Tour: Dashbot for Customer Service

Optimize Response Effectiveness

With Dashbot’s Phrase Clustering, you can identify where the chatbot may be breaking down through mishandled or unhandled Intents.

The information can be used to train your NLP engine to improve response effectiveness and user satisfaction.

Identify mishandled and unhandled Intents

Reduce failure rate

Reduce escalations / Increase containment

With Dashbot’s Escalation Report, you can quickly monitor escalations, or containment, to see the percentages overtime, as well as the common paths that lead to escalations, and the transcripts in which they occur.

Dive deeper into the flows that lead to escalations, to get a better understanding of user behavior, where the chatbot may be breaking down, and improve the overall user experience.

Get real-time notifications to insert live agents

Get real-time alerts of sessions going awry, and insert a live agent to help lead users to successful outcomes.

Dashbot APIs integrate with 3rd party live agent, chat platforms, including Genesys.


View users previous conversations or dive deeper into a conversation to better understand the context.

In addition to top messages, utterances, and intents, we reconstruct all the session transcripts – including live sessions which can be viewed in real time!

We support the full richness and multi-modal nature of platforms. Messages in and out can be rich media: images, animated gifs, audio, video, GPS coordinates, cards, lists, and more.

Dashbot also shows the underlying NLP Intents and Entities.

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Transcript Search

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Anomalous Sessions

Dashbot can detect anomalous sessions – outliers from typical conversions that may warrant further review.

Quickly identify outliers and dive deeper into the conversations to see where users may be having difficulty.

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Build a chatbot from live transcripts

Are you looking to build a chatbot? Do you have historical live agent transcripts?

Dashbot’s Phrase Clustering can process live agent transcripts to identify common user messages and Intents as well as agent responses to kick start chatbot development. The data can be used to train your NLP engine.

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