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Agent platform - List of Manufacturers, Suppliers, Companies and Products

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Example: [Recommendation of Parameters by Generative AI] Automotive Parts Manufacturer

Example: [Recommendation of Parameters by Generative AI] Automotive Parts Manufacturer

When the designer inputs parameters, it searches for past similar designs and suggests recommended parameters!

We would like to introduce a case where "generative AI" was implemented in development support at an automotive parts manufacturer. In this company, past design data was not integrated in the design of automotive parts, making it difficult to reuse similar designs. By utilizing an agent-based RAG, we integrated design data, CAE simulation results, and part characteristic data. This shortened design time and improved product development speed. 【Case Overview】 ■Challenges - Adjusting CAE simulation parameters took a lot of time - Unable to predict the impact range of design changes in advance, leading to an increase in the number of prototypes ■Results - Reduced the number of prototypes by half, achieving cost reduction - Improved simulation accuracy through optimization of CAE analysis *For more details, please refer to the related links or feel free to contact us.

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Example: Contract Review using RAG | Human Resources New Business Division

Example: Contract Review using RAG | Human Resources New Business Division

A case where standard correction proposals were automatically suggested, reducing review workload!

We would like to introduce a case where "generative AI" was implemented in the legal department of the new business division for human resources. In this department, checking contracts that vary by client took a lot of time and became a bottleneck in the contract signing process. To address this, we introduced an AI agent that utilizes the RAG model and integrates with a database of past contracts. This reduced the contract review time by 60%. 【Case Overview】 ■Challenges - There was a risk of overlooking important contract clauses, leading to unstable review accuracy. - Knowledge from similar contracts was not utilized, requiring a review from scratch each time. ■Results - The proof of concept (PoC) was completed in two months, and full-scale operation began in the third month. - The burden on the legal department was reduced, and sales speed improved. *For more details, please refer to the related links or feel free to contact us.

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Example: Automatic completion of inquiry information | SaaS company

Example: Automatic completion of inquiry information | SaaS company

The AI agent engages in chat support immediately after the form submission is completed, maintaining the user's interest!

We would like to introduce a case study of a SaaS company that implemented "generative AI" in their sales process. The company faced a challenge with a high dropout rate on their B2B inquiry form due to the cumbersome input of job titles and company size. To address this, they introduced an auto-completion feature for form inputs. When users entered their email address or company name, the AI automatically filled in the company information. This reduced the form dropout rate by 25% and expanded their opportunities for business negotiations. 【Case Overview】 ■Challenges - Slow response times after form submission led to a loss of customer interest. - Many of the inquiries received included leads with low purchasing intent. ■Results - Improved response speed immediately after inquiries, increasing the negotiation acquisition rate by 20%. - Enhanced lead scoring accuracy, boosting sales efficiency by 30%. *For more details, please refer to the related links or feel free to contact us.

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Example: Knowledge Organization using RAG | Manufacturing Industry (Production Department)

Example: Knowledge Organization using RAG | Manufacturing Industry (Production Department)

The AI automatically organizes past response history and updates the FAQ! A PoC will be conducted in two months, with full implementation in the third month.

We would like to introduce a case where "generative AI" was implemented for knowledge organization in the manufacturing industry (production department). In the company, the trouble response manuals for the manufacturing process were dispersed, making it time-consuming to find the necessary information. To address this, an AI agent utilizing RAG was introduced, integrating the manuals and trouble case database within the factory. This led to a reduction in trouble response time. [Case Overview] ■ Challenges - The burden on veteran employees concentrated due to the lack of knowledge among younger engineers. - Past cases were not systematically managed, leading to a reliance on individual responses. ■ Results - Improved response capabilities of new engineers reduced the burden on veteran employees. - Shortened trouble response time and reduced production line downtime. *For more details, please refer to the related links or feel free to contact us.

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Case Study: Machinery Manufacturer | Increase in Inquiries through SEO Enhancement and No-Code Updates

Case Study: Machinery Manufacturer | Increase in Inquiries through SEO Enhancement and No-Code Updates

With no-code support, the frequency of web page updates has increased threefold!

This is a case study of an industrial machinery manufacturer that achieved an increase in traffic through the use of SEO and improved efficiency in website updates through no-code editing. Challenges: The website's search ranking was low, and the influx of new leads was stagnating. Updates to product specifications and case studies could not be done in-house, leading to costs and time spent on external vendors. There were delays in responding to inquiries, causing leads to flow to competitors. Measures: SEO measures: Set target keywords and strengthened the technical blog and FAQ pages. No-code CMS implementation: Established an environment where staff could easily update case studies and product information. LP creation: Created landing pages focused on search traffic to increase inquiries. MA measures: Set up automated follow-up emails after inquiries to improve the conversion rate to business negotiations. Results: The website's search ranking entered the TOP 5, and organic traffic increased by 2.5 times. With no-code support, the frequency of web page updates improved by 3 times. The speed of lead response after inquiries improved, resulting in a 35% increase in the conversion rate to business negotiations.

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