Voice AI Upends Market Research with Faster, Cheaper Interviews

Keplar, a two-year-old market research startup, announced a $3.2 million seed round led by Kleiner Perkins to scale a voice-AI platform that runs customer interviews and delivers analysis in hours instead of weeks.

Founded by Dhruv Guliani, who worked on speech and voice models at Google, and machine learning engineer William Wen, Keplar automates the steps that once required expensive human moderators and long turnaround times.

What Keplar does

Customers build a study in minutes: any product question becomes an interview script, the voice assistant reaches out to participants — including customers pulled from a CRM when authorized — and conducts probing, natural-sounding conversations.

Conversations are transcribed and packaged into report-ready artifacts like slide decks and executive summaries, mirroring outputs that traditional consultancies provide but at far lower cost and much faster cadence.

Why now

Voice bots existed before, but rapid improvements in large language models and speech technologies mean AI moderators can hold natural, probing conversations. Keplar’s founders say participants sometimes forget they’re speaking to AI — a signal that the experience is convincing enough to surface richer feedback.

Early traction and competition

Keplar already counts Clorox and Intercom among customers. But it’s not alone: Outset and Listen Labs are well-funded competitors racing to capture the same shift from manual to automated research.

Practical impact for organizations

For product teams, this can mean rolling out user feedback loops across more segments and iterations without large consultancy budgets. Brands can scale qualitative interviews to thousands of customers, surfacing nuanced drivers of satisfaction, feature requests, and pricing sensitivity faster than quarterly studies allow.

Public sector or regulated industries could use similar tooling for stakeholder engagement, but must weigh consent, data residency, and transparency about AI use.

Risks and guardrails

Automating interviews raises familiar AI questions: sampling bias, consent and disclosure, model hallucination in summaries, and security when tapping customer CRMs. Human oversight, transparent consent flows, and validation against manual interviews remain critical safeguards.

What leaders should do next

Start small: run parallel AI and human-moderated studies to benchmark quality and identify bias. Prioritize integrations with CRMs and analytics so voice-derived insights flow into product roadmaps and executive dashboards. Treat the AI moderator as a force-multiplier, not a full replacement, until validation is complete.

QuarkyByte’s approach is to combine technical validation with pragmatic operational design: we map where automation brings the biggest ROI, test for bias and fidelity, and design pipelines that turn qualitative conversations into metrics leaders can act on.

Keplar’s funding and early deals signal a bigger trend: market research is moving from quarterly, boutique projects to continuous, AI-driven feedback loops. That shift promises faster decisions and lower costs — but only if organizations invest in validation, privacy, and integration from day one.

For teams that rely on customer insights, the question is no longer whether voice AI can run interviews, but how to deploy it responsibly to amplify truth rather than introduce noise.

• Faster turnaround: hours or days instead of weeks
• Lower cost per interview compared with traditional firms
• Scalable qualitative research across segments and iterations
• Requires governance: consent, bias checks, and security