DeepZero · AI VOC Insight & Decision System

Turn every authentic user voice
into an actionable business move

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.

DeepVOC product Demo

The VOC tool you bought only bought you hundreds of reports

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.

If tagging is wrong, insight is wrong

It cannot even catch irony like “waited half an hour—so efficient.” Wrong tags feed wrong conclusions.

You see a conclusion, but cannot find the root

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.

Problems stop at the report

Complaints and negative reviews are visible, but nobody is assigned, nobody follows up. The voice dies in a spreadsheet.

DeepVOC: from “seeing the voice” to “putting the voice to work”

More than seeing user voice clearly—every voice actually drives decisions and action in product, user, operations, and innovation.

Real data + AI tagging:
a reliable data foundation

  • Gather authentic voice from social media, e-commerce, customer service, research, and more
  • Fixed taxonomy plus AI discovery of new tags—the system keeps evolving
  • Multi-algorithm, multi-model validation; tagging accuracy above 90%
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Real multi-source data and AI tagging

Multidimensional intelligent insight, with decision recommendations you can act on

The same body of user voice, viewed from product, user, operations, and innovation—each with strengths, weaknesses, and recommended actions.

See what the product does well, where it falls short, and how to fix it

The same corpus automatically yields product strengths, weaknesses, and improvement recommendations—ready to use.

Product insight

AI assistant:
dig deeper, trace the source, know the why

  • Follow up in natural language, like a conversation
  • Every number traces back to real user quotes
  • Not a black box that spits conclusions—voice you can verify and dare to use for decisions
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Copilot assistant: conversational follow-up and source tracing

Intelligent tickets:
auto-dispatch, then act

  • When negative volume hits a threshold, preset rules fire automatically
  • Tickets are generated and assigned to the right store / department
  • Tickets link to the real data source; handling is fully traceable
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Intelligent tickets generated automatically

Agentic VOC, driven by AI agents

Five core Agents, each with a job, taking customer voice from insight all the way to action.

Smart Tagging Agent

Large-scale, multi-level auto-tagging; the taxonomy evolves dynamically

Insight Agent

Direct conclusions from product, user, operations, and innovation views

Copilot Discovery Agent

Conversational follow-up; every conclusion traces to real quotes

Ticket Agent

Auto-generate tickets by rule and drive business action

Report Agent

Combine the four insights into a report in one click

Every insight comes from real user quotes

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.

Authentic

Three kinds of authentic customer voice—external, internal, and research—enter analysis only after deduplication, commercial-post removal, and quality filtering.

Traceable

Every conclusion and every number links back to the original corpus segment. You can see it and check it.

Decision-ready

AI analyzes and interprets; it does not invent. For the first time, VOC conclusions can actually be used to decide.

Make every comment count

Case 1
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

Case 2
70% → 95%

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

Start by hearing what your users are actually saying