In-depth interviews.
At survey scale.
Qualitative research is the most powerful way to understand people. But it's expensive to run, slow to analyze, and impossible to scale.
Loda's AI voice agents conduct real conversations, not surveys with your employees, customers, and partners.
Trusted by forward-thinking teams
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Work-Life Balance
Sentiment
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Active
Interviews
From brief to insights
in 3 steps
Upload your brief
Define your research goals and key questions. Our AI constructs an adaptive interview guide.
Deploy via link
Send a simple link to your audience. They talk naturally to Loda in their preferred language.
Instant Findings
No transcription needed. Get answers immediately as interviews complete.
Deeper than
any survey
Loda asks follow-up questions, probes for context, and lets respondents express themselves freely. You get the richness of a qualitative interview — at a scale no human team could match. Insights start flowing as the first interviews complete, so you move from brief to findings in hours, not weeks.
Cost per 100 interviews
10x more affordable, zero commitment
AI interviews cost a fraction of human-led ones — making qualitative research viable for projects and budgets where it simply wasn't before. Offer your clients more, without shrinking your margins. Pay per interview, only when you run projects. No subscription, no lock-in.
Reach further,
reduce bias
Loda reaches your entire population — including those who'd never speak freely to a human interviewer. Without an interviewer in the room, people speak more candidly, and the voices that usually go unheard get equal weight. Run interviews in any language, with any audience, local or global, with the same ease and the same quality of conversation.
Active conversations
Built for every
research need
Employee listening
Go beyond annual surveys. Understand what your clients' employees actually think with conversations.
Customer CX
Replace shallow satisfaction scores with genuine insight. Loda interviews customers at scale.
Product feedback
Understand not just what users think of a product, but why — and what they'd need to love it.