LLM-powered voice assistant for call-center.
What does a company do?
The company develops and implements AI-powered voice assistants that automate tasks such as call verification and routing for inbound customer calls. Their solutions integrate with existing telecommunication systems to make customer service operations more efficient. By automating these processes, the company helps businesses handle calls faster, reduce errors, and lower operational costs while improving the overall customer experience.
The company’s goal is to improve how businesses handle customer interactions, making communication smoother, reducing costs, and ensuring better service for customers.
How does Vstorm cooperate with Call-center?
At Vstorm, we partner with our clients to solve complex challenges through innovative AI and LLM-based technologies. Our collaboration with the company showcases our ability to deliver tailored solutions that help scale the business by addressing challenges related to operational productivity, ensuring long-term impact.
The client approached us with a primary goal: to automate the verification and routing of inbound customer calls. Their existing system required significant manual intervention, which was both time-consuming and error-prone, especially during peak times. Additionally, the client faced difficulties scaling their process to accommodate a growing global customer base, with the need for support across multiple languages.
We began our collaboration by conducting a thorough audit of their existing solution. Through this process, we identified several key challenges, including:
- The manual handling of calls was inefficient and led to delays in response times.
- There was an increased error rate due to the manual verification and routing processes.
- The system struggled to scale to meet the demands of a global, multilingual customer base.
To address these issues, we proposed a comprehensive AI and LLM-based solution. The core of our approach involved integrating advanced AI technologies to automate the call-handling process, with minimal human intervention (their own voice assistant). The key technologies used in this solution included:
- Question Answering. Enabled the AI to understand and respond accurately to customer queries in real time, reducing the need for human intervention.
- Retrieval-Augmented Generation (RAG). Combined retrieval and generative models to provide contextually accurate responses to complex queries.
- Text Summarization. Condensed long inputs into concise summaries, speeding up decision-making and improving response times.
- Information Extraction. Extracted relevant data from unstructured inputs like emails and call logs, ensuring accurate and informed responses.
- Text Classification. Automatically categorized queries for efficient routing and processing based on the type of request.
- Speech-to-Text and Text-to-Speech (STT/TTS). Converted spoken language into text and vice versa, enabling smooth, natural voice interactions.
- LangChain Framework. To integrate LLM to improve the accuracy and adaptability of responses, even in complex scenarios.
Now their process looks the way:
When the call center receives an inbound call, the LLM-powered voice assistant verifies the caller’s identity and the purpose of the call. The system then processes the caller’s query in real time, using advanced language models to understand their request.
After identifying the type of query, the LLM automatically routes the call to the appropriate department or escalates it if needed. The solution supports multiple languages, ensuring global customer calls are handled smoothly.
Throughout the interaction, the LLM uses speech recognition and voice response to communicate with customers naturally, reducing the need for human agents. The system operates 24/7, allowing the call center to handle inquiries at any time of day, providing uninterrupted service. This process is designed to be fast, efficient, and scalable, enabling the call center to manage more calls with less downtime.
Result
The implementation of LLM-based voice assistant led to hyper-automation of the call-handling process, allowing the call center to manage a significantly higher volume of calls with greater accuracy and speed. This automation reduced the need for manual intervention, ensuring faster response times and minimizing errors.
In addition, the solution enabled hyper-personalization in customer interactions by tailoring responses to the specific needs of each caller. The enhanced decision-making processes driven by LLMs provided valuable insights that boosted both operational efficiency and customer satisfaction.
As a result, the call center can now focus on strategic growth initiatives and customer engagement, as the automated processes have freed up resources previously tied to manual call handling.
This project aligns perfectly with Vstorm ‘s mission: to help companies ethically implement AI, enabling people to focus on what truly matters.
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