Topic discovery, automated
BERTopic finds 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
Customer feedback,
finally understood.
topicara reads every survey, ticket, and review — and tells you which topics are growing, which customers are about to leave, and what to do about it.
No credit card required · Set up in minutes · Cancel anytime
Trusted by feedback teams at
ACMEQUANTUMNORDWESTAURORABASIS
Without topicara
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 topicara
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 scoring per item, then aggregated into a severity index per topic so the loud-but-small problems don’t drown out the quiet-but-systemic ones.
Churn early-warning
Unsupervised early-warning signals fire ~30 days before NPS drops. No labels needed. The system learns which language patterns precede cancellation in your specific corpus.
Action layer
Assign reviews, set deadlines, hand off between teams, and close the loop. Every alert points to a specific item with full context, not a number on a dashboard.
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
analyzed in under 2 minutes
73%
sentiment accuracy
on irony-heavy text
30 days
median lead time
between churn signal and cancellation
See your own feedback in topicara 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.
English, German, French, and Italian out of the box. The embedding model is multilingual; sentiment uses cardiffnlp/twitter-roberta for English and oliverguhr/german-sentiment-bert for German, with XLM-RoBERTa for FR and IT.