We analyzed 427,536 real interactions before building custovox.ai

custovox.ai is not built on assumptions. Before analyzing your customer data, we spent years analyzing real corpora — customer reviews, medical consultations, corporate communications — to validate the models that power the platform.

427,536interactions analyzed in foundational research
4independent research studies across 4 domains
5NLP methods validated on real-world data

Consumer electronics reviews — how people really experience technology

Corpus49,985 customer reviews · Semantic clustering (SBERT + k-means)

41.4% of customer verbatims are experiential — not functional. Customers don't describe features. They describe their life with the product. Only 36.6% focus on technical performance.

Applied in TextIQ — emotion and theme detection
Share of the 3 verbatim dimensions in 49,985 customer reviewsExperiential 41.4% (life with the product), instrumental 36.6% (features and performance), situational 22.0% (context, support, delivery). Experiential 41.4% Life with the product Instrumental 36.6% Features & performance Situational 22.0% Context, support, delivery Source: SBERT + k-means analysis of 49,985 Amazon customer reviews

Medical consultations — what predicts relational quality in text

Corpus377,273 text consultations · DistilRoBERTa + BART-MNLI

Affective dispersion (the co-activation of multiple emotions in a single message) predicts relational response quality better than a single dominant emotion. β = 0.163 vs β = 0.083. This principle structures PredictIQ's multi-signal churn scoring.

Applied in PredictIQ — multi-signal churn scoring

Voice assistant reviews — 4 dimensions of linguistic understanding

Corpus278 verified reviews · LDA topic modelling

Customer perception operates across 4 linguistic dimensions: lexical-semantic, contextual, situational and pragmatic. A 4.1/5 average score can hide deep frustration at the pragmatic level — commercial intrusion, broken implicit promises.

Applied in TextIQ — 4-level linguistic analysis
The 4 linguistic dimensions of a customer messageA customer message is read on 4 levels: lexical-semantic (words and meaning), contextual (history and entities), situational (space, time, social) and pragmatic (implicit intentions). Lexical-semantic Words & meaning Contextual History & entities Situational Space, time, social Pragmatic Implicit intentions Customer message Source: LDA analysis of 278 voice assistant reviews

Corporate speeches — how technology language evolves over time

Corpus13 conferences · 28h 49min of transcribed content · Word2Vec + SBERT + UMAP + HDBSCAN

The word 'intelligence' grew +480% between 2016 and 2025 in technology corporate discourse. 'Device' fell -65%. Companies no longer describe technology — they speak through it. Metaphors shifted from competition to living organisms and collaboration.

Applied in VoiceIQ — longitudinal trend detection
'intelligence' vs 'device' in technology corporate discourse, 2013–2025Normalized frequency (0–200). 'intelligence' rises from 20 in 2013 to 130 in 2023 and 116 in 2025; 'device' falls from 100 to 35. 0 50 100 150 200 2013 2015 2017 2019 2021 2023 2025 +480% since 2016 -65% since 2016 intelligence device Source: analysis of 13 technology conferences (2013–2025) · Word2Vec + SBERT

What this research means for your customer data

Most sentiment analysis tools are trained on generic corpora. custovox.ai models are grounded in peer-reviewed research on real customer interactions — not benchmarks, not synthetic data.

The result: analyses that capture what customers actually mean, not just what they literally say.

See it on your data →See all modules →