Behind every customer comment is a way of thinking. Standard analysis reads the surface — themes, sentiment, counts. LinguaFrame Analytics™ reads what's underneath: the motivational, cognitive and emotional signals that predict what someone will do next.
Every piece of customer text contains three distinct layers of information. Most tools read one. Better tools read two. LinguaFrame Analytics™ reads all three — and it's the third layer that changes what you'd do next.
Before any skilled communicator writes a single word, they need four things answered. LinguaFrame Analytics™ extracts all four from your verbatim data — at any scale.
Five real restaurant reviews. All rated 3 stars. Traditional analysis calls them all "mixed sentiment." Click each review to see what LinguaFrame Analytics™ reads underneath — and what that means for how you'd respond.
Once LinguaFrame Analytics™ scores a corpus of verbatims, the analytical work moves through two stages. First, map the terrain — discover which topics cluster, how intensely they're felt, and where they sit relative to each other. Then build the structural model — trace the causal pathways from topics through thinking styles to business outcomes.
Each dot is one verbatim, positioned in signal space — not by what words were used, but by the underlying thinking profile. Verbatims cluster naturally by motivational orientation and cognitive state. Hover any cluster to see the thinking profile it represents.
Once topics have sentiment intensity scores, LinguaFrame Analytics™ builds partial dependence curves — showing exactly where the intensity of a topic feeling tips into NPS impact. The non-linear thresholds that flat averages completely miss.
Behavioural involvement — visit frequency, purchase rate, activity level — tells you what people do. The Signal Depth Score tells you how cognitively and emotionally invested they are in what they do. These are completely different signals. And the second one predicts the future better.
Membership organisations communicate to everyone as though they're the same person. But their members have completely different thinking styles. The same renewal campaign — the same words — lands completely differently depending on how each member processes information. LinguaFrame Analytics™ tells you which is which.
Recent advances in AI reveal something remarkable: when a language model reads a sentence, it creates a mathematical fingerprint — 768 numbers — that encodes not just what was said, but how the mind was working when it said it. The motivational orientations and thinking styles that determine how someone will behave leave distinct geometric traces in that fingerprint space.
A 2024 study demonstrated that LLMs encode meaningful differences in how people think — not just what they say — with statistically significant effects on task performance depending on the cognitive mode active in the text. The fingerprint reflects the thinking style, not just the words.
A 2025 study confirmed that the way people use language reliably predicts their underlying motivational orientation — even when they have no intention of disclosing it. The signal is in the structure of the language, not in what is explicitly said.
We are testing whether the LinguaFrame™ signal dimensions are geometrically separable in the fingerprint space of customer verbatims — meaning they exist as real structure in the data, not as categories imposed from outside. If confirmed, the dimensions LinguaFrame™ reads aren't a framework we applied. They're a map the language drew.
LinguaFrame Analytics™ is in active development. If you work with open-ended survey data and want to see what the thinking layer reveals — let's talk.