AI Text Analytics
Hundreds of open answers.
Coded the way you'd code them.
AI does the reading. You keep the categories - and full control over what the answers mean, from the first theme to the last classification.
Demo video coming soon
Two-step, human-in-the-loop
A coding framework you approve, then AI applies it.
Screenshot: Theme approval screen
You approve the framework
AI tests candidate themes against several fresh batches of responses - not just a first guess - before proposing the ones that actually hold up. Delete, rename, or redefine anything until it matches how you'd describe your own data - nothing gets classified until you're happy with it.
Screenshot: Classification with confidence filter
Then it codes everything
AI sorts every response into your approved themes - no more scrolling through hundreds of answers by hand. A 0-100 confidence score on every classification lets you filter for the ones it's least sure about, and you can always recode any response yourself.
Keyword and sentiment tools hand you a black box and ask you to trust it.
Here, you decide what the answers mean - which is exactly what makes the results easy to stand behind in front of a client.
Auditable, not just automated.
Screenshot: Editing a proposed category
You own the taxonomy
Edit or delete any AI-proposed category before a single response is classified against it.
Screenshot: Filtering by confidence score
Confidence, not a guess
Every classification carries a 0-100 confidence score - filter by range to isolate exactly the responses worth a second look.
Screenshot: Recoding a response by hand
Recode anything, anytime
Manually move any individual response to a different category - the AI classification is a starting point, not the final word.
Grounded in an established analysis framework, not a prompt hack.
The workflow follows Braun & Clarke's widely-used thematic analysis framework, with one hard gate built in: bulk classification cannot start until a human has reviewed and approved the category structure. In testing, that classification step reaches 85-90% agreement with expert human coders - in the same range as agreement between two trained human coders.
The remaining gap is exactly why manual review stays part of the workflow: sarcasm, cultural context, and ambiguous references are things AI still can't reliably read on its own.
Read how the methodology worksExplore the rest of the platform
Stop coding verbatims by hand.
Let AI do the first pass - you stay in control of what it means.
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