Cataris Launches Official Version of "Cataris," a Deep Research AI Agent Specialized in Chemical Materials
Cataris Co., Ltd. announced the official launch of "Cataris," a Deep Research AI agent specialized for the chemical materials industry, on May 11, 2026. This platform provides end-to-end support from material application exploration to improvement proposals, and is, according to the company's research, the first of its kind in Japan.
📋 Article Processing Timeline
- 📰 Published: May 11, 2026 at 20:00
- 🔍 Collected: May 11, 2026 at 11:31
- 🤖 AI Analyzed: May 11, 2026 at 12:15 (43 min after Collected)
Cataris Co., Ltd. (Headquarters: Minato-ku, Tokyo; Representative Director CEO: Satoshi Matsumoto) will begin offering its AI platform "Cataris" for the chemical materials industry from May 11, 2026. To allow more users to experience this service, the company plans to exhibit at the "Sustainable Material Expo" within "Highly-functional Material Week [Osaka]" to be held at Intex Osaka from May 13 (Wednesday) to May 15 (Friday), 2026.
"Cataris" is a Deep Research AI agent specialized in chemical materials, leveraging multiple expert AIs to cross-utilize the latest external data and customer-specific data. It provides end-to-end support for hypothesizing new application candidates based on the structural characteristics of materials, extracting potential customers, and proposing improvements for target physical properties.
This service offers a comprehensive package to the chemical materials industry, integrating multiple specialized AIs and a customer-specific data platform. It is the first offering in Japan* to provide end-to-end support from application exploration to customer identification and improvement proposals.
* As of April 6, 2026, based on our company's research. Investigation conducted on publicly available information.
■ Changes in the Environment Surrounding the Chemical Materials Industry
In recent years, the environment surrounding the chemical materials industry has undergone significant changes. The Ministry of Economy, Trade and Industry positions the materials industry as a foundational industry supporting industrial competitiveness and considers strengthening competitiveness, taking into account GX (Green Transformation) and economic security, as a crucial issue. In the growth strategy of the Takayama Cabinet, the formulation of public-private investment roadmaps is underway for each of 17 strategic areas, with "complex new materials utilizing AI, etc." positioned as one of the key themes in the materials field. Furthermore, the Ministry of Education, Culture, Sports, Science and Technology has announced a policy to promote AI for Science, accelerating the utilization of AI across the entire R&D process. Against this backdrop, the importance of AI utilization, connecting R&D with business promotion, is also increasing in the chemical materials industry.
■ Challenges Faced by the Chemical Materials Industry
[Operational Challenges]
Tasks related to exploring applications and developing proposals for chemical materials require broad knowledge spanning chemistry, materials, applications, and markets. These tasks have traditionally been highly person-dependent and carried out over long periods. Additionally, in proposing material improvements for customer requests and anticipated applications, customer feedback obtained at sales sites and development knowledge held by R&D departments are often disconnected. This leads to challenges such as significant effort required to determine the direction of improvement development for sample products. "Cataris" was developed to advance these person-dependent and often fragmented operational flows using an AI agent.
[Technical Challenges]
Conventionally, exploring applications for chemical materials often stopped at information gathering and idea generation for candidate applications using general-purpose LLMs, RAG, and web searches, or information extraction from past internal documents. Beyond this, the organization of scientific evidence, evaluation of application scale, identification of potential customers, and consideration of material improvement policies frequently reverted to manual processes.
Furthermore, exploring chemical material applications and related development tasks require complex processing of both divergent and convergent thinking. Traditional methods, such as general-purpose LLM + RAG, faced technical hurdles in practical application due to challenges like "limited exploration space" and "tendency to converge on known patterns." "Cataris" is designed to bridge this technical gap.
■ About Cataris
[What Cataris Can Achieve]
"Cataris" identifies candidate applications and assumed required physical properties based on developed materials, related patents, and papers. It also presents application scale and potential customers. Additionally, by incorporating target physical properties and customer requests as further input, it supports improvement proposals tailored to the requirements of each application. For example, through examining improvement hypotheses, including structural changes and directions for additives/formulations, it promotes efficiency in sample work and early-stage development.
Specific use cases include the following applications:
1. Application Exploration for Existing Materials
For functional materials such as developed resins and additives, it organizes candidate applications, required physical properties for each application, and potential customers by cross-referencing related papers, patents, market information, and company information.
2. Improvement Proposals for Customer Requests
For example, in response to requirements such as heat resistance, transparency, and processability, it provides initial improvement hypotheses and verification points based on relevant knowledge, thereby supporting the direction-setting for development.
[Technical Features]
The biggest feature of "Cataris" is its ability to advance tasks from application exploration to customer identification and material improvement proposals in a seamless manner with AI on a virtual development space specialized for chemical material development and business promotion.
Another feature is that it doesn't just present candidate applications as mere ideas, but also connects the organization of scientific evidence, implications for business feasibility, and even material improvement proposals on the same platform.
At its core, it adopts a multi-agent architecture, where multiple specialized AIs are positioned under a parent AI.
"Cataris" is a Deep Research AI agent specialized in chemical materials, leveraging multiple expert AIs to cross-utilize the latest external data and customer-specific data. It provides end-to-end support for hypothesizing new application candidates based on the structural characteristics of materials, extracting potential customers, and proposing improvements for target physical properties.
This service offers a comprehensive package to the chemical materials industry, integrating multiple specialized AIs and a customer-specific data platform. It is the first offering in Japan* to provide end-to-end support from application exploration to customer identification and improvement proposals.
* As of April 6, 2026, based on our company's research. Investigation conducted on publicly available information.
■ Changes in the Environment Surrounding the Chemical Materials Industry
In recent years, the environment surrounding the chemical materials industry has undergone significant changes. The Ministry of Economy, Trade and Industry positions the materials industry as a foundational industry supporting industrial competitiveness and considers strengthening competitiveness, taking into account GX (Green Transformation) and economic security, as a crucial issue. In the growth strategy of the Takayama Cabinet, the formulation of public-private investment roadmaps is underway for each of 17 strategic areas, with "complex new materials utilizing AI, etc." positioned as one of the key themes in the materials field. Furthermore, the Ministry of Education, Culture, Sports, Science and Technology has announced a policy to promote AI for Science, accelerating the utilization of AI across the entire R&D process. Against this backdrop, the importance of AI utilization, connecting R&D with business promotion, is also increasing in the chemical materials industry.
■ Challenges Faced by the Chemical Materials Industry
[Operational Challenges]
Tasks related to exploring applications and developing proposals for chemical materials require broad knowledge spanning chemistry, materials, applications, and markets. These tasks have traditionally been highly person-dependent and carried out over long periods. Additionally, in proposing material improvements for customer requests and anticipated applications, customer feedback obtained at sales sites and development knowledge held by R&D departments are often disconnected. This leads to challenges such as significant effort required to determine the direction of improvement development for sample products. "Cataris" was developed to advance these person-dependent and often fragmented operational flows using an AI agent.
[Technical Challenges]
Conventionally, exploring applications for chemical materials often stopped at information gathering and idea generation for candidate applications using general-purpose LLMs, RAG, and web searches, or information extraction from past internal documents. Beyond this, the organization of scientific evidence, evaluation of application scale, identification of potential customers, and consideration of material improvement policies frequently reverted to manual processes.
Furthermore, exploring chemical material applications and related development tasks require complex processing of both divergent and convergent thinking. Traditional methods, such as general-purpose LLM + RAG, faced technical hurdles in practical application due to challenges like "limited exploration space" and "tendency to converge on known patterns." "Cataris" is designed to bridge this technical gap.
■ About Cataris
[What Cataris Can Achieve]
"Cataris" identifies candidate applications and assumed required physical properties based on developed materials, related patents, and papers. It also presents application scale and potential customers. Additionally, by incorporating target physical properties and customer requests as further input, it supports improvement proposals tailored to the requirements of each application. For example, through examining improvement hypotheses, including structural changes and directions for additives/formulations, it promotes efficiency in sample work and early-stage development.
Specific use cases include the following applications:
1. Application Exploration for Existing Materials
For functional materials such as developed resins and additives, it organizes candidate applications, required physical properties for each application, and potential customers by cross-referencing related papers, patents, market information, and company information.
2. Improvement Proposals for Customer Requests
For example, in response to requirements such as heat resistance, transparency, and processability, it provides initial improvement hypotheses and verification points based on relevant knowledge, thereby supporting the direction-setting for development.
[Technical Features]
The biggest feature of "Cataris" is its ability to advance tasks from application exploration to customer identification and material improvement proposals in a seamless manner with AI on a virtual development space specialized for chemical material development and business promotion.
Another feature is that it doesn't just present candidate applications as mere ideas, but also connects the organization of scientific evidence, implications for business feasibility, and even material improvement proposals on the same platform.
At its core, it adopts a multi-agent architecture, where multiple specialized AIs are positioned under a parent AI.