at Divi to 350 at Bill.com post-acquisition, then walked away to build something harder: infrastructure for hybrid AI-human revenue teams. At AskElephant, he's tackling the problem that every revenue leader faces but few can articulate—how to actually implement AI in revenue operations when your systems weren't built for it. With zero marketing spend, AskElephant hit 400% growth through pure referral motion and converts 85% of pilots to production (versus single digits industry-wide). Woody breaks down why most "AI-ready" companies aren't, how to structure pilots that actually ship, and what it takes to hire sellers who orchestrate agents instead of relying on armies of support staff.
Topics Discussed:
Post-acquisition culture collision: the cost of moving too fast versus too slow Why "AI readiness" is usually one person at a company, not the organization 
The 27-agent CRM system that delivers 5% forecast accuracy without human input 
Revenue outcome systems as category evolution: solving for predictability across disconnected tools 
Pilot-first GTM that converts at 85% by starting with one-minute-per-day wins 
Partner-led distribution through consultants evolving from slideware to implementation 
Hiring ops-minded sellers who code: over half of non-engineers using Cursor daily 
The PLG expansion coming in 2025 and why traditional demand gen is getting tested alongside door-to-door
GTM Lessons For B2B Founders:
Culture integration requires explicit deceleration early: Woody's team assumed Bill.com wanted their aggressive startup velocity immediately post-acquisition. They didn't slow down to map cultural differences, causing "whiplash" across 350 people. The specific mistake: not creating space to understand Bill's processes before challenging them. Even when acquired for your approach, the first 90 days should be listening and mapping, not executing. Only after understanding their system can you effectively challenge and merge cultures. This applies whether you're acquiring or being acquired—the cultural work is non-negotiable and front-loaded.
Diagnose AI readiness by system documentation, not enthusiasm: Most companies think they're AI-ready because leadership wants AI. Reality check: if your teams haven't documented their systems and processes, AI has nothing to learn from. AskElephant starts some customers with basic dictation—not because it's revolutionary, but because it's the prerequisite to anything meaningful. The diagnostic question: "Walk us through your current customer journey." If the answer is "we have sales stages," you're not ready for automation. You need documented systems before AI can execute them. Start by having AI observe and document before it acts.
Build agents incrementally to compound context: AskElephant runs 27 different CRM agents that collectively deliver 5% forecast accuracy. This wasn't built in one sprint—it took 40 hours of training and context-building. Each agent handles a specific job: contact creation, data enrichment, ICP scoring, churn monitoring, stage updates. The misconception founders have: AI should work perfectly from the first prompt. The reality: you build agents brick by brick, each one learning from the previous context layer. This is why their forecasting works—because 27 agents watching different signals together create accuracy that one "smart" agent can't.
Pilot conversion at scale requires deliberately small scope: Single-digit pilot-to-production rates happen because teams scope too big. AskElephant's 85% conversion comes from "dream big, implement small." First pilot: automated CRM notes. Then: notes humans wish they'd written. Then: automated field updates. Each step saves minutes, builds trust, proves value. Woody's framework: if you're not saving one minute per person per day in your first pilot, you've scoped wrong. The goal isn't to wow with ambition—it's to ship something that works perfectly, then expand from proven trust. Their customers average 27 hours saved per week per person, but none started there.
Revenue outcome systems emerge from tool sprawl failure: Every revenue leader uses 15-20 disconnected tools trying to make revenue predictable. The category insight isn't "operating systems"—it's that companies care about outcomes, not operations. AskElephant's positioning: we focus on the outcome (predictable revenue), not just the operating infrastructure. This distinction matters because it shifts the conversation from technical plumbing to business results. When creating categories, find the frame that makes the buyer's problem visceral and your solution inevitable, even if you're solving similar problems as others in the space.
Partner-led GTM turns consultants into distribution: AskElephant's entire growth came through partners: Salesforce/HubSpot consultants becoming AI strategists, sales coaches extending from training to implementation. The unlock: these partners needed a way to deliver lasting value beyond slideware. Previously, a coach would train your team and leave. Now they implement AI systems that hold teams accountable to the training, creating longer engagements and better outcomes. For founders: identify services providers whose business model gets dramatically better by incorporating your product. They become your sales force because you make them more valuable to their clients.
Hire for orchestration capability, not pure sales skill: Over half of AskElephant's non-engineering team uses Cursor daily. Woody hires "ops-minded" and "tech-minded" sellers who can manage AI agents alongside human work. The old model: silver-tongued seller + solutions engineer + 27 support people. The new model: one seller orchestrating 27 AI agents. These reps don't build lists, don't create SOWs, don't write product scopes, don't need SEs for demos. But they still need human connection skills—listening, curiosity, presence. The hiring filter: can this person think in systems and implement technical solutions while maintaining high-touch relationships? If they can't code enough to orchestrate agents, they can't scale in this environment.
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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

Why Portnox's CEO refuses to measure Net Promoter Score | Denny LeCompte

Portnox is an enterprise access control platform that eliminates passwords and enforces zero trust security. The company was bootstrapped for over a decade, plateauing at a few million in ARR before investors brought in Denny LeCompte as CEO four years ago. Since then, Portnox has grown 8x. But this episode isn't about that growth story. Denny, a former cognitive scientist and professor who taught psychometrics, uses his scientific background to systematically dismantle Net Promoter Score—explaining why it's methodologically flawed, how it misleads organizations, and which metrics actually correlate with business performance. This is a contrarian take grounded in measurement science, not marketing opinion.
Topics Discussed:
The fundamental psychometric flaws in NPS: why single-item questionnaires are unreliable and why throwing out 7s and 8s violates basic statistical principles
How NPS scores fluctuate based on survey UI presentation independent of actual customer sentiment
Why NPS creates incentive structures that encourage gaming rather than improving customer outcomes
The case for gross revenue retention and net revenue retention as the only ungameable metrics that matter
How measuring human behavior changes that behavior (the Heisenberg principle applied to business metrics)
Why investors care about retention rates above 90% but don't ask about NPS scores
GTM Lessons For B2B Founders:
Single-item questionnaires violate measurement principles: Denny's background in psychometrics immediately flagged NPS as unreliable. One-item measures lack the redundancy needed for reliability, and the methodology of throwing out middle responses (7s and 8s) then subtracting detractors from promoters is statistically nonsensical. At a previous company with thousands of data points, he observed NPS scores drop and rise based solely on how the survey rendered on the page—no business changes, just UI differences. When presentation affects your metric independent of the underlying construct, your instrument is broken. Founders with technical backgrounds should trust their instincts when measurement methodology feels scientifically unsound.
Compensation drives behavior more than metric accuracy: Portnox structures customer success compensation as 50% gross revenue retention and 50% net revenue retention. These are determined by finance and can't be manipulated. Denny had to rein in his CS team when they became overly focused on time-to-value because any number you give a team becomes their obsession. With NPS, teams game survey timing, cherry-pick recipients, and optimize for score rather than outcome. This is the Heisenberg principle applied to business: measuring changes the behavior. Choose metrics where gaming the number aligns with improving actual business outcomes.
Investors evaluate retention rates, not satisfaction surveys: When Denny presents gross retention above 90%, investors don't ask about NPS. Renewal behavior reveals actual satisfaction—customers voting with budget rather than survey responses. The test for any metric: "What are we doing differently if this number is up versus down?" If it doesn't drive distinct actions or reveal information not already visible in financials, eliminate it. NPS often becomes a number that exists because "we've always measured it," inherited from previous leadership without questioning its utility.
Question inherited practices ruthlessly: NPS gained adoption through Harvard Business Review credibility in 2003 and consulting firms building practices around it. The promise of "one number you need" appeals to executives wanting simple solutions. But herd behavior—"everyone else measures it"—perpetuates bad methodology. Denny's advice to founders stuck with NPS: give your team something else to focus on (gross retention is straightforward: don't let customers churn), then stop doing it. Sometimes you need to point to external validation to break internal momentum. The question isn't whether NPS correlates somewhat with growth—it's whether better alternatives exist that can't be gamed.
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Why Civ Robotics trains construction engineers into sales reps instead of hiring salespeople | Tom Yeshurun

Civ Robotics is automating construction layout—the process of translating blueprints into physical markers on job sites—using autonomous ground robots instead of traditional surveying crews. Founded by civil engineer Tom Yeshurun after he spent $2 million on a four-person surveying team for a single project, Civ has scaled from initial concept to deploying robots across the United States, Australia, Europe, and the Middle East, with 12 robots currently operating in Saudi Arabia alone. In this episode, Tom breaks down his tactical approach to product-market fit, why he pivoted from aerial drones to ground vehicles based on customer feedback, and how he's building sales teams by recruiting construction professionals rather than traditional sales reps.
Topics Discussed:
How Tom identified the construction layout automation opportunity while managing $120-500 million infrastructure projects
The two-year pivot from aerial drones to ground robots after target customers cited safety concerns
Market differences between Israel and the US: subcontracted surveying firms versus in-house EPC operations
Converting tier-one contractors like Bechtel and Primoris through persistence and geographic proof points
The product development framework: one request = document, two requests = build, three requests = should be done
Transitioning from paid digital ads to SEO/AIO optimization with measurable improvements in inbound quality
Using AI workflows to audit website metadata and align content with buyer personas instead of investor messaging
Sales hiring strategy: grooming construction engineers into customer success and sales roles versus hiring pure sales talent
International expansion through remote deployment and a LinkedIn-driven sale that generated 12 robots in Saudi Arabia
Product roadmap from layout automation to machine guidance and full construction equipment autonomy
GTM Lessons For B2B Founders:
Interview customers in your actual target geography, not just accessible markets: Tom built his initial prototype after interviewing Israeli and European companies, but the US market operates fundamentally differently—EPCs like Bechtel and Primoris handle surveying in-house due to volume, while Israeli EPCs subcontract to surveying firms. This changed the buyer persona, sales motion, and value proposition entirely. When he finally interviewed US companies, the feedback was immediate and actionable. Don't optimize for interview convenience—validate where you plan to sell.
Let technical decisions be customer-driven, not engineering-driven: Tom's team spent two years developing an aerial drone solution because it was technically more complex and exciting for engineers. Three early adopters said they liked the concept but feared the drone—if it was ground-based, they'd reconsider. Tom scrapped two years of development and rebuilt for ground vehicles. His takeaway: bring both options to target customers before committing development resources. Engineering preferences create technical risk; customer preferences create market risk.
Use the "one-two-three rule" for product prioritization: Tom's framework eliminates guesswork in product roadmaps: one customer requests a feature, document it; two customers request it, begin development; three customers request it, it should already be shipped. This prevents building "cool features" that product managers or engineers want but customers don't need, and ensures development resources map directly to revenue opportunities.
Deploy proof before the pitch to collapse enterprise sales cycles: When a major contractor asked if Civ's robot could handle Texas mud, Tom responded that they already had a robot deployed "literally a mile away" on an adjacent project. That proximity proof turned a Wednesday discovery call into a Monday deployment, followed by a one-month trial and conversion to a customer now running 15 robots. For hardware or complex B2B sales, having operational deployments near prospects eliminates the biggest objection: "will this actually work in our environment?"
Position yourself as a peer, not a vendor: Tom doesn't introduce himself as CEO or founder in sales conversations—he leads with his background as a civil engineer and field engineer who managed the same types of projects his buyers manage. This reframes the conversation from vendor-buyer to peer-to-peer, making it easier to discuss pain points candidly. In technical industries, domain credibility matters more than sales technique. If you lack it personally, your customer-facing team must have it.
Audit your website metadata as a conversion optimization lever: Tom discovered his road robot product page was showing solar farm videos in link previews because metadata wasn't optimized per product line. His team systematically reviewed every page's metadata, primary content, and video assets to ensure alignment with the specific buyer viewing that page. This granular optimization improved inbound quality measurably. Most B2B companies ignore metadata entirely—it's a high-leverage, low-effort fix.
Hire from industry for sales, hire generalists for marketing: Tom's board challenged him to "duplicate himself" as the company's best seller. His answer: recruit former construction project managers and field engineers who already communicate effectively and understand buyer pain points, then train them on sales process. For marketing, the talent pool with construction automation experience is too small, so he hired a generalist. This isn't about industry knowledge being unimportant—it's about recognizing where domain expertise is essential (customer-facing) versus learnable (content creation).
Create reciprocal value loops with influential customers: One customer produces professional-quality content about Civ's robots because showcasing innovation differentiates him with his own clients. Tom reciprocates by cutting the subscription price by 50%, explicitly framing it as "you're a great influencer and helping us spread the word." This relationship generated Civ's Saudi Arabia opportunity—12 robots sold—when the customer's LinkedIn post drew a comment from a prospect. Identify which customers benefit from being seen as early adopters, then structure commercial terms that reward amplification.
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Meta Description:
Tom Yeshurun, Co-Founder & CEO at Civ Robotics, shares his framework for product-market fit, hiring construction pros into sales roles, and scaling robotics deployments internationally on BUILDERS.

How Collate turned 12,000 open source users into an inbound sales engine | Suresh Srinivas

Collate is building a semantic intelligence platform that unifies fragmented metadata tooling across the modern data stack. With 12,000+ community members, 3,000+ open source deployments, and 400+ code contributors, the company has proven that open source can be a systematic GTM engine, not just a distribution tactic. In this episode of BUILDERS, I sat down with Suresh Srinivas, Co-Founder & CEO of Collate, to explore his journey from the Hadoop core team at Yahoo, through founding Hortonworks, to architecting data systems processing 4 trillion events daily at Uber—and why that experience led him to rebuild metadata infrastructure from scratch.
Topics Discussed:
Why platform builders at Yahoo and Hortonworks struggled to drive business value despite powerful technology
The metadata fragmentation problem: how siloed tools lack unified vocabularies and end-to-end context
Collate's contrarian decision to build Open Metadata from zero rather than spinning out Uber's internal tooling
Engineering an open core GTM model that generates nearly 100% inbound sales from technical practitioners
Scaling community contribution: moving from feedback loops to 400+ code contributors
Hiring a CMO to translate technical value into business-leader messaging without losing practitioner trust
The convergence thesis: structured data, knowledge graphs, and semantic layers as the foundation for reliable AI
GTM Lessons For B2B Founders:
Architect your open source for GTM leverage, not just distribution: Suresh built Open Metadata as a unified platform consolidating data discovery, observability, and governance—previously fragmented across multiple tools. This architectural decision created natural upgrade paths to Collate's managed offering. The lesson: open source architecture should solve a complete job-to-be-done that reveals commercial value through usage, not just demonstrate technical capability.
100+ daily practitioner conversations beats any user research: Collate maintains ongoing dialogue with their community across Snowflake, Databricks, and other integrations. Suresh called this "a product manager's dream"—immediate feedback on what breaks, what's missing, and what workflow improvements matter. For infrastructure startups, this beat rate of validated learning is nearly impossible to replicate through traditional customer development.
High-velocity releases build credibility faster than pedigree: Starting from scratch without Yahoo or Uber's brand meant proving commitment through shipping cadence. Collate's strategy: demonstrate you'll be around and responsive before asking for production deployments. This matters more in open source than closed-source where sales cycles force commitment conversations earlier.
Separate technical-buyer and business-buyer GTM motions explicitly: Collate's founding team spoke fluently to data engineers and architects who lived the metadata problem daily. Their CMO hire (after establishing product-market fit) brought expertise in articulating business impact—ROI on data initiatives, compliance risk reduction, AI readiness—without the founders faking business-speak. The timing matters: hire for the motion you're entering, not the one you're in.
Play the long game with builder-culture companies: At Uber, internal tools were 2-3 years ahead of vendor solutions but became technical debt as teams moved to new problems. Suresh's advice: "Keep in touch with these larger companies. Your technology will improve and you will have better conversation with larger technical companies." The wedge is timing—catch them when maintenance burden outweighs building pride, typically 24-36 months post-launch.
Design for all company scales from day one: Unlike Uber's internal metadata platform built for massive scale with corresponding complexity, Open Metadata works for small teams through enterprises. This wasn't just good design—it was GTM expansion strategy. Building only for scale locks you into enterprise-only sales. Building only for simplicity caps your ACV. The middle path requires architectural discipline upfront.
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How WindBorne Systems landed their first Air Force contract through Defense Innovation Unit | John Dean

WindBorne Systems is transforming global weather forecasting by deploying long-duration weather balloons that fly for weeks instead of hours. What began as a Stanford Student Space Initiative project has scaled to 100 balloons aloft simultaneously, targeting 500 by end of next year, with an end goal of 10,000 balloons monitoring Earth's atmosphere. In this episode of BUILDERS, I sat down with John Dean, Co-Founder and CEO of WindBorne Systems, to explore how the company secured its first government contract in under three years without lobbyists, achieved 4x annual manufacturing growth, and built Weather Mesh—an AI weather model that outperforms competitors from Google DeepMind.
Topics Discussed:
The technical evolution from Stanford project to operational constellation of altitude-controlled balloons
Strategic decision to pursue government revenue before building B2B forecasting products
Navigating Defense Innovation Unit and Air Force Lifecycle Management Center procurement as a founder
Timeline from founding to first grants (within six months) and first data delivery contract (two and a half years)
Current roughly 50/50 revenue split between civilian agencies (NOAA, international weather services) and Department of Defense
Building Weather Mesh after Huawei's Pangu Weather validated end-to-end AI forecasting viability
Transitioning from founder-led sales by promoting a Palantir hire from proposal writer to public sector growth leader
The 30-year vision of millions of fingernail-sized atmospheric sensors creating a planetary nervous system
GTM Lessons For B2B Founders:
Study the bureaucracy's incentive structures before pitching product value: John spent years mapping how government procurement actually works rather than leading with product capabilities. The critical insight: in DoD sales, the warfighter (end user) doesn't control purchasing decisions. Success requires understanding each stakeholder's specific mandate and aligning your solution to their organizational incentives, not just operational needs. For civilian agencies like NOAA, the dynamics differ entirely. Founders entering govtech should invest 6-12 months learning procurement mechanics before expecting revenue.
Use government contracts as non-dilutive scaling capital for hardware businesses: WindBorne secured SBIR grants within six months, then landed their first Air Force data delivery contract through Defense Innovation Unit at the two-and-a-half-year mark. John explicitly treated early grants as equivalent to venture funding but without equity dilution. For companies building physical infrastructure at scale (satellites, hardware networks, manufacturing operations), government contracts provide the runway to reach technical milestones that unlock larger B2B opportunities. This sequencing—government funding first, then B2B products built on that foundation—proves more capital-efficient than attempting to raise massive venture rounds upfront for unproven hardware.
Integrate with legacy systems rather than attempting wholesale replacement: WindBorne doesn't aim to replace the 1,000 radiosondes launched daily worldwide—they're expanding coverage from the current 15% of Earth (where humans can launch traditional balloons) to 100%. The hardware is revolutionary (weeks of flight versus two hours), but the go-to-market integrates into existing weather agency workflows and feeds into established models like GFS and ECMWF. This approach accelerated adoption because agencies could add WindBorne data without overhauling their entire forecasting infrastructure. The displacement of radiosondes becomes economically inevitable long-term, but only after proving the system at scale.
Move fast once adjacent technology validates your thesis: WindBorne wasn't investing in AI-based weather forecasting until Huawei's Pangu Weather paper demonstrated that end-to-end neural weather models could compete with physics-based simulations. Once that validation appeared, John's team moved immediately—adopting the open architecture and expanding it into Weather Mesh before the approach became widely adopted. The lesson isn't to wait for competitors, but to monitor adjacent technological developments and move decisively when validation emerges. They built a top-performing model by being early to a proven approach, not first to an unproven one.
Hire for mid-level roles and promote based on demonstrated judgment: John hired Dana from Palantir as a proposal writer, not as a sales executive. He watched her demonstrate strong opinions that consistently proved correct, then promoted her to build and lead the entire public sector growth organization. This internal promotion model worked better than external executive hires because the person already understood WindBorne's technology, customers, and internal culture. For specialized domains like government sales, bringing in experienced operators at individual contributor levels and promoting them as they prove their judgment builds more effective organizations than hiring executives to parachute in.
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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