Classification at volume
Tag and route tens of thousands of tickets or messages consistently, with a taxonomy built around your domain.
← NLP & text analytics portfolio
·NLP & text analytics · reference example
Ticket tagging and sentiment at scale — like SentiSum
SentiSum is support-ticket tagging and sentiment analytics across channels. It's a recognisable benchmark for a system class I build — here's what it does, and how I'd build yours.
Reference example — not my client. SentiSum is an independent product I'm not affiliated with, shown as a benchmark for this kind of build.
Live preview of www.sentisum.com — their site, shown as reference.
01What a SentiSum-class system does
The shape of the build
At its core, SentiSum is support-ticket tagging and sentiment analytics across channels — the kind of system you reach for when you have classification at volume and CX analytics. Here is how I'd build one for you.
Voice-of-customer analysis
Cluster free-text feedback into themes you can act on, and track how they move over time.
Sentiment & intent
Reliable sentiment and intent signals wired into the product, so the right message reaches the right queue.
Signal from speech
Extract structured signal from audio and transcripts — the analytics-heavy end of NLP where accuracy matters.
02More NLP & text analytics examples
Other benchmarks in this class
Same system class, different products. Each opens a page like this one.
Idiomatic
customer-feedback categorisation and voice-of-customer analysiswww.idiomatic.com
Relative Insight
comparative language analysis for brand and audience researchwww.relativeinsight.com
Kintsugi
voice-biomarker analysis that detects signs of depression and anxiety from short speechwww.kintsugi.ai
Want something like SentiSum?
Tell me what it needs to do and where it's getting stuck. I'll tell you honestly whether I'm the right person and what it would take — no pitch deck, no discovery call to book a discovery call.