Your agents can collaborate with AI
in every conversation
to drive the customer interaction forward
in the most effective and efficient resolution
...with adaptive workflows enhanced by dialogue suggestions,
behavioral cues, and knowledge surfacing
How Minerva CQ works
We analyzed hundreds of millions of customer-agent interactions and designed an experience around the conversation.
We then created a state of the art AI that tag teams with agents in real-time.
Before the conversation
Insights and omnichannel data are pulled, and parsed in real-time from multiple data sources.
Context is surfaced to the agent so the interaction is personalized and faster.
During the conversation
The optimal workflow is surfaced to resolve the customer's issue. As the conversation progresses the workflow adapts in real-time to what's being said.
The interaction is further enhanced with real-time dialogue suggestions, behavioral cues, and knowledge surfacing.
After the conversation
Auto summarization, passive NPS and CSAT, and key actions are provided to agent and supervisor and injected into enterprise systems and machine learning.
Training opportunities are identified.
In the contact center
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Improve the customer experience
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Elevate the agent experience
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Reduce handle times and costs
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Increase revenue via upsell/cross-sell
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Decrease agent onboarding timeframes
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Optimize agent knowledge and training
...make every agent a
customer service hero
Empower every service technician with knowledge
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Improve real-time knowledge access and guidance
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Elevate the employee experience
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Reduce resolution time
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Decrease employee onboarding timeframes
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Optimize enterprise knowledge and training
...in the field
