TwelveLabs is building purpose-built foundation models for video understanding, enabling enterprises to index, search, and analyze petabytes of video content at scale. Founded by three technical co-founders who met in South Korea's Cyber Command doing multimodal video understanding research, the company recognized early that video requires fundamentally different infrastructure than text or image AI. Now achieving 10x revenue growth and serving customers across media, entertainment, sports, advertising, and federal agencies, TwelveLabs is proving that category creation through extreme focus beats trend chasing. In this episode, Jae Lee shares how the company navigated early product decisions, built specialized GTM motions for established industries, and maintained technical conviction during years of building in relative obscurity.

Topics Discussed:

• How military research in multimodal video understanding led to founding TwelveLabs in 2020 
• The technical thesis: why video deserves purpose-built foundation models and inference infrastructure 
• Targeting video-centric industries where ROI justifies early-stage pricing: media, entertainment, sports, advertising, and defense 
• Partnership-driven distribution strategy and AWS Bedrock integration results 
• Specialized sales approach: generalist leaders, vertical-specific AEs and solutions architects Maintaining extreme focus and avoiding hype cycles during the first three years of building 
• Federal GTM lessons: why In-Q-Tel partnership and authentic mission alignment matter more than process optimization 
• The discipline of saying no to large opportunities that don't fit ICP 
• Keeping hiring bars high when the entire team is underwater

GTM Lessons For B2B Founders:

• Hire vertical specialists on the front lines, not just at the top: TwelveLabs structures its GTM team with generalist leaders (head of GTM and VP of Revenue) who can sell any technology, but vertical-specialized AEs, solutions architects, and deployment engineers. These front-line team members come directly from the four target industries and understand customer workflows, buying patterns, and integration points without ramp time.
• Infrastructure plays require integration partnerships, not displacement: In established industries with layered technology stacks, positioning as foundational infrastructure demands partnership-first distribution. Jae explained their approach: integration with media-specific GSIs, media asset management platforms, and cloud providers ensures TwelveLabs fits into existing workflows rather than forcing wholesale replacement.
• Extreme focus on first-principles product development beats fast-follower tactics: While competitors built quick demos by wrapping existing models, TwelveLabs spent three years building proprietary video foundation models and indexing infrastructure from scratch. Jae was explicit about the cost: "It was painful journey in the first like two and a half, three years because folks are flying by." The payoff came from solving actual customer problems—indexing 2 million hours of content in two days, enabling semantic search at scale, building agent workflows for specific use cases.
• Federal requires cultural alignment before GTM optimization: TwelveLabs' federal success stems from authentic mission alignment, not just process execution. With In-Q-Tel as an investor providing interface to agencies and founders with military backgrounds, the company established credibility through shared values rather than sales tactics.
• ICP discipline protects product focus and team morale: Saying no to large early opportunities that don't fit ICP is operationally painful but strategically essential. Jae acknowledged the difficulty: "Early on saying no to customers is hard... as a founder you want to grow your business and you know that's going to be good for the morale. But that's only true when the customers are actually their ideal customers."

Freeplay AI emerged from a precise timing insight: former Twitter API platform veterans Ian Cairns and Eric Schade recognized that generative AI created the same platform opportunity they'd previously captured with half a million monthly active developers. Their company now provides the observability, evaluation, and experimentation infrastructure that lets cross-functional teams—including non-technical domain experts—collaborate on AI systems that need to perform consistently in production.

Topics Discussed:

• Systematic customer discovery: 75 interviews in 90 days using jobs-to-be-done methodology to surface latent AI development pain points
• Cross-functional AI development: How domain experts (lawyers, veterinarians, doctors) became essential collaborators when "English became the hottest programming language"
• Production AI reliability challenges: Moving beyond 60% prototype success rates to consistent production performance
• Enterprise selling to technical buyers: Why ABM and content worked where ads and outbound failed for VPs of engineering
• Category creation without precedent: Building thought leadership through triangulated insights across hundreds of implementations
• Offline community building: Growing 3,000-person Colorado AI meetup with authentic "give first" approach

GTM Lessons For B2B Founders:

• Structure customer discovery with jobs-to-be-done rigor: Ian executed a systematic 75-interview program in 90 days, moving beyond surface-level feature requests to understand fundamental motivations. Using Clay Christensen's framework, they discovered engineers weren't just frustrated with 60% AI prototype reliability—they were under career pressure to deliver AI wins while lacking tools to bridge the gap to production consistency. This deeper insight shaped Freeplay's positioning around professional success metrics rather than just technical capabilities.
• Exploit diaspora networks from platform companies: Twitter's developer ecosystem became Ian's customer research goldmine. Platform company alumni have uniquely valuable networks because they previously interfaced with hundreds of technical teams. Rather than cold outreach, Ian leveraged existing relationships and warm introductions to reach heads of engineering who were actively experimenting with AI. This approach yielded higher-quality conversations and faster pattern recognition across use cases.
• Target sophistication gaps in technical buying committees: Traditional SaaS tactics failed because Freeplay's buyers—VPs of engineering at companies building production AI—weren't responsive to ads or generic outbound. Instead, Ian invested in deep technical content (1500-2000 word blog posts), speaking engagements, and their "Deployed" podcast featuring practitioners from Google Labs and Box. This approach built credibility with sophisticated technical audiences who needed education about emerging best practices, not product demos.
• Build authority through cross-portfolio insights: Rather than positioning as AI experts, Ian built trust by triangulating learnings across "hundreds of different companies" and sharing pattern recognition. Their messaging became "don't just take Freeplay's word for it—here's what we've seen work across environments." This approach resonated because no single company had enough AI production experience to claim definitive expertise. Aggregated insights became more valuable than individual case studies.
• Time market entry for the infrastructure adoption curve: Ian deliberately positioned Freeplay for companies "3, 6, 12 months after being in production" rather than competing for initial AI experiments. They recognized organizations don't invest in formal evaluation infrastructure until they've proven AI matters to their business. This patient approach let them capture demand at the moment companies realized they needed serious operational discipline around AI systems.

Cerebrium is a serverless AI infrastructure platform orchestrating CPU and GPU compute for companies building voice agents, healthcare AI systems, manufacturing defect detection, and LLM hosting. The company operates across global markets handling data residency constraints from GDPR to Saudi Arabia's data sovereignty requirements. In a recent episode of Category Visionaries, I sat down with Michael Louis, Co-Founder & CEO of Cerebrium, to explore how they built a high-performance infrastructure business serving enterprise customers with high five-figure to six-figure ACVs while maintaining 99.9%+ SLA requirements.

Topics Discussed:

• Building AI infrastructure before the GPT moment and strategic patience during the hype cycle
• Scaling a distributed engineering team between Cape Town and NYC with 95% South African talent
• Partnership-driven revenue generation producing millions in ARR without traditional sales teams
• AI-powered market engineering achieving 35% LinkedIn reply rates through competitor analysis
• Technical differentiation through cold start optimization and network latency improvements
• Revenue expansion through global deployment and regulatory compliance automation

GTM Lessons For B2B Founders:

• Treat go-to-market as a systems engineering problem: Michael reframed traditional sales challenges through an engineering lens, focusing on constraints, scalability, and data-driven optimization. "I try to reframe my go to market problem as an engineering one and try to pick up, okay, like what are my constraints? Like how can I do this, how can it scale?" This systematic approach led to testing 8-10 different strategies, measuring conversion rates, and building automated pipelines rather than relying on manual processes that don't scale.
• Structure partnerships for partner success before revenue sharing: Cerebrium generates millions in ARR through partners whose sales teams actively upsell their product. Their approach eliminates typical partnership friction: "We typically approach our partners saying like, look, you keep the money you make, we'll keep the money we make. If it goes well, we can talk about like rev share or some other agreement down the line." This removes commission complexity that kills B2B partnerships and allows partners to focus on customer value rather than internal revenue allocation conflicts.
• Build AI-powered competitive intelligence for outbound at scale: Cerebrium's 35% LinkedIn reply rate comes from scraping competitor followers and LinkedIn engagement, running prospects through qualification agents that check funding status, ICP fit, and technical roles, then generating personalized outreach referencing specific interactions. "We saw you commented on Michael's post about latency in voice. Like, we think that's interesting. Like, here's a case study we did in the voice space."
• Position infrastructure as revenue expansion, not cost optimization: While dev tools typically focus on developer productivity gains, Cerebrium frames their value proposition around market expansion and revenue growth. "We allow you to deploy your application in many different markets globally... go to market leaders love us and sales leaders because again we open up more markets for them and more revenue without getting their tech team involved."
• Weaponize regulatory complexity as competitive differentiation: Cerebrium abstracts data sovereignty requirements across multiple jurisdictions - GDPR in Europe, data residency in Saudi Arabia, and other regional compliance frameworks. "As a company to build the infrastructure to have data sovereignty in all these companies and markets, it's a nightmare." By handling this complexity, they create significant switching costs and enable customers to expand internationally without engineering roadmap dependencies, making them essential to sales teams pursuing global accounts.

OpenInfer addresses the enterprise infrastructure gap that causes 70% of edge AI deployments to fail. Founded by system architects who previously built high-throughput runtime systems at Meta (enabling VR applications on Qualcomm chips via Oculus Link) and Roblox (scaling real-time operations across millions of gaming devices), OpenInfer applies proven architectural patterns to enterprise edge AI deployment. The company targets three specific customer pain points: cost reduction for AI-always-on applications, data sovereignty requirements in regulated environments, and reliability for systems that must function regardless of connectivity. In this episode of Category Visionaries, CEO and Founder Behnam Bastani reveals how external market catalysts like DeepSeek's efficiency breakthrough transformed investor perception and validated their compute optimization thesis.

Topics Discussed:

• System architecture pattern replication from Meta's Oculus Link to Roblox to OpenInfer
• The compute efficiency gap: why "throwing hardware" at AI problems creates market inefficiencies
• How DeepSeek's January 2025 breakthrough shifted investor sentiment from skepticism to oversubscription
• Customer targeting methodology: focusing on business unit leaders facing career consequences
• Government market discovery: air-gapped environments and data sovereignty requirements
• Technical demonstration strategies for overcoming the 70% edge deployment failure rate
• Privacy-first AI positioning unlocking previously inaccessible use cases

GTM Lessons For B2B Founders:

• Target decision-makers with career-level consequences: Rather than pursuing prospects who might "take a risk," Behnam focuses on "those that lose their jobs if they're not solving the problem" - specifically business unit leaders whose profit margins or sales metrics directly impact their career trajectory. This creates urgency that comfortable cloud users lack and accelerates deal cycles by aligning solution adoption with personal survival incentives.
• Leverage external market catalysts for thesis validation: OpenInfer initially faced investor pushback ("Nvidia's got everything working well. Why you think you can do anything better?") until DeepSeek's efficiency breakthrough provided third-party validation. "January hits and then there's DeepSeek... People called us, hey, you're DeepSeek on edge." Founders should identify potential external events that could validate their contrarian thesis and be prepared to capitalize when these catalysts occur.
• Lead with technical proof points over explanations: In markets with high failure rates, demonstrations eliminate skepticism faster than education. "We definitely have metrics, demos, and we go with those. We demonstrate what's possible... we remove this skepticalism in terms of ease of deployments, power of edge in one shot." This approach recognizes that technical buyers need confidence before curiosity.
• Pursue unexpected traction sources aggressively: Despite targeting enterprise ISVs, government demand emerged due to air-gapped environment requirements. "Government is actually becoming huge traction primarily because data ownership was a major topic to them." Rather than forcing initial market hypotheses, founders should redirect resources toward segments showing organic product-market fit signals, even when they require different sales processes.
• Build credibility through architectural pattern repetition: Investors backed OpenInfer because "we are the people that have built this twice, scaled it to millions." Repeating proven technical patterns across different contexts creates sustainable competitive advantages that new entrants cannot replicate without similar experience depth.

Hamming AI has emerged as a pioneer in voice agent quality assurance, creating what founder Sumanyu Sharma calls a "new category" of QA for conversational voice agents. After spending a decade building data products at scale at companies like Tesla and Citizen, Sharma recognized an acute pain point as voice agents began proliferating: enterprises desperately needed confidence that their voice agents would work reliably before launching to production. In this episode of Category Visionaries, Sharma shares how his team accidentally created a new category by following their instincts and leveraging a decade of expertise in reliability testing, audio processing, and machine learning.

Topics Discussed:

• The evolution from Tesla's data science team to founding a voice agent QA company
• How "wandering the desert" for months led to finding the perfect problem-solution fit
• Building a completely inbound-driven go-to-market strategy in an emerging category
• The decision to launch before feeling ready and building alongside customers
• Why the voice agent market skeptics were wrong about market size
• Creating enterprise trust through reliability testing at scale

GTM Lessons For B2B Founders:

• Follow your instincts when you have deep domain expertise: Sharma spent months "wandering the desert" looking for the right problem until voice agent QA clicked. He emphasizes that when you have a decade of relevant expertise, you can recognize the perfect problem when it appears. As he put it, "when you see it, you kind of know... I am perfectly equipped to solve this specific problem. I'm built for this." Founders should trust their instincts when they have genuine domain expertise rather than overthinking market validation.
• Build something people want before focusing on category creation: Unlike many founders who start with category creation in mind, Hamming AI "accidentally" created their category by obsessively solving customer problems. Sharma notes, "We weren't looking to create a category. We were just looking to solve a problem that we feel passionate about, that we are already experts at." This customer-first approach led to organic category emergence and sustainable demand.
• Launch before you feel ready and build with customers: Sharma's biggest learning was launching with a "half-baked" product rather than perfecting it in isolation. "We didn't have a product that we thought was incredible. We just thought, hey, it kind of works, but let's actually build the product together with customers." This approach accelerated learning cycles and created stronger product-market fit than months of internal development would have achieved.
• Leverage contrarian insights from deep market proximity: While others dismissed voice agent QA as "too small," Sharma's data science background and proximity to builders gave him conviction. He analyzed the fundamentals: "Voice is a universal API for people. Voice agents are just becoming possible. They will be unreliable. Therefore, testing is very important. That's the math." Founders should develop conviction through first-principles thinking rather than consensus market opinions.
• Focus obsessively on customer success over marketing in emerging categories: Hamming AI remains completely inbound-driven, focusing entirely on making existing customers successful rather than traditional marketing. Sharma explains, "The voice space is so small where if you are doing a good job and if you build a product that people love, they will tell their friends about it." In nascent categories, product excellence and word-of-mouth can be more effective than broad marketing campaigns.

Nevermined is pioneering the infrastructure for AI commerce, building payment rails specifically designed for agent-to-agent transactions. With a vision of trillions of AI agents functioning as both merchants and consumers, Don Gossen brings 20 years of AI experience to solving what he believes will be the foundational payment challenge of the next era of computing. In this episode of Category Visionaries, Don shares insights on creating an entirely new category—AI commerce—and the unique go-to-market challenges of building for a future that's rapidly becoming reality.

Topics Discussed:

• The emergence of two distinct agent modalities: agent as proxy and agent as independent economic actor
• Why existing payment infrastructure cannot handle the scale and velocity of AI agent transactions
• Nevermined's commission-based business model focused on agent-to-agent payments
• The fundamental cost model differences between SaaS and AI agents
• Creating the "AI commerce" category and the strategic importance of early categorization
• Go-to-market strategy targeting verticalized AI agent builders with Series A+ funding
• The infrastructure investment phase versus deployment challenges in AI adoption

GTM Lessons For B2B Founders:

• Target customers who have proven business models, not just potential: Don's go-to-market strategy specifically targets AI agent companies that have raised Series A or later rounds. His reasoning: "Hopefully the VCs that are backing them have done some due diligence. And the money they're earning is actually real." Rather than chasing every potential customer, focus on those who have already validated their revenue model and can immediately benefit from your solution.
• Understand the fundamental cost structure of your customer's business model: Don identified that AI agents have an inverted cost model compared to traditional SaaS—most costs are operational (OpEx) rather than capital (CapEx). He explains: "The cost model is basically flipped. Most of your cost is actually on the opex... Your operating costs fluctuate based on the request." This insight shaped Nevermined's entire value proposition around cost monitoring and settlement rather than just payment processing.
• Create category language early, even before market adoption: Don coined "AI commerce" in 2023 when "people were like, what the hell's an AI agent?" His approach: "It always helps to categorize and provide language that's going to allow people to understand what it is that you're talking about... It's the memeification of the category." Don't wait for your market to mature—create the vocabulary that will define it.
• Focus on the operational reality, not the theoretical use case: While competitors focus on connecting bank accounts to AI agents for consumer purchases, Don focuses on the underlying workflow costs: "How much does the workflow cost to actually render that outcome?" Understanding the true operational mechanics of your customers' business—not just their surface-level needs—can create significant competitive differentiation.
• Leverage deep domain expertise to identify non-obvious problems: Don's 20 years in AI revealed that variable AI agent responses create variable operational costs—a problem most founders wouldn't recognize. He notes: "Until recently most people didn't realize that is a major issue in operating these solutions." Deep industry experience can help you spot problems that newer entrants miss entirely.

Typedef is building an inference-first data engine designed for the new era of AI agents and machine-to-machine interactions. With $5.5 million in funding, the company is reimagining data infrastructure for a world where both humans and AI systems need seamless access to data processing capabilities. In this episode of Category Visionaries, I sat down with Kostas Pardalis, Co-Founder & CEO of Typedef, to explore how the company is addressing the fundamental shift from traditional business intelligence platforms to AI-native data infrastructure that treats inference as a first-class citizen alongside traditional compute resources.

Topics Discussed:

• Typedef's vision for inference-first data infrastructure in the AI era
• The transition from human-only to machine-to-machine data interactions
• Why infrastructure companies take longer to reach revenue but build deeper moats
• The evolution from pre-AI data platforms to AI-native solutions
• Design partner strategies for infrastructure companies
• Go-to-market approaches that combine bottom-up (engineers) and top-down (decision makers) strategies
• Category creation challenges in rapidly evolving AI markets
• The importance of open source and education in developer-focused go-to-market

GTM Lessons For B2B Founders:

• Start go-to-market activities during the design partner phase: Kostas emphasized that go-to-market isn't something you switch on after product development. "It's okay to go out there and talk about something that it's not very well defined or it might change, but actually it doesn't matter... go to market like just like everything else, it's an interactive process." B2B founders should begin building awareness, creating content, and engaging with potential customers even while their product is still evolving.
• Design partners must have real pain, not just time: The biggest insight about design partnerships is treating them like real customer relationships. "A design partner is still someone who has a problem that needs to be solved... no one is just donating their time out there... There still has to be value there." Don't approach design partnerships as charity work - ensure there's genuine mutual value exchange where your solution addresses real business pain.
• Product-market fit requires both product AND market innovation: Kostas challenged the common engineering mindset about product-market fit: "Many times, especially engineers, think that when we say product, market fit is that we have market, which is a static thing and we just need to iterate over the product until we find the right thing that matches exactly the market. No, that's not right." B2B founders must innovate on both the product and go-to-market sides simultaneously, including defining their target vertical and building appropriate sales motions.
• Infrastructure sales require dual-persona strategies: When selling to developers and technical infrastructure, you need both bottom-up and top-down approaches. "Even if you go to the manager and they love what you are saying, you still have to convince the engineers to use this thing... And they have a lot of leverage and vice versa." The bottom-up motion involves open source adoption and education, while the top-down involves traditional outbound sales to decision makers.
• Category creation doesn't guarantee category dominance: Having witnessed category creation firsthand, Kostas shared that defining a category doesn't ensure winning it. "It doesn't necessarily mean that because you define the categories that you are going to win at the end... Vercel was not actually the company that invented the category there." Focus on solving real problems and building sustainable competitive advantages rather than just being first to market with category messaging.

Personal AI is pioneering the next generation of artificial intelligence with their memory-first platform that creates personalized AI models for individuals and organizations. Having raised over $16 million, the company has evolved from targeting consumers to focusing on enterprise customers who need highly private, precise, and personalized AI solutions. In this episode of Category Visionaries, we sat down with Suman Kanuganti, CEO and Co-Founder of Personal AI, to explore the company's journey from early AI experimentation in 2015 to building what he envisions as the future AI workforce for enterprise organizations.

Topics Discussed:

• Personal AI's evolution from consumer-focused to enterprise B2B platform
• The technical architecture behind personal language models vs. large language models
• Privacy-first approach and competitive advantages in regulated industries
• Go-to-market pivot and scaling from small law firms to enterprise contracts
• Unit economics advantages and 10x cost reduction compared to traditional LLMs
• Vision for AI workforce integration in public companies within 3-5 years

GTM Lessons For B2B Founders:

• Recognize when market timing doesn't align with your vision: Suman's team was building AI solutions as early as 2015, nearly a decade before the ChatGPT moment. When ChatGPT launched in November 2022, Personal AI faced confusion from investors and customers about their differentiation. Rather than forcing their sophisticated personal AI models on consumers who wanted simpler solutions, they recognized the market mismatch and pivoted. B2B founders should be prepared to adjust their go-to-market approach when market readiness doesn't match their technical capabilities, even if their technology is superior.
• Find your wedge in enterprise through specific pain points: Personal AI discovered their enterprise entry point by targeting "highly sensitive use cases that LLMs are not good for" where companies would be "shit scared to put any data in the LLM." They focused on precision and privacy pain points that large language models couldn't address. B2B founders should identify specific enterprise pain points where their solution provides clear advantages over existing alternatives, rather than trying to be everything to everyone.
• Let customer expansion drive revenue growth: Personal AI's enterprise strategy evolved organically as existing contracts "started growing like wildfire as more people had a creative mindset to solve the problem with the platform." They discovered that their Persona concept allowed enterprises to consolidate multiple AI use cases into one platform. B2B founders should design their platforms to naturally expand within organizations and reduce vendor fragmentation, creating stickiness and increasing average contract values.
• Leverage architectural advantages for unit economics: By positioning their personal language models between customer use cases and large language models, Personal AI achieved "10x lower cost" per token. This architectural decision created both privacy benefits and economic advantages. B2B founders should consider how their technical architecture can create sustainable competitive advantages in both functionality and economics, not just features.
• Geography matters more than you think for fundraising: Suman identified his biggest fundraising mistake as not moving to San Francisco earlier, stating "back in 2022 or 2023 is when I should have moved to San Francisco, period." He learned that being part of the Silicon Valley ecosystem and conversation is critical for fundraising success. B2B founders should consider the strategic importance of physical presence in key markets, especially when raising capital, and not underestimate the value of in-person relationship building.

Wispr Flow has transformed voice dictation from a frustrating novelty into a seamless productivity tool that users trust implicitly. With a recent $30 million Series A led by Menlo Ventures, the company has achieved remarkable product-market fit through 90% word-of-mouth growth and users who share the product organically without prompting. In this episode, I sat down with Tanay Kothari, CEO and Co-Founder of Wispr Flow, to learn about the company's pivot from hardware to software, their approach to manufacturing viral moments, and their strategy for competing against tech giants with distribution advantages.

Topics Discussed:

• Wispr Flow's pivot from building voice assistant hardware to focusing on voice-to-text software
• The company's unique approach to achieving sub-half-second latency and exceptional accuracy
• Building viral growth through manufactured "aha moments" and exceptional user onboarding
• Competing against OpenAI and Apple through speed of execution and user experience focus
• The challenge of building for mainstream users beyond Silicon Valley's tech-savvy population
• Strategic decisions around cutting non-essential growth channels to maintain focus

GTM Lessons For B2B Founders:

• Manufacture viral moments through obsessive user research: Tanay personally onboarded the first 500 users via Google Meet, watching their facial expressions, mouse movements, and emotional reactions in real-time. This intensive observation allowed him to identify and systematically reproduce moments of user delight. He explained, "Find the things that repeatedly create delight, make sure that never dies, and then find the other places where there's confusion and kind of take them out." B2B founders should invest heavily in understanding the micro-moments of user experience, as these compound into organic growth at scale.
• Leverage authentic product usage by your target buyers during fundraising: When Wispr Flow raised their Series A, every VC in Silicon Valley was already using the product daily. Tanay noted, "I didn't need to convince them about why the product was good. All I had to tell them about if you believe why Whisper is good today, here is where we can take the company." This eliminated the typical product demonstration phase and shifted conversations to vision and execution capability. B2B founders should prioritize getting their product into the hands of potential investors as users before ever pitching them as investors.
• Build anti-fragile technology that improves as the industry evolves: Rather than competing directly with AI model capabilities, Wispr Flow built infrastructure that gets better as underlying AI models improve. Tanay instructs his team: "If at some point that you feel afraid of a new model launching, you're doing something wrong." This philosophy led them to focus on latency, user experience, and integration rather than competing on raw AI performance. B2B founders in AI-adjacent spaces should identify where they can create value that compounds with industry improvements rather than being displaced by them.
• Cut aggressively to maintain focus during rapid growth: Despite conventional wisdom, Wispr Flow eliminated SEO efforts entirely because "no one is searching for voice dictation" and most people don't know the technology has reached usability thresholds. Tanay applies an extreme 80/20 rule: "You can cut the 80% of the things that are not giving you the results... You find a new 20% that's going to give you 80% more results and you can just keep doing that again and again."
• Design for mainstream adoption beyond early adopters: While most AI tools target Silicon Valley technologists, Tanay identified that 95% of the population represents the real market opportunity. He noted these users "end up being your most loyal users" because they have less churn and higher lifetime value than tech-savvy early adopters.

Prior Labs is pioneering foundation models for structured data, bringing transformer technology from the generative AI world to tabular data that sits in databases and spreadsheets across every business. With $9 million in funding and over 1.5 million downloads of their open-source model, Prior Labs is revolutionizing how data scientists work with structured data by creating universal models that can handle multiple use cases instead of requiring custom models for each specific application. In this episode, I sat down with Sauraj Gambhir, Co-Founder of Prior Labs, to explore how they're transforming machine learning workflows from taking days to seconds and building a global community around their breakthrough technology.

Topics Discussed:

• Prior Labs' mission to bring transformer technology to structured data and tabular datasets
• The transition from traditional 20-year-old machine learning methods to universal foundation models
• Building a horizontal product that serves data scientists across finance, healthcare, and scientific research
• The company's open-source strategy with 1.5 million downloads and community-driven development
• Social media and community-building tactics that drove adoption across LinkedIn, Twitter, and Discord
• Scaling from 3 to 16 team members in seven months while maintaining technical focus
• Fundraising strategy for AI companies and the balance between raising enough capital without over-inflating
• Plans for geographic expansion from Berlin to the US market

GTM Lessons For B2B Founders:

• Lead with open source for technical audiences: Prior Labs built their entire go-to-market strategy around an open-source model that anyone can download and use for free. Sauraj explained, "We've got like over one and a half million downloads and it is open source. You just need to attribute us that you're using our model." This approach allowed them to achieve massive adoption while building credibility with their technical audience. B2B founders targeting developers or technical users should consider how open source can accelerate adoption and community building before monetization.
• Build community ownership, not just engagement: Sauraj approaches community building like team building, saying, "If you think about your team as a founder, like when you build a team, you want them to feel like it's their company... I'm trying to take that same philosophy towards community building." He creates biweekly Discord updates where half the content showcases community contributions, leading members to actively submit their use cases and request features.
• Leverage co-founder networks strategically for different audiences: Prior Labs uses each co-founder's unique network to reach different segments. Sauraj noted, "One of my co founders has been a professor of machine learning for the last 12 years. So he already had a pretty good following of let's say the data science community... when we need to generate inbound, I'm the one pushing when we need to like generate more technical applications for people to apply for jobs with us. We're going through my co founders networks."
• Focus adoption over monetization in emerging categories: Despite having paying customers, Prior Labs keeps their API free and focuses entirely on adoption metrics. Sauraj explained, "Right now we are offering it for free because we just want like adoption is really the biggest use case at the moment... when we have like the next versions of the models, that's really when we're going to be able to flip the switch."
• Use technical documentation as brand building: Instead of focusing on traditional marketing materials, Prior Labs invested heavily in developer-focused assets. Sauraj said, "We were really focused on getting really good docs in place, really good, like GitHub read me in place. And the brand was really kind of like building this community and being like open and honest with the community."

Tera AI is pioneering a software-centric approach to robotics, moving away from traditional hardware-dominated solutions toward a unified operating system for robotic platforms. After raising $8 million and transitioning from insurance applications to robotics, the company is building what founder Tony Zhang envisions as "a general purpose operating system for robot platforms" powered by spatial foundation models. In this episode of Category Visionaries, Tony shares his journey from Google X to founding Tera AI, including hard-won lessons about market validation, customer discovery, and the critical importance of understanding buyer priorities.

Topics Discussed:

• Tera AI's evolution from geospatial foundation models in insurance to robotics applications
• The challenges of customer discovery in regulated industries like insurance
• Tony's experience at Google X and the ChatGPT moment that sparked entrepreneurial action
• First Round's Product Market Fit program and structured customer discovery methodology
• The transition from hardware-centric to software-centric robotics architecture
• Fundraising strategies and developing instincts for investor feedback
• Building a team of top-tier AI researchers in a competitive talent market

GTM Lessons For B2B Founders:

• Lead with priority validation, not pain discovery: Tony learned the hard way that not every pain point can be solved on a VC timeline. His breakthrough insight was asking upfront: "Tell me if this is one of your top three priorities. If not, tell me what are those three priorities." He discovered that many insurance prospects liked their solution but had more pressing infrastructure problems unrelated to AI. B2B founders should qualify buyer priorities before presenting solutions to avoid getting trapped in lengthy sales cycles for non-critical problems.
• Understand regulatory constraints early in enterprise markets: Tera AI spent nearly a year in insurance before realizing that regulatory barriers made technology adoption extremely difficult, regardless of product-market fit. Tony explains: "Because of the regulations in America, it is incredibly difficult for an insurer or carrier to adopt new technology, especially technology that was as new as the stuff that we were building." Founders entering regulated industries should map compliance requirements and adoption timelines before committing significant resources.
• Structure customer discovery to eliminate waste: Through First Round's PMF program, Tony discovered they were doing discovery calls inefficiently, often requiring multiple meetings with the same prospects. The key insight was asking the right qualifying questions upfront rather than leading with solutions. This approach eliminated unnecessary follow-up meetings and accelerated their discovery process by 5x. Founders should develop structured discovery frameworks with clear qualifying criteria before scaling outreach efforts.
• Market timing requires both technology readiness and buyer urgency: Tony's "ChatGPT moment" wasn't just about technological possibility—it was about recognizing the convergence of technical capability and market readiness. He emphasizes: "It wasn't too early, it wasn't too late." The key was understanding that spatial AI could finally deliver value that buyers were ready to adopt. Founders should evaluate both technical feasibility and market timing when deciding on startup opportunities.
• Attract talent with novel technical challenges, not just compensation: Despite intense competition for AI talent in Silicon Valley, Tera AI successfully recruits top researchers by offering genuinely innovative work. Tony explains: "We genuinely try to innovate across the entire stack. We build our own models, we build our own datasets, we can write papers on the things we're doing." They target researchers who are "bored to death by the LLM world" and want to work on groundbreaking spatial AI problems.

NEURA Robotics is transforming the robotics industry by building cognitive robots powered by physical AI. With €120 million raised and 5,000-10,000 robots already deployed, the company has set an ambitious goal of deploying 5 million robots by 2030. In this episode, I sat down with David Reger, CEO and Founder of NEURA Robotics, to explore how his company is solving the reliability and adoption challenges that have kept robotics a niche market, and his vision for making robots as ubiquitous as smartphones.

Topics Discussed:

• NEURA's partnership-driven go-to-market strategy using horizontal and vertical partners
• The company's unique physical AI model built specifically for embodied intelligence
• Current deployment of household robots starting with elderly care applications
• The challenge of raising hardware funding in Europe versus Japan and China
• Building cognitive robots that can operate with limited compute and bandwidth
• Creating a platform ecosystem where partners can download skills and applications
• The regulatory and cultural barriers to robot adoption in different markets
• NEURA's recent partnership with SAP and strategy to become Europe's next €100 billion company

GTM Lessons For B2B Founders:

• Leverage established channels for reliability-critical products: David built NEURA's entire go-to-market strategy around partnering with established robot companies rather than direct sales. He recognized that for reliability-critical hardware like robots, startups face an inherent trust deficit. "If you're talking about robots, there's all about reliability, it's all about trust because it has to run 24/7... And if you're looking into strength of a startup, that's exactly the point. Like this is something you don't have." B2B founders in hardware or mission-critical software should consider white-label partnerships with established players who already have the service infrastructure and customer trust.
• Build horizontal and vertical partnership ecosystems simultaneously: NEURA created a dual partnership model - horizontal partners (robot manufacturers) for broad distribution and vertical partners (domain specialists like welding or household task companies) for specialized applications. This creates a platform effect where "our partners don't have to have the knowledge, but they can simply download, let's say an app or a skill and they can use the robot like in all kinds of different domains." B2B founders should consider how to enable both broad distribution and deep specialization through complementary partnership types.
• Target markets where regulatory shifts create urgency: David identified that China's 2030 goal of transforming 5% of working labor to robotics (40 million robots) would force global competition. "The whole world has to, let's say, also wake up in the same time... because if we don't want to end up, let's say as a museum, we have to also contribute." B2B founders should identify geopolitical or regulatory shifts that create market urgency and position their solutions as necessary responses to competitive pressure.
• Raise capital in markets that understand your technology: When European and US investors were skeptical of hardware, David found receptive investors in Japan who "believe in robots" and understood the market potential. He eventually had to pivot to China for speed, then later successfully raised €120 million in Europe when the market shifted. B2B founders should be willing to pursue capital in non-obvious geographies where their technology vision is better understood, even if it requires navigating different business cultures.
• Focus on physical AI differentiation for embodied products: David emphasized that NEURA's competitive advantage lies in their physical AI model: "I do believe that like our AI model is one of the, let's say it's the best in the world in that space, because simply it's much more efficient and actually built for being physical, while the most other models are not."

Revv is transforming one of America's most traditional industries with AI-powered technology that helps auto repair shops navigate the complexity of modern vehicles. With over $33 million in funding and explosive growth from 2 to 75+ employees in just 24 months, Revv has found product-market fit in a massive, underserved market. In this episode of Category Visionaries, we sat down with Adi Bathla, CEO and Co-Founder of Revv, to explore how he built an AI platform that's revolutionizing auto repair workflows and compressing sales cycles from 21 days to just 3 days.

Topics Discussed:

• Revv's origin story and Adi's path from NASA award winner to auto industry entrepreneur
• The regulatory catalyst driving massive industry transformation (government-mandated automatic emergency braking by 2029)
• How modern cars evolved from mechanical devices to "computers on wheels" requiring specialized repair knowledge
• The challenge of acquiring first customers in a traditionally offline, relationship-driven industry
• Revv's approach to integrating with existing shop workflows rather than forcing adoption of new platforms
• The intense transition from founder-led sales to scalable go-to-market systems
• Building a mathematical rebuild of their sales process that compressed cycles from 21 days to 3 days
• Scaling from onboarding 20 shops per month to over 100 shops per month in just 12 months

GTM Lessons For B2B Founders:

• Meet customers where they are, not where you want them to be: Adi learned early that success in offline industries requires deep integration with existing workflows rather than forcing behavioral change. "Meeting an offline industry user where they are in their workflow rather than asking them to move towards your way of doing things" became a core thesis. Revv integrates with shops' existing scanning tools and estimatics software, running in the background to provide insights without disrupting established processes.
• Leverage regulatory triggers as market catalysts: Revv's timing was driven by government regulation mandating automatic emergency braking in all vehicles by 2029, which accelerated the adoption of advanced driver assistance systems across the vehicle fleet. This created an acute pain point as repair shops struggled to service increasingly complex technology. Adi explained, "The best birthplace of startups is when there is a government regulation or a functional change or net new technology that didn't exist before."
• Embrace the pain of rebuilding your go-to-market repeatedly: As Revv scaled, Adi made the difficult decision to completely rebuild their sales process multiple times. "There's no shame in admitting that what worked from the 0 to 5 journey is going to not work for the 5 to 25," he explained. The company rebuilt their entire sales motion to compress cycles from 21 days to under 3 days by redefining customer personas, creating targeted talk tracks, and engineering demos that immediately showcase ROI.
• Focus on value propositions that drive immediate business impact: Revv succeeds because it promises both revenue generation and liability protection - compelling value propositions for shop owners. Their demos pull actual repair data from prospects' systems and show concrete ROI numbers, leading to a 60%+ demo-to-close rate. Rather than selling on features or efficiency gains, they demonstrate how their platform directly impacts the bottom line and reduces legal risk.
• Double down on unsexy, fragmented markets: The auto repair industry represents 400,000 businesses in the US alone, with 80% still operating as independent shops. While consolidation exists, the fragmented nature creates massive opportunity for horizontal solutions. Adi noted, "The more unsexy the industry, the more rich I think they are." These markets often lack sophisticated software solutions and have customers starved for technology that genuinely solves their problems.

Spot AI is pioneering the transformation from traditional video surveillance to intelligent video AI agents that can monitor, analyze, and respond to events in the physical world. With $93 million in funding, the company has evolved from providing simple camera management to building AI security guards and operational agents that can process hundreds of video feeds simultaneously, take autonomous actions, and augment human workers in manufacturing, retail, and security roles. In this episode of Category Visionaries, I sat down with Sudarshan Bhatija, Co-Founder and COO of Spot AI, to explore the company's journey from video surveillance to video AI agents and their vision for physical AI.

Topics Discussed:

• Spot AI's evolution from video surveillance to video intelligence to video AI agents
• The shift from IT-focused security tools to operations-wide business applications
• How AI agents can monitor hundreds of camera feeds and take autonomous actions
• The role of customer feedback in driving product development and market expansion
• Marketing philosophy focused on authenticity and customer outcomes
• Building high-performing marketing teams based on capability over experience
• The future of physical AI and AI agents with "eyes, hands, and legs"

GTM Lessons For B2B Founders:

• Capture existing demand and redirect to your category: Spot AI initially targeted customers searching for "video surveillance" but converted them by demonstrating superior value in video intelligence and operational insights. Sudarshan explained that customers "are still married to the old category and starts looking for that, but the subset of customers that wants more" responds to messaging around deeper insights and operational outcomes. B2B founders should identify customers searching for legacy solutions who are actually underserved by existing categories and ready for innovation.
• Let customer demand pull you upmarket and into new use cases: Rather than forcing expansion, Spot AI allowed existing customers to drive their evolution into higher-value AI agent applications. Sudarshan noted, "customers proactively pulling us into higher value use cases, pulling us up market, and basically the demand has already been created and we've been responding to that." B2B founders should build strong customer listening mechanisms and let proven demand from existing customers guide product development and market expansion.
• Build an early organic acquisition engine around category transition: Spot AI captured significant early growth by ranking for legacy category searches while converting visitors with next-generation messaging. They "built an organic strategy on Google to be able to acquire a lot of these leads" searching for video surveillance but presented solutions for video intelligence. B2B founders in evolving categories should dominate SEO for legacy terms while using landing pages and demos to educate prospects about superior alternatives.
• Hire marketing talent based on "can do" over "has done": Sudarshan emphasized that marketing success comes from "the ability to learn really fast and are deeply, you know, take strong ownership of their outcomes" rather than just experience. He found that "people who have the right bent of mind, the marketing bent of mind, but just have really high horsepower" outperform resume-based hires. B2B founders should prioritize intellectual curiosity, ownership mentality, and learning velocity when building marketing teams.
• Develop authentic, customer-centric marketing that speaks human-to-human: Spot AI's marketing philosophy centers on "focusing all our efforts on high value customer outcomes" and "authenticity" rather than "manicured" corporate messaging. Sudarshan noted that even in B2B, "you're selling to a business, but you're actually selling to a person." B2B founders should embrace authentic, conversational marketing that addresses real customer problems rather than polished but generic corporate communications.

Regal AI is revolutionizing the contact center landscape with its voice AI agent platform that's transforming how businesses communicate with customers. With $82 million in funding, Regal has positioned itself at the forefront of the AI revolution in customer service. In this episode of Category Visionaries, I spoke with Alex Levin, CEO and Co-Founder of Regal AI, about the company's journey from building tools to optimize human agent performance to pioneering voice AI agents that can handle customer interactions with unprecedented effectiveness.

Topics Discussed:

• Regal AI's pivot from optimizing human agent calls to developing AI agents
• The economics of AI agents compared to human agents (10-20¢ per minute vs. $1 per minute)
• How AI agents achieve 97% containment rates versus the 20-40% traditional benchmark
• The challenges of enterprise sales in the contact center space
• The evolution of Regal's go-to-market strategy as AI capabilities have rapidly advanced
• The future of voice as the primary channel for brand engagement

GTM Lessons For B2B Founders:

• Timing your product evolution is critical: Alex emphasizes the importance of not moving too early or too late when pivoting to new technology. "If you invested too early, it was a waste, but if you wait too late then all the first trials with every company would be with another AI provider, not with us," Alex explains. Their decision to wait until LLMs were capable enough before focusing on AI agents prevented them from wasting resources on soon-to-be-obsolete technology while still allowing them to be early in the market.
• Enterprise sales requires embracing the process: When moving upmarket, Alex learned that trying to rush enterprise sales leads to poor outcomes. "If you try to rush it in an enterprise environment, which is possible, you're not going to have a good outcome," he shares. B2B founders should understand the sales timeline for their specific industry and be prepared for longer, more complex sales cycles when targeting enterprise customers.
• Build foundational technology that transcends AI hype: Regal's advantage came from building deep platform infrastructure before AI agents were ready. "Most of the companies that exist today, all they've ever built is this thing that interacts with the customer, the agent itself, the voice and the LLM, which is relatively trivial actually," Alex explains. By building integrations with customer data systems, decision engines, and channel management tools first, they created a more comprehensive solution that could quickly incorporate AI advances when the technology matured.
• Reconsider conventional marketing channels: Alex notes that traditional B2B marketing approaches are losing effectiveness: "A lot of the traditional channels that used to work just don't work or are not efficient anymore. So paid SEM, traditional sponsorships of online content, writing blog posts in some big paper... a lot of these demand gen channels are just highly ineffective." Founders should prioritize breaking through with authentic founder-led storytelling rather than relying solely on conventional demand generation tactics.
• The economics of AI can reverse long-standing business practices: Regal AI's solution flips conventional contact center wisdom on its head. As Alex explains, "Instead of calls being the most expensive thing you have, AI calls are the cheapest channel you have. So you lead with those calls and you do as many calls as possible because it's cheaper than any other channel." B2B founders should look for opportunities where AI fundamentally changes the cost structure of traditional business operations.

SuperAnnotate is revolutionizing how companies manage their AI training data with a comprehensive infrastructure platform. Having raised over $53 million in funding, SuperAnnotate has evolved from a specialized algorithm for autonomous vehicles to a centralized data hub that enables enterprises to collaborate with multiple service providers and internal teams. In this episode of Category Visionaries, we spoke with Vahan Petrosyan, CEO and Co-Founder of SuperAnnotate, who shared his journey from PhD student to tech founder and unpacked his vision for creating what he describes as "a database for training data" - similar to Databricks but specialized for AI training data.

Topics Discussed:

• SuperAnnotate's evolution from algorithm to comprehensive data labeling infrastructure

• The journey from academic research to founding a tech startup

• How an early contract with an autonomous driving company validated their solution

• The strategic pivot from competing with service providers to creating a collaborative ecosystem

• The transformation of their go-to-market strategy to create stickier enterprise relationships

• SuperAnnotate's focus on building a centralized training data platform for enterprise AI

• The importance of automation and "SuperAnnotate agents" for AI data operations

• How customizability has enabled SuperAnnotate to support diverse generative AI use cases

Vectara is pioneering the field of Retrieval Augmented Generation (RAG), addressing the critical challenge of hallucinations in AI systems. With over $53 million in funding, Vectara has positioned itself as the go-to platform for enterprises seeking to combat "RAG sprawl" while building AI assistants and agents that are accurate, secure, and explainable. In this episode of Category Visionaries, I sat down with Amr Awadallah, CEO and Founder of Vectara, to explore his journey from Egyptian immigrant to serial entrepreneur and his vision for creating AI systems that enterprises can truly trust.

Topics Discussed:

• Amr's journey from Egypt to Stanford in 1995 and how it transformed his career aspirations
• The entrepreneurial "infection" at Stanford that led Amr away from academia
• Founding and selling his first startup, Activia, to Yahoo in just one year
• The comparison of creating successful companies to the joy of having children
• How Vectara addresses the critical problem of hallucinations in large language models
• The concept of "RAG sprawl" and why enterprises need centralized governance
• Amr's framework for evaluating startup opportunities: technological inflection points, real problems, and great teams
• Why this AI revolution is a bigger technological inflection point than the internet or big data

GTM Lessons For B2B Founders:

• Focus on your unique value proposition: Amr emphasized the importance of standing out in a crowded market by focusing on what makes you unique. Vectara doubled down on accuracy and hallucination detection, becoming known as the company to combat AI hallucinations. B2B founders should identify what they can be known for that differentiates them from competitors.
• Choose your go-to-market strategy deliberately: When deciding between product-led growth or enterprise sales, commit fully to the approach that fits your business. For enterprise sales, implement account-based marketing focused on your ideal customer profile, host targeted field events, and use strategic dinners with compelling speakers to attract key prospects.
• Don't try to boil the ocean: The number one reason companies fail after team issues is lack of focus. Early-stage founders should maintain agility to test different approaches but quickly narrow focus based on where they're getting traction. Treat use cases as "two-way doors" - try them, keep what works, and move on from what doesn't.
• Build for the coming AI agent revolution: Amr predicts we'll move from the current "AI assistant phase" (requiring human oversight) to the "AI agent phase" (fully autonomous AI) within one year. B2B founders should position their products for this transition, particularly focusing on accuracy and trust as critical requirements for enterprise adoption.
• Leverage technological inflection points: Major technological shifts create gaps that allow startups to disrupt established players. Amr has built companies around three major inflection points: the internet (Activia), big data (Cloudera), and now large language models (Vectara). B2B founders should identify inflection points relevant to their industry and build solutions that capitalize on the new opportunities they create.

Ducky AI operates as a pre-LLM compute layer, enabling businesses to make their proprietary data accessible to large language models without compromising security or privacy. In this episode of Category Visionaries, James O'Brien shares the journey of pivoting from a customer support solution to becoming a machine learning infrastructure tool that helps developers find and transform internal business data for optimal LLM consumption. With $2.7 million in funding, Ducky is positioning itself as the essential bridge between enterprise data and AI systems, making advanced AI capabilities accessible to technical teams without requiring extensive ML expertise.

Topics Discussed:

• Ducky's evolution from a customer support solution to a developer-focused ML infrastructure tool
• The validation process that led to identifying knowledge accessibility as a core market problem
• How and why the team executed their pivot in just three months
• The challenges of defining an ideal customer profile in the rapidly expanding AI space
• Building a go-to-market strategy in Nashville's emerging tech ecosystem
• Fundraising lessons learned during the SVB collapse

GTM Lessons For B2B Founders:

1. Listen when developers ask for your infrastructure: James discovered their true product-market fit when developers started requesting access to Ducky's knowledge retrieval infrastructure rather than their customer-facing application. "We had a revelatory moment where we realized that a bunch of developers had asked for access to our infrastructure, our knowledge retrieval infrastructure. And that's kind of what we're good at." This insight led to their pivot toward becoming an API-first tool that matched their technical strengths with the right audience.
2. Validation is a superpower: The Ducky team excels at gathering unbiased feedback from potential customers. When considering their pivot, they embraced this strength: "I think one of the things that we're best at as a team is validation. I think we're really good at drawing relatively unbiased... input and feedback from people that we're interviewing or talking to." For B2B founders, this emphasis on rigorous customer validation before building can be the difference between success and wasted engineering resources.
3. Make pivot decisions with data, not emotion: When considering a change in direction, Ducky time-boxed their exploration to three weeks, built multiple prototypes, and showed them to potential customers. "It was pretty clear after three weeks that one was not only a better use of our skills and time, but also a better market fit." B2B founders should approach pivots methodically, setting clear timelines and success criteria for validation.
4. Design pricing that aligns with value creation: James emphasizes usage-based pricing that fundamentally connects revenue to customer value: "If you use it and it works, you will use it more. And that means that we're doing our job. And that's awesome. That's all I ever want to do, quite frankly, is get paid for actually bringing value to people." This approach creates natural incentives for both the vendor and customer, unlike the multi-year contracts that often create misaligned incentives.
5. Look beyond AI hype to focus on business problems: James discovered that many companies have been tasked to "do something with AI" without clear objectives. "People are like, 'hey, we got to do something with AI,' but we don't know what that is. And then they think so deeply about, 'hey, how are we going to construct this?'" B2B founders should help customers cut through the hype by focusing on the underlying business value and specific problems to solve, rather than getting lost in technical details.

Fiddler is pioneering AI observability technology to help enterprises deploy trustworthy artificial intelligence. With $68 million in funding, Fiddler provides a "watchdog" platform that continuously monitors AI models, enabling companies to maximize ROI while minimizing risks. In a recent episode of Category Visionaries, I sat down with Krishna Gade, CEO and Founder of Fiddler, to discuss the critical importance of transparency in AI systems and how businesses can safely operationalize AI capabilities in an era where AI applications are rapidly proliferating across industries.

Topics Discussed:

• The evolution of AI from classical machine learning to generative AI and agentic systems
• The transparency challenges associated with increasingly complex "black box" AI models
• How Fiddler's observability platform provides insights into AI model performance and trustworthiness
• The emergence of "AI observability" as a defined category in enterprise tech
• The tension between maximizing AI's business value while minimizing associated risks
• The ongoing transformation of enterprise software as AI becomes central to every application
• Major AI trends including decreasing model training costs and the rise of automation through AI agents

CoactiveAI is pioneering the multimodal application platform (MAP) category, enabling companies to unlock value from their visual content through AI-powered search, tagging, and analytics. In this episode of Category Visionaries, I sat down with William Gaviria Rojas, Field CTO & Co-Founder of CoactiveAI to discuss his inspiring journey from Colombian refugee to MIT graduate and successful founder, as well as CoactiveAI's vision for transforming how enterprises work with visual content.

Topics Discussed:

• William's journey from Colombian refugee to MIT and entrepreneurship
• The founding story of CoactiveAI and early backing from a16z
• Evolution of the AI landscape pre and post-ChatGPT
• Creating the multimodal application platform category
• Building AI applications for media and entertainment companies
• Challenges and opportunities in trust and safety use cases
• Going from prototypes to production AI implementations

GTM Lessons for B2B Founders:

• Category Creation Through Customer Discovery: Rather than trying to serve every vertical initially, CoactiveAI went through a deliberate "market annealing" process. Through extensive customer conversations, they discovered that media and entertainment companies had the most complex content challenges and could derive the most immediate value from their technology. Focus on finding the intersection of your technical capabilities and the markets where you can have the biggest impact.

• Technical Differentiation in a Crowded Market: When the AI space became crowded post-ChatGPT, CoactiveAI maintained their edge by emphasizing their deep technical capabilities built since 2021. William notes, "We weren't some sort of light wrapper around OpenAI or ChatGPT." In emerging technology markets, having genuine technical differentiation can help you stand out from opportunistic newcomers.

• Enterprise Partnership Strategy: Rather than pursuing transactional sales, CoactiveAI focuses on building true partnerships with enterprise customers. As William explains, "If you just come to somebody like a vendor and that's really all you're kind of doing, this is just kind of a transactional sale." Instead, they invest in deeply understanding customer pain points and collaborating on solutions, which has led to successful deployments with major companies like NBCUniversal and Thomson Reuters.

• Evolving Value Proposition: William observed that while 2024 was about prototypes and proofs of concept, 2025 is focused on demonstrating real ROI from AI implementations. He emphasizes the importance of having clear customer success stories and ROI metrics as the market matures. B2B founders should anticipate and adapt to these shifts in buyer expectations.

• Vision-Driven Culture: CoactiveAI published their culture on GitHub as one of their first actions, emphasizing that success isn't just about business metrics but also about how you achieve that success. William's vision includes building "ladders" for the next generation of diverse founders. This clear mission has helped attract talent and align the team around common goals.