The Founders and Leaders Series

Episode 14: Lewis Reeves, Walr

Episode Overview Lewis Reeves, founder and CEO of Walr, on building a data infrastructure platform that powers market research agencies at scale. Episode Highlights * Agentic 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 Guest Lewis 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 [https://www.insightplatforms.com/].

Episode 13: Samuel Cohen, Fairgen

Episode Overview Fairgen founder Samuel Cohen on synthetic data, digital twins and how AI is making market research more accessible to more organisations. Episode Highlights * A 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 Guest Samuel 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 [https://www.insightplatforms.com/].

Episode 12: Kathy Cheng, inca

Episode Overview Kathy Cheng, founder and CEO of inca, on why better survey experiences produce better data — and what the industry needs to change. Episode Highlights * From 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 Guest Kathy 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 [https://www.insightplatforms.com/].

Episode 11: Ange Taylor - AddMaple

Episode Overview Angelique Taylor, founder of AddMaple, shares how frustration with fragmented data tools inspired a visual-first exploration platform built for insights teams. Episode Highlights * From 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 Guest Angelique 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 [https://www.insightplatforms.com/].

Episode 10: Frederic-Charles Petit, CEO, Toluna

Episode Overview Frédéric-Charles Petit, Founder and CEO of Toluna, on building a 25-year-old consumer insights company for the age of AI. Episode Highlights * Four 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 Guest Frédéric-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 [https://www.insightplatforms.com/].