Last Updated： Aggregation Period:Jun 24, 2026～Jul 21, 2026
This ranking is based on the number of page views on our site.
Last Updated： Aggregation Period:Jun 24, 2026～Jul 21, 2026
This ranking is based on the number of page views on our site.
Last Updated： Aggregation Period:Jun 24, 2026～Jul 21, 2026
This ranking is based on the number of page views on our site.
511～526 item / All 526 items
Specialized trading company for tools, equipment, and machinery.
We are a specialized trading company that sells various tools, equipment, and machinery, primarily focusing on small tools necessary for "monozukuri" (manufacturing). We propose the most suitable products from thousands of manufacturers to meet the diverse needs of various industries and companies.
Specialized manufacturer of painting jigs and tools.
Specialized manufacturer of painting jigs and tools.
A system development company based in South Shinshu.
We are a company that develops various systems and software, established in 2017. Our staff, primarily consisting of returnees from the Iida and Shimoina regions, incorporates their free ideas and creativity into our software, adding "usability" to meet our customers' needs and providing software that satisfies our clients.
Transforming the insights of experts into the organization's treasure. Free explanatory materials provided.
We will solve the challenge of technology transfer that many manufacturing sites face by utilizing AI for digital asset creation. Simply analyzing sensor values (such as vibration and temperature) makes it difficult to predict true anomalies. By linking the event logs from the field that experienced professionals use to explain "why that operation was performed at that time," we can incorporate the long-developed intuition for detecting anomalies into the AI model. This enables us to elevate individual technical skills into a lasting asset for the entire organization. We provide a detailed explanation of specific methods for embedding expert skills into AI in our materials.
[Free explanatory materials] "Securing talent" is the biggest bottleneck. The next step you should take immediately.
In the 2023 survey, "securing talent" in data utilization emerged as a significantly prominent issue, overwhelming other items. The sense of shortage has increased dramatically from 45.5% in 2022 to 57.5% in 2023, becoming a critical bottleneck for many companies. In response to this situation, strategies that promote the establishment of a foundation requiring advanced specialized skills "without relying on personnel" are being sought. We will present how to build an automated foundation using external tools and AI. A "de-personalization" strategy to ensure data utilization continues even in the absence of experts will be made available in the materials.
[Free Presentation of Explanatory Materials] Repeated improvements in a short cycle, responding promptly to changes in needs.
In today's rapidly changing market and needs, the introduction of agile methods is required for building data infrastructure. By not deciding everything in the initial stages and repeatedly implementing and improving in short cycles, we can sequentially provide high-priority features. This method, which immediately reflects feedback, maximizes development speed and enables a direct connection to business value. We will provide a detailed introduction to the process of building a foundation that continues to evolve flexibly and quickly. You can learn agile development techniques for building a resilient data infrastructure through our materials.
PDFs, emails, and images are also assets. How to advance the organization of accurate and unified data.
The accuracy of AI output is directly linked to the quality of the input data and the structure of the "connections." It is important to integrate not only structured data but also unstructured data such as PDFs and images, and to provide semantic context. Inaccurate data reduces reliability and can lead to locally optimal judgments. We will explain using a simple architecture diagram for AI utilization, from data lakes to DWH and data marts. The full scope of cross-sectional collaboration design to maximize the potential of AI will be published in the materials.
Eliminate "translation loss" between strategy and implementation. Committed to business growth through collaborative engineering.
NTP Corporation is a group of professionals whose mission is to design the future from the front lines of the field. We value a sense of ownership that goes beyond mere system development and deeply engages with the business environment. We lead the entire process from IT strategy planning to design, implementation, and subsequent operations, thoroughly eliminating the "translation loss" that often occurs between strategy and implementation. We have deep strengths in both manufacturing and IT, and we are committed to delivering tangible results. We provide detailed documentation on the support system by engineers who are thoroughly familiar with the manufacturing field.
It won't change overnight. Support for a continuous approach as an organization.
A phased approach from STEP 1 to 3 is effective for the significant transformation of utilizing generative AI in business. We start with "building the foundation," such as establishing a CoE (Center of Excellence) organization, followed by conducting PoCs (Proof of Concepts) and training in specific areas. Ultimately, we progress to full-scale implementation, continuously creating new services, reducing costs, and identifying optimal use cases. We support companies in their trial and error processes, facilitating long-term and effective AI utilization. The document provides a detailed explanation of the "three steps" to avoid failure in full-scale AI implementation.
After 2026, the success or failure of a business will be determined by the "speed of value delivery."
With the advent of generative AI, the "democratization of intelligence" is progressing, and we delve into the core of the IT strategies that companies should truly seek. The proliferation of generative AI has ushered in an era where advanced data utilization is possible even for non-experts. What is crucial in future IT strategies is to maximize the "Time-to-Value," the time from conception to value delivery. Breaking through stagnation in PoC (Proof of Concept) and how quickly business impact can be generated will determine a company's competitive advantage. Please make use of this document, which encompasses new AI strategies.
Eliminating data silos with Databricks implementation, achieving self-service utilization for over 300 people.
This is an example of a refresh of IT infrastructure that significantly reduced the burden of "data preparation," a major factor hindering the utilization of data. By implementing Databricks, we eliminated data silos that were scattered across various departments and established an integrated data platform. As a result, data scientists and analysts can now utilize data through self-service, successfully reducing preparation time by 80%. Currently, a culture where over 300 employees regularly use data has been established. We have published a roadmap for building an efficient data infrastructure in our materials.
Directly interact with customers and develop an MVP in a few weeks. A sense of speed that surpasses competitors.
We will introduce a practical case of FDE that ensures rapid product development and competitive advantage in overseas expansion. In our global new business, we established a system to dispatch FDE to the local area to engage in direct dialogue with customers. We developed an MVP (Minimum Viable Product) in just a few weeks, reducing the time to market by 20% compared to conventional methods. The ability to launch quickly is a strength of the FDE model, which allows for immediate reflection of on-site feedback. Please check the materials for the secrets of speedy new business development.
Why can't we utilize IoT even though it has been implemented? Breaking free from data silos and individual reliance.
We will clarify the true nature of the so-called "barrier to AI implementation," which many Japanese companies are facing, where they cannot progress beyond the PoC (proof of concept) stage. Although data is being accumulated through IoT, there are many cases where decisions vary by site, and the data is not in a format that can be handed over to AI. Additionally, the "limitations of retrofitting" where existing equipment and rules do not assume collaboration with AI is also a significant challenge. We will discuss the importance of developing a data integration platform necessary to break through these barriers and achieve "autonomous control (physical AI)" that goes beyond mere visualization. Let's obtain a free resource that provides solutions to eliminate the bottlenecks in successfully implementing AI in your company.
Explaining the currently trending GTM engineering utilizing AI!
This document explains the DX strategy for transforming on-site data into profits and increasing sales. It includes the three levels of proficiency in AI-Ready, the reasons why traditional SFA/CRM implementations fail, a new approach called "GTM Engineering," and case studies of AI-Readiness in chemical trading companies. It also contains information about our free AI-Ready assessment (current status diagnosis). Please make use of it. 【Contents】 ■ Current state of AI utilization: Explanation of the strategic shift from defense to offense ■ Limitations of traditional sales DX: Three barriers that lead to failure with just tool implementation ■ GTM Engineering: Definition and role of a new approach to designing revenue ■ Case studies and successful models: AI-Readiness and Quick Wins in chemical trading companies ■ Expected return on investment: Impact on top line and bottom line ■ NTP's solution: Comprehensive implementation support services closely tied to the field *For more details, please download the PDF or feel free to contact us.
![[Real Estate Industry Case] Building an AI Data Platform for Extracting High-Quality Properties Using GCP](https://image.mono.ipros.com/public/product/image/2117351/IPROS7648768629228127256.png?w=280&h=280)
Reduced procurement man-hours by 80%! Successfully standardized a previously individualized process. Free explanatory materials available.
This is a development case in the real estate industry that established a system for the automatic extraction of high-quality properties and the rapid display of promising property candidates on the web. We adopted Google Cloud Platform (GCP) as the cloud infrastructure and implemented a scalable AI data platform by combining Vertex AI and BigQuery. As a result, we have successfully standardized the procurement process, which previously relied on the expertise of the personnel. Consequently, we achieved an overwhelming result by reducing the enormous labor involved in property extraction by 80%. *Specific system configurations and secrets to success during the implementation and development phases are available in the presentation materials.
