Topic discovery, automated
BERTopic surfaces themes you didn’t know to look for. No tagging rules, no taxonomies to maintain — the model retrains as your conversations evolve.
Voice-of-customer platform · Built in Switzerland
Customer feedback,
finally understood.
Surveys, support tickets, app reviews, emails — clustoria reads them all and tells you which topics are growing, which customers are about to leave, and exactly who on your team should act.
No credit card · 5-minute setup · Available in 5 languages
Trusted by feedback teams at
ACMEQUANTUMNORDWESTAURORABASIS
Without clustoria
What teams do today
- Tag feedback manually in a spreadsheet
- Wait for monthly NPS reports that lag reality
- Build dashboards no one ever opens
With clustoria
What teams do with us
- Topics surface themselves from raw text
- Churn signals fire ~30 days before NPS drops
- Alerts route to the right person automatically
Capabilities
Everything you need to act on what customers are telling you.
Sentiment and severity
Multi-lingual, irony-aware sentiment per item — then aggregated into a severity index per topic so loud-but-small problems don’t drown out quiet-but-systemic ones.
Churn early-warning
Unsupervised signals fire ~30 days before NPS drops. No labels needed. The system learns the language patterns that precede cancellation in your specific corpus.
Chat with your data
Ask clustoria anything about your customers in plain language. The agent runs read-only queries, answers in your language, and links every claim to the original feedback.
Auto-routing rules
IF/THEN automation built for non-technical users. Route negative + churn-risk items to your CSM; tag praise for marketing; bump priority on severity ≥ 0.7. Backtest before you ship.
Action layer + audit trail
Assign reviews, set deadlines, hand off between teams. Every alert points to a specific item with full context, and every routing decision shows why it fired.
How it works
Five stages from raw text to the right inbox.
01
Ingest
Surveys, tickets, reviews — every channel.
02
Embed
MiniLM turns each item into a 384-dim vector.
03
Cluster
BERTopic + HDBSCAN find the natural groupings.
04
Score
Sentiment, severity, and churn risk per topic.
05
Alert
The right person hears the right signal in time.
01 · Ingest
Surveys, tickets, reviews — every channel.
02 · Embed
MiniLM turns each item into a 384-dim vector.
03 · Cluster
BERTopic + HDBSCAN find the natural groupings.
04 · Score
Sentiment, severity, and churn risk per topic.
05 · Alert
The right person hears the right signal in time.
Use cases
Built for teams drowning in unstructured customer feedback.
Telecoms / ISPs
Network-quality complaints peak weeks before churn. Spreadsheet tagging never keeps up with regional outages or pricing-change blowback.
Best fit: support orgs with >5k feedback items/month across web, app, and call centre.
SaaS customer success
Tickets, in-app messages, and NPS comments live in separate tools. The CSM team can name 3 themes but suspect there are 30 — they just can’t prove it.
Best fit: CS teams managing >200 accounts who want topic-level health scores per customer cohort.
Retail & e-commerce
Reviews, returns reasons, and Trustpilot rants tell the same story in three languages. The merchandising team only sees the star rating.
Best fit: brands with multilingual review streams who need topic + sentiment per SKU/category.
10,000
feedbacks
analysed in under 2 minutes
5
languages
full UI + AI replies in EN · DE · FR · IT · ES
30 days
median lead time
between churn signal and cancellation
See your own feedback in clustoria today.
Connect a CSV or wire up a channel and the first topics surface in under five minutes.
Frequently asked
The questions buyers ask first.
The product UI ships in English, German, French, Italian, and Spanish — each user picks their own. The chat agent and action briefs answer in your chosen language too. Sentiment models are multilingual: cardiffnlp/twitter-roberta for English, oliverguhr/german-sentiment-bert for German, and XLM-RoBERTa covers FR / IT / ES.