The Founders and Leaders Series

Episode OverviewTerence McCarron of OpinionRoute on AI in market research, agile agencies, data quality and synthetic data.Episode HighlightsOpinionRoute's Navigator platform shows how AI adds the most value when it is built on a foundation of applied human expertise rather than used as a replacement for it.Terence argues the future of the industry favours small to mid-size agencies, because agility paired with high-level expertise beats scale when labour and budgets are under pressure.Years of measuring data quality have convinced Terence that the real risk to survey research is the commoditisation of respondents' time and trust, not the methodology itself.Legacy survey software built on old technology is heading for disruption, as AI-era tools let smaller, leaner competitors move faster and more cheaply.AI moderation delivers real speed to insight, but Terence has seen recruitment and validation shortcuts produce failures that slower, more careful methods avoid.Terence is sceptical of synthetic data used on its own, but sees real promise in overlaying opinion data on large, constantly refreshed behavioural datasets.About the GuestTerence McCarron is the founder and CEO of OpinionRoute, a tech-enabled services business that has spent twelve years supporting small to mid-size market research agencies, including building its core Navigator platform. Before founding OpinionRoute, he ran North America for Cint, a programmatic sample company, where he saw early signs of the data quality and labour challenges that would later reshape the research industry. He has grown OpinionRoute to around 45 people and is a vocal advocate for the small to mid-size agency model in an era of AI-driven disruption.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewCaplena co-founder Maurice Gonzenbach on building AI feedback intelligence, where foundation models fall short, and how surveys will evolve.Episode HighlightsCaplena is a feedback intelligence platform that helps enterprises collect open-ended feedback from surveys, reviews and support data, combine it with structured metadata, and surface proactive insights rather than waiting to be asked a question.The company started in 2018 from Maurice's academic work on sentiment analysis, after conversations with market researchers revealed how poorly served the problem of coding open-ended questions was at the time.Maurice is candid about where general-purpose AI tools like Claude now outperform specialist software: for quick, one-off, low-stakes surveys, he recommends people skip Caplena entirely. Enterprise use cases still need Caplena's reproducibility, quality assurance and system of record.He sees consulting and software businesses converging, and expects survey research itself to change fundamentally, moving from static forms to voice input and two-sided, conversational follow-up with respondents.Maurice pushes back on the idea that synthetic data will replace real customer research, arguing that using a "stochastic randomness engine" to manufacture certainty works against what research is meant to achieve.On building Caplena, he reflects on the classic startup trap of over-hiring after raising funding, and on how a co-founder and a network of non-competing startup peers provided the direct, honest challenge that shaped better decisions.About the GuestMaurice Gonzenbach is co-founder and CEO of Caplena, a feedback intelligence platform based in Zurich, Switzerland. With a background in computer science and machine learning, he began working on sentiment analysis during his academic thesis before co-founding Caplena in 2018 with Pascal to solve the problem of coding open-ended survey responses. Since then, he has grown the company from a niche text-analytics tool into a broader insights platform serving mid-size and enterprise clients, giving him a close-up view of how AI, and more recently large language models, are reshaping the market research and customer experience industry.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewTovah Paglaro, founder of Avalanche Insights and Fathom AI (now QuestionPro), on building and exiting research tech companies at the intersection of human expertise and AI.Episode HighlightsFrom storytelling to strategy: Tovah’s career began in TV production, where she learned lessons about trusting specialists, building cultures of “ambitious sprints and actual rest,” and the power of shared narrative that she carried into every company she builtPioneering qual at scale: Avalanche Insights was a tech-enabled consultancy doing qualitative research at scale before the current wave of AI, growing to $5 million in revenue and 60 people before its acquisitionThe services vs. tech dilemma: Building a company that blends human expertise with technology means managing two different incentive structures and cultures. VCs once rejected the model; now everybody wants it.Spinning out Fathom AI: The decision to extract the core text analytics technology into a standalone SaaS platform, the lesson that internal tools don’t translate directly to external products, and the eventual acquisition by QuestionPro.Lessons from bad hires: Why early-stage founders almost inevitably hire wrong, and why the real test of leadership is how you handle it, from accountability and speed to supporting people on the way out.Strategic exits are relational: Most reasonable exits are strategic, built on trust and relationships rather than pitch decks. Tovah shares practical advice on what founders should be doing long before they’re ready to sell.About the GuestTovah Paglaro is a serial founder with over 20 years of leadership experience across media, civic technology, and market research. She built and exited Avalanche Insights, which pioneered qual at scale as a tech-enabled consultancy, and Fathom AI, a text analytics platform recently acquired by QuestionPro. She now consults and advises early-stage companies and organisations navigating growth, AI transformation, and the intersection of human and technology-driven services.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewLewis Reeves, founder and CEO of Walr, on building a data infrastructure platform that powers market research agencies at scale.Episode HighlightsAgentic workflows in practice: Walr has built over one hundred AI agents; one example is a two-agent, one-human pipeline for survey programming where one agent builds the survey, a second verifies it, and a human reviews only the flagged exceptions. Project volumes managed per person have roughly doubled in six months.Democratising AI beyond engineering: Over half of Walr's 100+ internal agents were built by operations staff, not the product or engineering team: a sign that AI tooling has moved well beyond technical teams.Synthetic data and digital twins: Lewis shares a measured view on synthetic audiences. Walr is running internal trials, but sees customer demand as still cautious, particularly where validation cycles are long, such as political polling.Data quality reframed: Rather than treating data quality as a single issue, Lewis argues for breaking it into distinct categories (survey design, respondent experience, fraud, and inattention) each requiring different solutions.Lessons from scaling: Lewis reflects on the challenges of rapid growth, the importance of following your instincts on change, maintaining close attention to cash flow, and the irony of a market research business that had to learn to listen more carefully to its own customers.About the GuestLewis Reeves is the founder and CEO of Walr, an enterprise platform providing end-to-end online quantitative data collection — covering survey building, audience access across 100+ markets, and data structuring. He co-founded his first research business at 23, which became Savanta, before launching Walr at 28. Under his leadership, Walr has processed over 1.5 billion survey questions and appeared on multiple fast-growth business lists.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewFairgen founder Samuel Cohen on synthetic data, digital twins and how AI is making market research more accessible to more organisations.Episode HighlightsA taxonomy of synthetic data — Samuel maps the space across two axes: methodology (directional to foundational) and stakes (low to high), showing why different approaches suit different research contexts.Why "boosting" has become standard — Fairgen's augmentation technology is now used by brands including T-Mobile and L'Oréal to dramatically expand the number of segments they can reliably report on, with three years of rigorous statistical validation behind it.The problem with fully synthetic panels — Samuel explains why prompting LLMs to simulate survey respondents produces poor results: large language models are "averaging machines" that flatten individual variance and produce near-identical responses.How Fairgen builds category-level digital twins — Each twin is anchored to a real person and enriched with category-specific survey data, clickstream, transactional and live news data, refreshed quarterly to maintain accuracy.The democratisation of research — AI-powered tools — including digital twins and AI-moderated qualitative research — have the potential to make insights accessible to startups, SMBs and underserved markets that have historically been priced out.Founder lessons — Samuel shares two core pieces of advice: challenge every assumption, and invest consistently in building relationships and networks.About the GuestSamuel Cohen is the founder and CEO of Fairgen, a synthetic data company serving the market research industry. He studied mathematics at Oxford and completed a PhD at UCL, spending the majority of his doctoral research at Facebook AI Research Labs, where his team built foundational generative models that now underpin mainstream image and video generation tools. He founded Fairgen four years ago, applying that background to the challenges of survey research and consumer insights. Fairgen works with large insight agencies and directly with enterprise brands including T-Mobile, L'Oréal and Coty.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewKathy Cheng, founder and CEO of inca, on why better survey experiences produce better data — and what the industry needs to change.Episode HighlightsFrom qual to quant — by accident. Kathy built inca out of frustration with traditional surveys, bringing qualitative thinking — projective techniques, conversational flow, participant engagement — into quantitative research at scale.The participant experience problem. Poor data quality is not the fault of respondents. It is the result of survey designs that have barely changed since the 1930s. Improving the experience improves the data.AI as part of the whole, not a bolt-on. inca's AI moderation capability is central to the product, but Kathy argues that conversational AI embedded in a poor overall experience will not deliver meaningful results on its own.Synthetic data: a useful tool, not a replacement. Kathy sees synthetic data as a short-term response to declining survey quality, and worries that adopting it at scale means giving up on the belief that people have genuine things to share.The blurring of qual and quant. The more significant shift in the industry over the next few years will not be technological — it will be human. Researchers will need to develop skills across both traditions to get the most from new platforms.Always go to a meeting with an ask. The single most useful piece of business advice Kathy has received — and one she now applies consistently as a self-described introvert running a growing technology business.About the GuestKathy Cheng is the founder and CEO of Nexxt Intelligence, the company behind inca — which stands for Inquisitive Natural Conversation Agent.She started her career as a qualitative researcher and founded the business around a decade ago with a simple idea: that surveys should work more like conversations. inca combines structured survey tools with AI moderation, projective techniques and conversational probing to improve the participant experience and the quality of the data it produces. The platform is used for both quantitative research and qualitative research at scale.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewAngelique Taylor, founder of AddMaple, shares how frustration with fragmented data tools inspired a visual-first exploration platform built for insights teams.Episode HighlightsFrom UX researcher to founder: Ange's background in user research — spanning qualitative interviews, SQL data, and survey analytics — led her to build a tool that bridges the gap between different data types in a single visual interface.What AddMaple does: AddMaple is a visual-first data exploration platform that ingests survey data, online reviews, customer support tickets, social media feedback, and data lakes, allowing insights teams to filter, cross-tabulate, and identify themes with automatic statistical testing throughout.The AI integration debate: Ange makes a clear architectural distinction between generative AI and deterministic analysis — in AddMaple, AI sits as an interpretive layer above a stats engine that runs all calculations, ensuring reproducible, traceable results every time.Synthetic data: hype vs. reality: Ange is sceptical of synthetic data as a replacement for real human research, though she uses AI personas herself as a sparring partner for ideation and hypothesis testing — not as a substitute for statistically valid findings.The undervalued UI: Against the prevailing trend of chat-first AI interfaces, Ange argues that a well-designed user interface — one that integrates AI rather than replacing the GUI with it — remains under-appreciated and is central to her product philosophy.Bootstrapping in an insight-tech world: Building fully bootstrapped in a space dominated by well-funded competitors has kept Ange close to her users, though she reflects honestly on the trade-offs — including reconsidering how she presents that status to enterprise prospects.About the GuestAngelique Taylor is the CEO and founder of AddMaple, a visual-first data exploration platform designed for market researchers and insights professionals. She began her career as a user researcher in FinTech, developing an early fluency in both qualitative methods and quantitative data analysis. That dual perspective became the foundation for AddMaple, which she has built as a fully bootstrapped company while based in Ljubljana, Slovenia. Ange is a vocal advocate for high-quality user interface design and rigorous, reproducible research methods in an era of rapid AI adoption.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewFrédéric-Charles Petit, Founder and CEO of Toluna, on building a 25-year-old consumer insights company for the age of AI.Episode HighlightsFour pillars, one platform. Toluna's model combines a global consumer panel, proprietary technology, specialist methodology, and people to deliver integrated consumer insight at scale for enterprise brands.Synthetic personas grounded in real data. Toluna's AI-driven synthetic personas were built on a foundation of first-party panel data and machine learning developed from 2019 — well before the generative AI boom — enabling fast, high-correlation answers to real business questions.AI as the engine of democratisation. Frédéric-Charles argues that AI is the most significant transformation force in market research, enabling research to move at the speed at which content is now created and decisions are now made.From gatekeeper to door opener. The insights function inside enterprise organisations is shifting — from controlling access to research to enabling broader teams to act on consumer data with confidence and speed.Adoption is maturing, not just experimenting. Customer uptake of AI-first research solutions at Toluna has already surpassed full-year revenues from the previous year, pointing to a market that is moving from pilot to embedded practice.Strategy and tactics in equal measure. After 25 years building Toluna, Frédéric-Charles's core leadership lesson is the need to balance long-term strategy with close operational involvement — what he calls "top down and bottom up."About the GuestFrédéric-Charles Petit is the Founder and CEO of Toluna, a global digital consumer insights company he has built over more than 25 years. Under his leadership, Toluna has evolved from a panel provider into a technology-driven insight platform serving enterprise brands worldwide.The company's current focus is on embedding AI across both quantitative and qualitative research, including the development of synthetic personas and AI-powered ad testing tools. Frédéric-Charles is based in France and is a long-standing figure in the research technology industry.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewAndrew Cooper, founder and CEO of Verve, on synthetic personas, the respondent crisis, and a new model for AI-powered insight.Episode HighlightsFrom access panels to AI simulations: Andrew traces Verve's evolution from one of the early online panel companies to a specialist in AI-powered synthetic personas — built on the conviction that "a good simulation is better than an average sample."Addressing the respondent crisis: Rather than replicating large samples artificially, Verve combines proprietary client data with curated sources to build auditable, validated simulations that correlate at 0.9 with real human responses.Where synthetic personas work best: B2B and specialist audiences — where recruiting real respondents is slow, expensive or unreliable — are proving to be the strongest use cases, alongside healthcare and financial services segments.Humans and simulations working together: Verve's model keeps real people involved in the insight process, using simulations to handle onerous or tactical tasks, while human respondents contribute depth, validation and qualitative richness.The skills challenge for the industry: AI is reshaping the capabilities research businesses need — consultative skills, critical thinking and an AI-native mindset are growing in importance, while routine analytical work is declining.Oracles and internal AI agents: Verve has built 60 internal AI tools ("Oracles") covering specialist areas of their workflow, effectively doubling productive capacity without adding headcount.About the GuestAndrew Cooper is the founder and CEO of Verve, a UK-based insight technology company specialising in AI-powered synthetic personas and simulations. He previously co-founded Research Now, one of the pioneering online access panel businesses.With over two decades in the research industry, Cooper has been an early advocate for respondent quality and engagement, and now leads Verve's work helping brands including Mars and Samsung generate board-level insight through a combination of real human data and intelligent simulation.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.

Episode OverviewOlaf Lenzmann discusses Market Logic's AI evolution, synthetic personas, innovation workflows and the lessons from building a research tech platform over 15 years.Episode HighlightsAI-powered insights platforms: How Market Logic's DeepSights connects internal and external data sources, transforming knowledge management into active decision support through generative AI capabilitiesThe rise of synthetic personas: Why AI-generated personas based on proprietary research data are gaining strong demand, enabling organisations to activate strategic assets and make insights more accessible across teamsRethinking innovation workflows: How AI is shifting from simple automation to collaborative tools that present comprehensive options, requiring deeper human judgement rather than eliminating thinkingManaging AI governance and quality: The evolving role of insights teams as gatekeepers who establish guardrails, vet data sources and build systems thinking into AI-human workflowsBuilding for scale versus customisation: Lessons on balancing enterprise customer needs with product development, the importance of thought leadership and avoiding reactive feature-buildingAdvice for research tech founders: Why sustainable competitive advantage requires more than technology capability—whether through human expertise, partner ecosystems or compounding knowledge modelsAbout the GuestOlaf Lenzmann is Co-Founder and Chief Innovation and Product Officer at Market Logic Software, a Berlin-based company that provides enterprise insights platforms. Market Logic's DeepSights solution enables organisations to integrate research, knowledge and data about consumers, markets and brands into a unified platform. With approximately 15 years in the commercial market, the company serves major enterprises across CPG, automotive, financial services, retail and healthcare sectors. Olaf has led the company's evolution from knowledge management through to AI-powered insights activation and innovation support.Learn more about the impact of technology and AI on research, insights & analytics at Insight Platforms.