How Amazon and Chobani Use Strella’s AI Interview Platform to Revolutionize Customer Research

Traditional customer research has long been a bottleneck for brands that rely on deep qualitative insights. The process—from recruiting participants to moderating interviews and analyzing data—often stretches across eight weeks or more. In today’s fast-moving markets, where consumer preferences shift by the day, that delay can mean the difference between leading a category and lagging behind. This is where Strella, an emerging AI-powered research platform, has begun to change the rules. Recently adopted by Amazon and Chobani, the startup’s AI-driven interview system is helping global brands accelerate insights and streamline research cycles that once required entire teams and long timelines.

Traditional qualitative research has always been about depth over speed. Teams labor to find the right participants, conduct hours of interviews, and manually synthesize transcripts into reports. Costs add up, human error creeps in, and scalability becomes nearly impossible. Fraud in online surveys and respondent bias further compromise data quality. As companies compete for faster decision-making, even the most sophisticated research organizations are under pressure to deliver insights in days rather than weeks.

Strella’s founders, Lydia Hylton and Priya Krishnan, set out to tackle these challenges head-on. Their vision: automate the most time-consuming parts of qualitative research while preserving its human nuance. Strella’s AI interview platform simulates a skilled moderator capable of asking adaptive follow-up questions, detecting hesitation or evasion in voice tone, and surfacing key emotional cues during live conversations. Sessions resemble a natural video call, but with an AI interviewer that listens, probes, and adjusts its approach in real time. Human researchers can jump in to guide the session or observe silently as the AI conducts the dialogue.

Unlike scripted chatbots or static surveys, Strella’s system captures open-ended conversation and automatically converts it into structured insights. After each interview, the platform generates highlight reels, charts, and thematic summaries that reveal patterns across participants. One of its most distinctive capabilities is persistent mobile screen sharing—a feature particularly valuable for usability testing. Participants can share their screens continuously, allowing researchers to observe real-world app interactions while the AI collects behavioral and verbal feedback simultaneously.

The technology has already caught the attention of major brands. Amazon and Chobani are among the early adopters using Strella’s platform to accelerate product feedback and customer experience studies. Both companies face the same fundamental challenge: making rapid, data-informed decisions about products and marketing without sacrificing accuracy. With Strella, they can conduct hundreds of moderated interviews in a fraction of the time while maintaining rigor through automated synthesis. The platform’s early traction has been impressive, reporting zero customer churn and significant revenue growth as enterprise contracts expand.

One surprising discovery from Strella’s work is how candid participants can be when interacting with AI. Early studies suggest that respondents feel less pressure to impress or withhold opinions when talking to a non-human interviewer. The result: richer, more authentic qualitative data. This phenomenon points to a deeper shift in how trust and honesty might evolve in the research process, challenging long-held assumptions about human moderation being essential for emotional depth.

The benefits of Strella’s approach are clear—speed, scalability, and cost efficiency. What previously took two months can now be achieved in days. Automated fraud detection helps ensure respondent authenticity by flagging inconsistent responses or voice anomalies. Continuous learning across projects allows the system to improve its questioning and synthesis over time. For research teams, the platform’s repeatability means studies can be run continuously rather than episodically, embedding qualitative feedback loops directly into product cycles.

However, like any AI-driven system, Strella’s model comes with limitations and ethical considerations. The quality of insights still depends on thoughtful prompt design and oversight. AI moderation may occasionally miss emotional nuances or cultural context that a human expert would catch. Privacy and data consent remain critical, especially when video and behavioral data are involved. The founders emphasize hybrid use—AI for scale and efficiency, humans for judgment and empathy. This blended approach appears to be the future of credible research automation.

Strella enters a competitive field that includes established analytics platforms and survey tools like Qualtrics, but its differentiator lies in depth and flexibility. Where most solutions automate quantitative feedback or scripted interviews, Strella delivers free-form conversation and active learning. Its mobile-first screen-sharing design further sets it apart for digital product testing, where observing real behavior is often more valuable than verbal feedback. Some competitors are experimenting with synthetic respondents—AI-generated digital twins meant to mimic real consumers—but Strella’s focus remains firmly on real human interaction enhanced by artificial intelligence.

The implications for the future of research are significant. As AI moderation becomes normalized, companies can integrate qualitative understanding directly into their design and marketing workflows. Instead of waiting weeks for insight reports, teams can run continuous studies, test new ideas on demand, and adapt strategies in real time. For startups and Fortune 500s alike, this could redefine how decisions are made—replacing intuition-driven calls with constant, conversational feedback loops powered by AI.

Strella’s rise marks a turning point in how organizations listen to their customers. The combination of human empathy and machine speed opens the door to a new kind of qualitative research—one that is always on, globally scalable, and grounded in authenticity. For Amazon, Chobani, and others adopting the platform, the message is clear: the future of understanding consumers lies not in more data, but in smarter, faster conversations that bridge the gap between technology and human truth.