GIBBERISH and TB Next Communications Launch Collaboration for Contact Center DX Utilizing Generative AI
GIBBERISH and TB Next Communications to Collaborate on Generative AI for Contact Centers
📋 Article Processing Timeline
- 📰 Published: March 28, 2026 at 00:51
- 🔍 Collected: March 28, 2026 at 21:59 (21h 8m after Published)
- 🤖 AI Analyzed: April 15, 2026 at 03:44 (413h 44m after Collected)
GIBBERISH Inc. (Headquarters: Shibuya-ku, Tokyo; President and CEO: Takashi Ide; hereinafter referred to as GIBBERISH) will collaborate with TB Next Communications Inc. (Headquarters: Toshima-ku, Tokyo; President and CEO: Koji Otsuki; hereinafter referred to as TB Next Communications), which operates a contact center support business, to support the reform of contact center operations utilizing generative AI.
As part of this collaboration, a proof of concept (PoC) was conducted for the contact center operations of a major automobile manufacturer, confirming results such as a maximum reduction of 50% in inquiry response time.
■ Contact Center Challenges and the Necessity of Generative AI
In contact centers, operational burdens are increasing due to the rise and diversification of inquiries, while the difficulty of securing and training personnel is increasing year by year. Balancing quality and response speed with limited resources has become a major management challenge.
Generative AI is expected as a solution, but simply introducing AI tools does not yield sufficient results. It is essential to review business processes themselves and incorporate AI.
■ Solution Approach Through Collaboration Between Both Companies
By combining the BPR and operational design expertise in contact centers cultivated by TB Next Communications with the technical and operational implementation know-how accumulated by GIBBERISH through supporting over 1,000 generative AI implementations, we will support the transition to business design that assumes generative AI.
By utilizing GIBBERISH's customer service platform "DECA AI接客" (DECA AI Sekkyaku), we will achieve business process reform that goes beyond mere tool introduction by supporting everything from knowledge management to AI implementation and on-site operations.
■ Results Demonstrated in PoC
A proof of concept (PoC) utilizing "DECA AI接客" was conducted at an inquiry desk for a major automobile manufacturer's dealerships, which is handled by TB Next Communications.
### Implementation Overview
* Period: July - December 2025
* Target: Specific inquiry scope (contract confirmation/application, usage methods, etc.)
* Verification Task: Actual email response operations
### Introduced Solutions
In this project, the following functions from GIBBERISH's "DECA AI接客" were introduced:
* Channel-Integrated Knowledge Base: A database was built to centrally manage scattered FAQs, manuals, and past inquiry history.
* AI Widget: An assistant tool that can be used on the operator's browser without the need for new application additions. It supports automatic generation of response drafts based on inquiry content and immediate knowledge search.
### Main Results
**① Reduction of Email Response Workload by Up to 50%**
Through AI-assisted knowledge search and response draft generation, the time from response preparation to reply composition was reduced from an average of approximately 10 minutes to approximately 5 minutes.
**② Achieved a Search Hit Rate of Approximately 80%**
High-precision search hit rates were achieved through knowledge organization and optimization of related keywords.
**③ Standardization of Response Quality**
By having AI present response drafts that reflect the knowledge of experienced operators, the experience of...
As part of this collaboration, a proof of concept (PoC) was conducted for the contact center operations of a major automobile manufacturer, confirming results such as a maximum reduction of 50% in inquiry response time.
■ Contact Center Challenges and the Necessity of Generative AI
In contact centers, operational burdens are increasing due to the rise and diversification of inquiries, while the difficulty of securing and training personnel is increasing year by year. Balancing quality and response speed with limited resources has become a major management challenge.
Generative AI is expected as a solution, but simply introducing AI tools does not yield sufficient results. It is essential to review business processes themselves and incorporate AI.
■ Solution Approach Through Collaboration Between Both Companies
By combining the BPR and operational design expertise in contact centers cultivated by TB Next Communications with the technical and operational implementation know-how accumulated by GIBBERISH through supporting over 1,000 generative AI implementations, we will support the transition to business design that assumes generative AI.
By utilizing GIBBERISH's customer service platform "DECA AI接客" (DECA AI Sekkyaku), we will achieve business process reform that goes beyond mere tool introduction by supporting everything from knowledge management to AI implementation and on-site operations.
■ Results Demonstrated in PoC
A proof of concept (PoC) utilizing "DECA AI接客" was conducted at an inquiry desk for a major automobile manufacturer's dealerships, which is handled by TB Next Communications.
### Implementation Overview
* Period: July - December 2025
* Target: Specific inquiry scope (contract confirmation/application, usage methods, etc.)
* Verification Task: Actual email response operations
### Introduced Solutions
In this project, the following functions from GIBBERISH's "DECA AI接客" were introduced:
* Channel-Integrated Knowledge Base: A database was built to centrally manage scattered FAQs, manuals, and past inquiry history.
* AI Widget: An assistant tool that can be used on the operator's browser without the need for new application additions. It supports automatic generation of response drafts based on inquiry content and immediate knowledge search.
### Main Results
**① Reduction of Email Response Workload by Up to 50%**
Through AI-assisted knowledge search and response draft generation, the time from response preparation to reply composition was reduced from an average of approximately 10 minutes to approximately 5 minutes.
**② Achieved a Search Hit Rate of Approximately 80%**
High-precision search hit rates were achieved through knowledge organization and optimization of related keywords.
**③ Standardization of Response Quality**
By having AI present response drafts that reflect the knowledge of experienced operators, the experience of...