Elevate the Agent Experience
AI automation for agents exponentially improves the experience and increases the productivity of your most important contact center asset, your agents, setting them up for success so they can deliver the best possible customer service experience.
Real-Time Assistance
With Live Conversational Tasks and Engagement
AgentAssist is your agent’s personal intelligent virtual assistant, freeing them to focus on delivering timely, personalized service and leaving transaction execution, information retrieval and other repetitive or time-consuming tasks to automation.
AgentAssist uses AI to monitor the customer-agent conversation, to identify intents and sentiment– such as interest in solving a specific problem; buy and upsell opportunities and customer frustration–and to perform tasks and provide guidance to improve engagement. Agents also collaborate one on one with AgentAssist, asking questions or initiating tasks for AgentAssist to perform in parallel during live conversations with customers.
SMART ASSISTANCE
Empower Agents
with the most relevant information
At the start of any conversation on any channel, AgentAssist can look up customer information from CRM and other third-party systems and proactively provide agents with the right context based on customer goals. User’s session history, previous chat history and current sentiment are all part of the context.
OPPORTUNISTIC ASSISTANCE
Monitor Conversations
side by side with live agents and initiate automations
AgentAssist monitors the conversation; detects intent; and advises agents and performs tasks throughout the engagement, auto collecting responses as the conversation progresses.
FULFILLMENT OF ACTIONS
Perform Tasks
based on intent detected or agent request
AgentAssist automates tasks and fulfillment for agents during the conversation. There are unlimited possibilities for automating tasks that AgentAssist will execute for agents.
Examples of tasks AgentAssist performs for agent include common transactions like balance transfer; book appointments; schedule field service; change travel; update policy; gather customer information and find and retrieve information from multiple systems. These and more can all be automated and performed by AgentAssist during live conversations, saving agents value time.
Agents also engage directly with AgentAssist and ask questions or initiate tasks or transactions for AgentAssist to execute that AgentAssist does not suggest.
SUGGESTED RESPONSES
Suggest Responses
based on intent detected
AgentAssist can suggest one or more preset responses for any intent detected. Responses can include customer name, time of day or other data pulled from third-party sources like CRM systems.
NEXT BEST ACTIONS
Proactively Direct
the conversation forward
AgentAssist can provide next best action prompts to agents based on the intent detected or the stage in a conversation. The actions can also include buttons to trigger API calls, launch applications or trigger a robotic process automation (RPA) process.
SENTIMENT ANALYSIS
Personalize Conversations
delivered at the right time in the right way
AgentAssist informs and empowers agents throughout conversations performing sentiment analysis and offering timely empathetic response suggestions for better customer and agent experiences.
AUTOMATE POST-CALL WRAP UP
Summarize Conversations
to speed up post-call wrap up
Conversations are automatically summarized for agents for review and editing, saving valuable time in post-call processing, aiding in compliance and increasing agent and overall contact center efficiency.
INTEGRATION
Enterprise CRM and Back-End System Integration
delivers access to relevant information
AgentAssist is easily integrated with an enterprise CRM and other backend systems, seamless surface information, launch applications, run RPAs, and provide automated conversational suggestions to agents based on customer conversation history and intent.
SELF LEARNING
Virtual Agent Assistants Get Smarter Over Time
AgentAssist becomes a super virtual agent, learning from each conversation via machine-learning (ML) algorithms and continuously improving the agent-customer experience in handling service requests.