The Best AI Tools for Qualitative Research

Qualitative research has always been slow. Traditional interviews and focus groups generate rich insight, but transcribing, coding and synthesising those conversations takes days or weeks. AI has changed that. Today, you can automate large parts of the workflow without losing depth or quality.

AI can already handle core tasks like:

• Transcribing interviews and focus groups

• Coding and clustering themes

• Flagging sentiment and tone

• Searching across huge volumes of verbatims

• Producing structured summaries in minutes

Below is a practical overview of the leading AI tools for qualitative research, and where Tellet sits in this landscape.

Tellet

The leading platform for AI-moderated qualitative research and analysis

Tellet takes a different approach from every other tool on this list. Where most platforms focus on analysing transcripts after the fact, Tellet combines AI-moderated interviews with automated analysis in a single system. It runs the conversation, captures richer data, and produces insight far faster than any manual team could.

It is used by market research agencies and insights teams across the world for concept testing, innovation, brand positioning, CX, usage and attitudes, and large exploratory studies where speed and depth both matter.

What sets Tellet apart

AI-moderated interviews
Tellet runs one-to-one interviews at scale. Its AI moderator can probe, clarify, follow up and adapt questions based on what the participant says. The result is more spontaneous, contextual answers than standard surveys.

Voice-first design
Most tools analyse text. Tellet captures voice responses by default. That means you get tone, emotion and nuance that text-only platforms miss. Tellet also supports 50+ languages, which makes global fieldwork straightforward.

Transcription and translation built in
Interviews are automatically transcribed with high accuracy, cleaned of filler words, and translated if needed. No separate tooling required.

Automated qualitative analysis
Tellet identifies themes, clusters concepts, maps sentiment, surfaces patterns and builds structured insight frameworks. You can drill into any insight and trace it back to the exact verbatim.

Quant-style profiling
Because interviews scale to hundreds or thousands of respondents, Tellet can quantify themes, segment audiences and show differences between groups with real statistical weight.

Full reports in hours
These include themes, opportunity areas, tensions, decision drivers and representative quotes. Everything is traceable back to raw data.

GDPR-compliant and EU-ready
Built for European clients. Data is stored and processed within Europe.

Tellet is ideal for teams that want the depth of qualitative research with the speed and reach of quantitative methods. It is the only platform on this list that delivers both collection and analysis in one workflow, end to end.

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Dovetail

Dovetail centralises interview and feedback data for research and product teams. It auto-transcribes audio files, highlights quotes and supports manual tagging.

Key features include auto-transcription, browser-based coding, integrations with common work tools and visual reports. Its strength is collaboration and knowledge management rather than moderated research or deep analysis.

Zonka Feedback

Zonka aggregates open-ended feedback from multiple channels and uses AI to classify themes, detect sentiment, recognise entities and highlight urgent issues. It is strong for CX teams who deal with large volumes of qualitative feedback across touchpoints.

NVivo

A long-standing qualitative analysis tool used in academic and corporate settings. NVivo 15 includes an AI assistant to support coding and summarising. It handles many data types and offers advanced queries, but it has a steep learning curve and is built around manual workflows.

ATLAS.ti

Another established QDA tool with AI-assisted coding using GPT models. Strong for visual mapping and complex coding structures. Suitable for researchers who prefer a traditional qualitative workflow with some automation layered on top.

Remesh

A platform for large-scale “live digital focus groups”. Hundreds or thousands of participants respond to open-ended questions in parallel, and AI analyses responses in real time. It prioritises scale and speed over one-to-one depth.

ChatGPT

Not a dedicated research tool, but widely used for fast summarisation and exploratory coding. Useful as a co-pilot, though accuracy requires careful prompting and human validation.

Google NotebookLM

A research notebook powered by generative AI. Strong for document-level analysis, summarisation and Q&A, but not built specifically for market research or structured qualitative workflows.

Wrapping Up

AI has reshaped qualitative research. Teams now have options across the whole spectrum: traditional QDA tools with AI add-ons, feedback analytics platforms, large-scale group discussion tools, and AI helpers like ChatGPT.

Tellet stands apart because it unifies the process. Instead of running interviews in one place, cleaning data in another and analysing it somewhere else, Tellet handles the entire workflow: moderating interviews, capturing richer inputs, analysing them and producing reports in hours. It is the most efficient way to run modern qualitative research at scale.

If you want depth, speed and reliability in one system, Tellet is the practical choice.