MindCoder is a web-based tool that helps researchers quickly make sense of unstructured data — interview transcripts, meeting notes, open-ended survey responses. Rather than replacing the researcher, it augments the qualitative coding process with AI-powered suggestions, keeping human judgment at the center.
Open MindCoder 2.1 →In practice, researchers often need quick, lightweight coding — a fast pass over meeting notes, an initial sense of interview themes, or a rough categorization before committing to a full analysis. Traditional QDA tools aren't built for this informal mode of work.
MindCoder fills this gap. It supports the full spectrum from quick intuition to deep analysis, using LLMs to accelerate the parts that are tedious without compromising the parts that require thought.
MindCoder follows the "Code-to-Theory" model proposed by Saldaña — a structured yet flexible approach that builds understanding progressively from ground-level data through increasingly abstract layers.
Interview transcripts, notes, open-ended responses
LLM suggests codes; researcher refines and validates
Related codes grouped into meaningful clusters
Abstract patterns that answer research questions
MindCoder is designed around three principles that keep the researcher in control while letting AI handle the tedious parts.
Start with a blank slate and let codes emerge (inductive), or bring an existing codebook and let MindCoder apply it (deductive). Switch between modes at any point.
Every AI-generated code comes with the source text it was derived from. Accept, edit, split, merge, or reject — every decision is logged and traceable.
Do a quick first pass to get a rough sense, then return for deeper analysis. MindCoder supports the natural, non-linear way researchers actually work with qualitative data.
No account needed — start with a demo project.
Launch MindCoder 2.1 →