Plenty of reports, no conclusions
Dashboards, word clouds, sentiment curves—everything is there. What to change and what to do still has to be analyzed from scratch.
DeepVOC collects authentic voice of the customer from public channels, private domains, and inside the enterprise. AI agents drive richer tagging, multidimensional insight, traceability, and action. We do not deliver reports. We deliver results.
Dashboards, word clouds, sentiment curves—everything is there. What to change and what to do still has to be analyzed from scratch.
It cannot even catch irony like “waited half an hour—so efficient.” Wrong tags feed wrong conclusions.
A number sits there, but you do not know which comment it came from or how it was calculated. You cannot trust it, let alone act on it.
Complaints and negative reviews are visible, but nobody is assigned, nobody follows up. The voice dies in a spreadsheet.
The same body of user voice, viewed from product, user, operations, and innovation—each with strengths, weaknesses, and recommended actions.
Five core Agents, each with a job, taking customer voice from insight all the way to action.
Large-scale, multi-level auto-tagging; the taxonomy evolves dynamically
Direct conclusions from product, user, operations, and innovation views
Conversational follow-up; every conclusion traces to real quotes
Auto-generate tickets by rule and drive business action
Combine the four insights into a report in one click
Deduplicated, commercial posts removed, low quality filtered. When the source is real, tags are accurate, and content is traceable, VOC conclusions can be trusted and used to decide.
Three kinds of authentic customer voice—external, internal, and research—enter analysis only after deduplication, commercial-post removal, and quality filtering.
Every conclusion and every number links back to the original corpus segment. You can see it and check it.
AI analyzes and interprets; it does not invent. For the first time, VOC conclusions can actually be used to decide.
We used to hunt for issues across tens of thousands of comments. Now the system tells us where the problem is and automatically links it to the specific store. This system has shortened the cycle from finding a problem to fixing it.
Yujian Xiaomian · Head of IT, Ma Songyan
A leading dairy brand used multi-algorithm, multi-model tagging to raise tagging accuracy from about 70% to 95%, supporting a million-scale corpus and a four-level taxonomy, and completing a full VOC system switchover.
A leading dairy brand · VOC project