Vivollo
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Conversation Intelligence

Know why customers reach out.

Every conversation is auto-classified, scored for sentiment and rolled into trends — and Vivollo flags the questions your knowledge base can't answer yet, turning raw chats into a to-do list.

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OverviewAug 15 – Sep 14

  • Total conversations

    153

    In selected range

  • Classified

    %90

    138 of 153 conversations

  • Average fill rate

    %74

    Across active dimensions

  • Active insights

    9

    Open findings

12 knowledge suggestions waitingTopics the assistant couldn't answer, each with a draft to review.Review

Conversation volume

Daily volume across the selected range

Recent insights

Latest auto-detected findings

  • LanguageGerman-language volume is rising against a flat baseabout 24 hours ago
  • Query typeReturns cluster hard on one product1 day ago
  • SentimentFrustration rises sharply on the second contact1 day ago

Every conversation is a data point nobody has to tag. Vivollo labels topic, intent and sentiment itself, surfaces what changed as a finding, and turns what it could not answer into the article to write next.

01Signals

It tells you what changed before the tickets do.

Each conversation is tagged by topic, intent, sentiment and your own dimensions. Counts become trends; trends that move become findings — a German-language spike, a returns cluster on one product.

The Findings list: a German-language rise, a returns cluster, a sentiment shift

Findings, detected on their own

Trends, shifts and correlations across your conversations, with the change and the period.

  • Neutral45%
  • Positive29%
  • Mildly frustrated14%
  • Frustrated7%
  • Angry5%

Break a segment down

Any dimension, as a bar chart or a pie.

The Taxonomy list of classification dimensions

Your own dimensions

Sentiment, query type, topic, churn risk — built in or yours.

The Explorer with a matching-conversations count and breakdown

Down to the conversations

Filter, then read the threads behind a number.

first response
11 s
resolution
2m 40s
handed off
6%

Speed, measured

Response and resolution times per segment.

02Gaps

The questions it could not answer become the docs to write.

Unanswered questions cluster into topics with evidence. Each gets a draft; you review, publish, and watch next week's number move.

  • Cluster

    Recurring unanswered questions, grouped by topic and ranked by how many asked.

  • Draft

    A generated article from the samples, for you to edit.

  • Publish

    One click into the knowledge base; the assistant answers it from then on.

The Suggestions list: topics, conversation counts, evidence and a Publish button per row

Proof of control

From a gap to a published answer.

Forty people asked about gift wrapping and nobody could answer. The topic was clustered, a draft was written from three samples, an editor approved it, and the article went live — the next question was answered from it.

  1. 1cluster_unanswered1.2 sInputlast 30 days'gift wrapping' · 40 chats
  2. 2generate_draft4.8 sInput3 samplesdraft · 210 words
  3. 3review—Inputeditorwaiting for approval · 1 edit
  4. 4publish_article60 msInputPayment & orderspublished
  5. 5rag_search280 msInput'gift wrap?' · next chatanswered from the new article
Example conversation; latencies are illustrative.

Connected channels and stores

All integrations
  • WhatsApp
  • Instagram
  • Messenger
  • Web widget
  • ConnexeaseConnexease
  • GrispiGrispi

Works well alongside

Knowledge EngineSourced answers from your real content.Agentic AIActs on real data — tools, memory, resolution.Unified InboxEvery channel in one inbox — context follows the customer.

Ready to meet your AI agent?

Book a demo and we'll build a working agent on your real data — across WhatsApp, Instagram and your website. Live in days.

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