Remesh is an AI-powered platform designed for conducting qualitative research through live focus groups, asynchronous surveys, and video interviews at scale. It enables users to engage up to 1,000 participants in real-time sessions or 5,000 in flexible modes, utilizing text-based conversations to gather and analyze feedback efficiently. The platform integrates voting mechanisms where participants rate each others responses, generating metrics like Percent Agree Scores to quantify consensus. AI tools automatically summarize responses, identify themes, and compare segments, reducing manual analysis time significantly. It supports over 35 languages with built-in translation for global reach.

Key features include Live mode for dynamic, timer-based interactions with real-time probing and moderation options like Autosend. Flex mode allows self-paced participation, ideal for overcoming time zone barriers, with on-platform recruitment ensuring vetted participants. The Video feature facilitates in-depth interviews, incorporating voice and emotion analysis via AI. Remesh ensures data security through SOC 2 Type II compliance and offers flexible support from self-service to fully managed services. General pricing structures are competitive, often based on usage or subscription, comparing favorably to alternatives in terms of scalability.

Competitors such as UserTesting AI focus more on video-based user testing, providing detailed behavioral insights but less emphasis on large-scale text voting. Qualtrics excels in comprehensive survey design and employee experience metrics, with stronger customization but potentially higher complexity for quick setups. Suzy offers similar consumer insights at scale, with agile questioning, though Remesh stands out in AI-driven analysis speed.

Users appreciate Remesh for its ability to combine quantitative and qualitative data, delivering actionable insights rapidly. However, limitations exist in template customization and stimuli randomization, which may restrict advanced research designs. The platform performs well in market exploration, concept testing, and employee engagement, detecting pain points through anonymous feedback.

In practice, Remesh fits organizations needing fast, reliable research without extensive resources, though it may require precise question crafting to maximize AI accuracy. Integration into existing workflows is seamless, supporting design, collection, and analysis stages effectively.