How real-time agent assist surfaces knowledge base articles during calls

Real-time agent assist technology is revolutionizing the way customer service teams operate, particularly in how they access and utilize knowledge base articles during live calls. This technology not only enhances agent performance but also significantly improves customer satisfaction by providing timely, relevant information. In this post, we will explore how real-time agent assist surfaces knowledge base articles during calls, the technology behind it, and its practical implications for your organization.

Understanding Agent Assist Technology

Core Definition:
Agent assist technology leverages real-time artificial intelligence to monitor customer interactions, comprehend context and intent, and deliver pertinent information, guidance, and recommendations to agents during conversations. This capability is crucial for improving call outcomes and enhancing overall customer experience.

What It's NOT:

  • Not just a searchable knowledge base
  • Not static scripts or call flows
  • Not post-call quality scoring
  • Not a chatbot or IVR system

The power of agent assist lies in its ability to provide contextualized support, enabling agents to access the right knowledge base articles at the right moment. This capability is essential for resolving customer inquiries efficiently and effectively.

The Technology Stack

Agent assist technology operates through a sophisticated stack of components that work together to deliver real-time support. Here’s a breakdown of the layers involved:

Layer 1: Conversation Intelligence
This layer utilizes speech-to-text and text analysis to capture and understand conversations. Key features include:

  • Transcription accuracy: 95%+ for enterprise-grade performance
  • Sub-second latency: Critical for real-time support
  • Intent and entity recognition: Helps identify what the customer is asking for

Layer 2: Context Engine
This layer interprets the meaning of conversations, customer sentiment, and call purpose. It includes:

  • Customer intent analysis: Determines what the customer wants
  • Emotional sentiment detection: Understands the customer's mood
  • CRM and history integration: Provides agents with background information

Layer 3: Intelligence & Decision Engine
This AI-driven layer decides what guidance to provide based on the context. Examples include:

  • If the customer is frustrated → de-escalation prompts
  • If there’s a compliance moment → required disclosures
  • If there’s a knowledge gap → relevant article suggestions

Layer 4: Presentation & Delivery
This layer focuses on how information is presented to the agent without disrupting their workflow. Features include:

  • Knowledge article cards
  • Script suggestions
  • Real-time alerts
  • Next best action recommendations

Layer 5: Integration Framework
This layer connects the agent assist solution to contact center platforms, CRM systems, and knowledge bases, ensuring seamless operation.

Core Platform Capabilities

To maximize the effectiveness of real-time agent assist, certain capabilities are essential:

  1. Real-Time Processing

    • Sub-2-second latency from speech to guidance
    • Continuous analysis throughout the interaction
    • Ensures agents receive guidance when needed, not after a delay

  2. Context-Aware Knowledge Surfacing

    • Automatically displays relevant information based on the ongoing conversation
    • Eliminates the need for agents to search manually, reducing handle time and improving resolution rates

  3. Sentiment Detection & Escalation Prevention

    • Recognizes shifts in customer emotion and prompts agents with de-escalation tactics
    • Helps prevent escalations before they occur

  4. Compliance Monitoring

    • Ensures adherence to regulatory and policy standards
    • Prompts agents with required disclosures and flags prohibited language

  5. Multichannel Support

    • Operates across various channels, including voice, chat, email, and social media
    • Provides consistent support to agents, regardless of the communication method

  6. CRM & System Integration

    • Seamlessly connects with existing technology stacks to ensure smooth adoption
    • Facilitates data sharing and improves efficiency

  7. Supervisor Analytics

    • Enables real-time monitoring and intervention capabilities
    • Provides performance insights that amplify supervisor capacity and enable data-driven coaching

Business Impact & Metrics

The implementation of real-time agent assist technology can lead to significant improvements across various metrics:

  • Average Handle Time (AHT): Reduction of 10-25% due to faster information access and fewer transfers.
  • Transfer/Escalation Rate: Reduction of 20-40% as agents are equipped to resolve issues on the first call.
  • After-Call Work (ACW): Reduction of 15-30% through auto-documentation and quicker case completion.
  • First Call Resolution (FCR): Improvement of 10-20 percentage points, leading to fewer callbacks.
  • Customer Satisfaction (CSAT): Increase of 8-15% as a result of quicker, more knowledgeable responses.

These metrics demonstrate the tangible benefits of integrating real-time agent assist technology, showcasing its value not only in operational efficiency but also in enhancing the overall customer experience.

Implementation Considerations

Implementing a real-time agent assist solution requires careful planning and execution. Here are key considerations for a successful deployment:

Preparation:

  • Define clear business objectives, such as improving AHT, FCR, or CSAT.
  • Assess the existing environment, including call volume, agent count, and technology stack.

Execution:

  • Choose the right agent assist platform that fits your needs, focusing on features like real-time processing and context-aware knowledge surfacing.
  • Conduct a pilot program with a small group of agents to test the solution and gather feedback.

Evaluation:

  • Monitor key performance metrics during the pilot phase to measure effectiveness.
  • Gather agent feedback to identify areas for improvement.

Iteration & Improvement:

  • Use insights from the pilot to refine the implementation before a full rollout.
  • Continuously optimize the system based on ongoing performance data and agent input.

By following these steps, organizations can effectively leverage real-time agent assist technology to enhance their customer service operations and drive better outcomes for both agents and customers.