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AI-Moderated Research

AI Research Assistant

AI Research Assistant

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Qualitative insights at the speed of your business

Conveo automates video interviews to speed up decision-making.

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Definition:

An AI research assistant is a purpose-built analytical layer within an AI-moderated research platform that allows insights professionals to interact with their qualitative data the way they would with a knowledgeable colleague. Rather than exporting transcripts and coding manually, researchers can ask direct questions such as which segments showed the strongest emotional response or how attitudes shifted across markets, and receive sourced, traceable answers grounded in real participant responses. In the context of AI-moderated research, the assistant connects findings across multiple studies, flags contradictions between new and prior evidence, and can generate synthetic personas built from real responses to help teams pressure-test concepts before committing to a direction. For enterprise CMI and insights teams managing large volumes of qualitative data, this capability transforms a static archive into a continuously queryable intelligence layer.

How Conveo Does It

Conveo's AI research assistant lets teams interrogate their qualitative data in plain language, drawing on findings from AI-moderated video interviews with real participants across 50-plus languages. Studies can be launched in under 30 minutes, and because sessions run asynchronously at scale, the assistant has access to rich, multimodal data including speech, tone, and facial cues within days of fieldwork opening. Every answer is traceable to verbatim quotes and video clips, so stakeholders can verify the evidence behind any finding rather than accepting a summary at face value.

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Related terms.

AI-Moderated Research

AI Interviewer

Use an AI Interviewer to capture deep customer insights in days, not weeks. Get multimodal behavioral data from real participants at enterprise scale.

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AI-Moderated Research

AI-Powered Research

Learn what AI-powered research means for enterprise teams. Conveo delivers real qualitative insight at scale, with results in days, not weeks.

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AI-Moderated Research

AI-Moderated Research

Learn what AI-moderated research is and how it helps enterprise teams gather qualitative insights faster, at scale, using real participants and AI-led interviews.

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Frequently asked questions.

An AI research assistant is a conversational analysis interface built into a qualitative research platform. It allows researchers and insights teams to ask plain-language questions about their data and receive structured, sourced answers without manually reviewing transcripts. Unlike a generic AI chatbot, a purpose-built research assistant grounds every response in real participant data, with traceable links back to specific quotes, video clips, and thematic evidence from completed studies.
Enterprise insights teams are typically small relative to the volume of research requests they receive. An AI research assistant reduces the time between data collection and decision-ready output by allowing researchers to query findings directly rather than spending days in manual analysis. It also makes qualitative data accessible to non-researchers across brand, product, and strategy functions, extending the value of every study without requiring additional analyst time or specialist interpretation skills.
Traditional qualitative analysis involves a researcher manually reviewing transcripts, applying a coding framework, identifying themes, and synthesising findings into a report. This process is thorough but slow, often taking days or weeks per study. An AI research assistant compresses that cycle by automating initial coding and thematic clustering, then allowing researchers to interrogate the output conversationally. The key distinction is that the assistant accelerates analysis without removing researcher judgment. It surfaces patterns; the researcher interprets and contextualises them.
AI is shifting the research assistant function from a reactive, manual role to a proactive analytical capability. Modern AI research assistants can connect findings across multiple studies, flag when new evidence contradicts prior assumptions, and generate persona-based simulations grounded in real participant responses. This means insights teams spend less time organising and summarising data and more time on the interpretive and strategic work that requires human judgment. The result is faster turnaround without sacrificing the depth stakeholders expect.
Enterprise teams typically use an AI research assistant at two points in the research cycle. During active fieldwork, they use it to monitor emerging themes and identify gaps before the study closes. After fieldwork, they use it to answer specific stakeholder questions quickly, such as comparing responses across segments or pulling verbatim evidence for a presentation. Teams with a growing insight library also use the assistant to surface relevant findings from past studies when briefing new projects, reducing duplicated research effort.

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