RightTouch Announces AI Contact Center Concept Centered on AI Operators
RightTouch has unveiled its "AI Contact Center" concept, with AI operators at its core, and launched "QANT Knowledge Hub," a new product for its knowledge integration platform. This aims to resolve labor shortages and customer service challenges, striving for a self-evolving model where accuracy improves with use.
📋 Article Processing Timeline
- 📰 Published: April 28, 2026 at 20:00
- 🔍 Collected: April 28, 2026 at 11:31
- 🤖 AI Analyzed: April 28, 2026 at 13:02 (1h 30m after Collected)
RightTouch Co., Ltd. (Headquarters: Shinagawa-ku, Tokyo; Representative Directors: Shuhei Nomura / Hiroto Nagasaki; hereinafter "RightTouch"), a provider of AI contact center platforms for enterprises, announces its "AI Contact Center" concept centered on AI operators. Concurrently, it will begin offering "QANT Knowledge Hub," a new product for its knowledge integration platform, which serves as the brain for this concept.
RightTouch launches "QANT Knowledge Hub (β)," an AI-ready knowledge integration platform to realize AI contact centers. Product release details here: https://righttouch.co.jp/news/4N5BcObP
The goal of this concept is not merely to "automate inquiries," but to "create a mechanism where the accuracy of inquiry resolution continuously improves with operation."
By redesigning the entire customer contact point based on AI, the system will comprehensively utilize response data and knowledge data accumulated in contact centers. This allows for organic collaboration between AI-powered automated responses (AI operators) and human support, continuously improving and advancing the customer experience (CX).
■ Background: Structural Challenges Facing Contact Centers
In recent years, the environment surrounding contact centers has changed significantly. With the advancement of digitalization, customer contact points have diversified, and the number of inquiries has increased year by year. Customer experience (CX) has become a crucial management theme that dictates a company's competitive advantage. While expectations for highly accurate and prompt responses are rising, the following structural challenges are becoming apparent in the field:
Challenge ① Limits of human-resource-dependent operating models
For many years, contact centers have operated on a labor-intensive model, "securing response capacity by increasing personnel." However, due to difficulties in recruitment, persistently high turnover rates, and rising labor costs, securing the necessary personnel itself has become difficult.
Meanwhile, with the spread of digital channels and the increasing complexity of services, the number of inquiries continues to grow, and the gap between demand and supply is widening.
In this situation, the traditional approach of "responding by increasing staff" has reached its limits in terms of both profitability and operations. A shift to an operating model that is not dependent on human labor is required.
Challenge ② Stagnation of self-resolution measures
In response to labor shortages, many companies have promoted self-resolution measures such as organizing FAQs, improving web guidance, and introducing chatbots and voice bots. However, the effectiveness of these efforts tends to diminish after reaching a certain level, leading to situations where "implementation has occurred, but results are not improving" and "on-site workload is not decreasing."
The reason for this is that tools are operated in isolation and are not linked with customer data such as VoC (Voice of Customer), web behavior, attributes, and past interactions. The reality is that the operational burden remains high, data necessary for improvement is not utilized, and a mechanism for continuously improving accuracy is not incorporated.
Challenge ③ Fragmentation of corporate knowledge management
Even more serious is the fragmentation of corporate knowledge data management. In many companies, knowledge is managed in a distributed manner, such as "for FAQs," "for operators," and "for AI." As a result, increased update burden and information inconsistencies occur, making consistent customer service difficult.
Furthermore, the fragmentation of customer contact points (web, chat, phone), knowledge, and response logs creates stagnation in the improvement cycle, preventing the overall evolution of customer service.
Against this background, there is a growing need to redesign the contact center's operations themselves.
■ AI Contact Center Provided by RightTouch
In response to these challenges, RightTouch is working on redesigning the contact center's operating model itself.
AI operators will handle everything from initial responses to resolution, and the knowledge integration platform will learn and integrate response logs and VoC, thereby realizing a self-evolving operating model where accuracy improves with every operation.
The biggest feature of this approach is that it enables "continuously evolving customer service," which could not be achieved with conventional, partially optimized AI implementations.
Beyond mere efficiency through automated responses, AI and humans will collaborate to improve response quality and optimize for each customer. By having AI handle routine tasks and humans focus on high-value areas such as complex issues and emotional support, more accurate responses tailored to the customer's situation and context will be possible.
In the future, the company aims to optimize data utilization across the entire enterprise, envisioning a world of "agent-to-agent" collaboration.
① "Strong AI Operator" that improves with real-world operation
This is not just a simple Q&A automated response tool; it can understand the customer's context and stably guide customers to self-resolution.
RightTouch launches "QANT Knowledge Hub (β)," an AI-ready knowledge integration platform to realize AI contact centers. Product release details here: https://righttouch.co.jp/news/4N5BcObP
The goal of this concept is not merely to "automate inquiries," but to "create a mechanism where the accuracy of inquiry resolution continuously improves with operation."
By redesigning the entire customer contact point based on AI, the system will comprehensively utilize response data and knowledge data accumulated in contact centers. This allows for organic collaboration between AI-powered automated responses (AI operators) and human support, continuously improving and advancing the customer experience (CX).
■ Background: Structural Challenges Facing Contact Centers
In recent years, the environment surrounding contact centers has changed significantly. With the advancement of digitalization, customer contact points have diversified, and the number of inquiries has increased year by year. Customer experience (CX) has become a crucial management theme that dictates a company's competitive advantage. While expectations for highly accurate and prompt responses are rising, the following structural challenges are becoming apparent in the field:
Challenge ① Limits of human-resource-dependent operating models
For many years, contact centers have operated on a labor-intensive model, "securing response capacity by increasing personnel." However, due to difficulties in recruitment, persistently high turnover rates, and rising labor costs, securing the necessary personnel itself has become difficult.
Meanwhile, with the spread of digital channels and the increasing complexity of services, the number of inquiries continues to grow, and the gap between demand and supply is widening.
In this situation, the traditional approach of "responding by increasing staff" has reached its limits in terms of both profitability and operations. A shift to an operating model that is not dependent on human labor is required.
Challenge ② Stagnation of self-resolution measures
In response to labor shortages, many companies have promoted self-resolution measures such as organizing FAQs, improving web guidance, and introducing chatbots and voice bots. However, the effectiveness of these efforts tends to diminish after reaching a certain level, leading to situations where "implementation has occurred, but results are not improving" and "on-site workload is not decreasing."
The reason for this is that tools are operated in isolation and are not linked with customer data such as VoC (Voice of Customer), web behavior, attributes, and past interactions. The reality is that the operational burden remains high, data necessary for improvement is not utilized, and a mechanism for continuously improving accuracy is not incorporated.
Challenge ③ Fragmentation of corporate knowledge management
Even more serious is the fragmentation of corporate knowledge data management. In many companies, knowledge is managed in a distributed manner, such as "for FAQs," "for operators," and "for AI." As a result, increased update burden and information inconsistencies occur, making consistent customer service difficult.
Furthermore, the fragmentation of customer contact points (web, chat, phone), knowledge, and response logs creates stagnation in the improvement cycle, preventing the overall evolution of customer service.
Against this background, there is a growing need to redesign the contact center's operations themselves.
■ AI Contact Center Provided by RightTouch
In response to these challenges, RightTouch is working on redesigning the contact center's operating model itself.
AI operators will handle everything from initial responses to resolution, and the knowledge integration platform will learn and integrate response logs and VoC, thereby realizing a self-evolving operating model where accuracy improves with every operation.
The biggest feature of this approach is that it enables "continuously evolving customer service," which could not be achieved with conventional, partially optimized AI implementations.
Beyond mere efficiency through automated responses, AI and humans will collaborate to improve response quality and optimize for each customer. By having AI handle routine tasks and humans focus on high-value areas such as complex issues and emotional support, more accurate responses tailored to the customer's situation and context will be possible.
In the future, the company aims to optimize data utilization across the entire enterprise, envisioning a world of "agent-to-agent" collaboration.
① "Strong AI Operator" that improves with real-world operation
This is not just a simple Q&A automated response tool; it can understand the customer's context and stably guide customers to self-resolution.