The qualitative research tech market is exploding—with every platform promising to transform your workflow, supercharge your analysis, and surface insights you’d never find on your own. But let’s be honest: not all tools are created equal, and picking the wrong one can seriously slow your team down.
This guide explains how we are comparing six of the top AI-powered qualitative analysis platforms. So, that you can do the same (or just review our findings when they come out).
What You Actually Need from a Qual Analysis Tool
Before we dive into the comparison, let’s talk non-negotiables. After working with dozens of market research teams, these are the four features we consistently see as mission-critical:
1. Platform Agnostic(aka No Vendor Lock-In)
If you are looking for one platform that you can collect and analyze all types of data, there are lots of other options (e.g. Aha, Recollective, D-Scout). Since we often use our clients' preferred data collection platform, we need something that is agnostic. Ideally, we want something that can take data from Zoom, survey platforms, video interviews, focus groups regardless of the platform on which it was recorded.
2. Real Analysis of Real Qual Data
We’re talking long-form interviews, group transcripts, maybe even ethnographies. The tool needs to go beyond surface-level keyword searches and be able to extract themes, analyze sentiment, and find patterns across segments. If it can’t handle complexity, it’s not worth our time.
3. Speed That Doesn’t Sacrifice Depth
AI should make things faster and better. A good platform helps to process large volumes of messy data quickly, so our team can spend less time coding and more time interpreting and storytelling.
4. Verifiable Quotes (Because Accuracy Matters)
AI insights mean nothing if we can’t back them up. We need accurate, attributable quotes to support our findings—period. Bonus points if it’s easy to pull those quotes into presentations or client deliverables.
The Platforms We Put to the Test
We evaluated six tools that are leading the pack when it comes to AI-powered qual analysis. Each has its own strengths—and some clear trade-offs.
🔹 CoLoop
🔹 DoReveal
🔹 Dovetail
🔹 flowres (by myMRPlace)
🔹 marvin
🔹 Quillit (by Civicom)
Feature-by-Feature: How They Stack Up
We ran all six tools through the same dataset and evaluated them across six dimensions:
1. Ease of Setup
How fast can you get started? We looked at upload flow, speaker identification, and how intuitive it is to set up a new project.
2. Ease of Use
A steep learning curve kills adoption, and there are tools that more naturally fit into the way we, as researchers, typically work.
3. Depth of Analysis
Does it go beyond surface-level tagging? We prioritized platforms that could extract themes, analyze sentiment, and find cross-project patterns.
4. Accuracy of Insights
How trustworthy are the results? We looked closely at quote extraction, sentiment tagging, and theme identification.
5. Security & Compliance
GDPR, SOC 2, audit trails—we checked whether each platform met enterprise-level data standards.
6. Reporting Capabilities
You’re not just analyzing—you’re presenting. We looked for flexible export formats, highlight reels, and easy integration into decks.
So... Which One’s Right for You?
There’s no single winner here. It comes down to what matters most to your team. Do you need something that plays well with clients? Prioritize security? Want a platform your whole team can learn in a day?
Ask yourself:
- What are our must-have features?
- What’s our budget?
- How technical is our team?
- Do we need to meet specific compliance standards?
Most of these platforms offer free trials—use them. Nothing beats getting hands-on to see what actually fits your workflow.
If you’re overwhelmed by options, you’re not alone. But with the right tool in place, your team can stop drowning in transcripts and start uncovering real insights faster.
Let the AI do the heavy lifting. You focus on the thinking.
