[TOKYO, November 2, 2026] Recruiting up to Five Pharmaceutical, Biotech, and Research Organizations to Evaluate the Platform Using Their Own Scientific Literature and Internal Documents Ingenta Inc. (Headquarters: Minato-ku, Tokyo; hereinafter “Ingenta”) today announced that it has begun accepting applications for proof-of-concept (PoC) implementation of the cloud-based (SaaS) version of “Research Navi,” its pharmaceutical research support software that received the 2026 GOOD DESIGN AWARD. The program is open to up to five pharmaceutical companies, biotechnology ventures, and university or research institution R&D departments. Participants will be able to evaluate the platform's effectiveness in their actual research workflows using their own scientific literature and internal documents, with the program running through the end of February 2027. The application deadline is Friday, December 11, 2026. Research Navi is an AI platform for pharmaceutical research designed to connect knowledge dispersed across scientific publications and internal documents through Graph AI. It enables researchers to verify the information underlying AI-generated answers for themselves. For details of the award, please see the announcement below. Award Announcement: Pharmaceutical Research Support Software “Research Navi” Wins the 2026 GOOD DESIGN AWARD (October 27, 2026) Overview of the PoC Program The Research Navi PoC program is designed to help organizations assess how the platform can support research activities using actual research topics and their own scientific literature and internal documents. The results will help identify practical ways to use Research Navi and challenges to address before full-scale deployment. Implementation Process Preparation: Design the Evaluation and Configure the Environment Ingenta will work with each organization's representatives to agree on research topics and evaluation criteria. Together, the parties will select the scientific publications and internal documents to be imported, establish an environment segregated for each organization, and configure user accounts and access permissions. Ingenta will also explain its data handling and security mechanisms to the participant's information systems team. Usage: Apply Research Navi to Real Research Topics Following hands-on training, researchers will use Research Navi in their day-to-day research work. Participants will be able to upload documents, perform analysis and searches, and verify supporting evidence themselves. Ingenta will hold regular meetings throughout the program to provide guidance on how best to use the platform. Effectiveness Evaluation: Summarize Findings and Define Next Steps The platform's effectiveness will be evaluated against the agreed criteria, and the findings will be compiled into a report. Ingenta will identify potential use cases and challenges related to full-scale implementation and propose next steps. Evaluation Criteria (Examples) Time required for literature searches and information organization Ease of verifying the evidence and sources behind AI-generated answers Reuse of internal documents and findings from previous investigations Suitability for the organization's research topics and workflows Program Details Eligible participants：R&D departments of pharmaceutical companies, biotechnology ventures, universities, and research institutions Number of participants：Up to five organizations Application deadline：· Friday, December 11, 2026 (applications will close once five participants have been selected) Program period：· Through the end of February 2027 (approximately two to three months from commencement, including preparation and effectiveness evaluation; start dates will be determined in consultation with participants) Product：Research Navi, cloud-based version (please contact Ingenta separately regarding the on-premises version) How to apply：· Contact Ingenta through the inquiry form on the Research Navi official website (https://navi.ingenta.ai/) or using the contact details below. Please include your organization, research field, and a brief description of the topic you would like to evaluate. Participation conditions will be provided individually according to the scope of the evaluation. Note: Visitors can try Research Navi hands-on at GOOD DESIGN Marunouchi during the second exhibition period of “My Favorite G Mark 2026” (October 31–November 13, 2026). Please see the award announcement for details. What Participants Can Explore During the PoC Verify AI-Generated Answers and Their Supporting Evidence on a Single Screen The Research Navi answer interface consists of three tabs: “Response,” “Explore,” and “Reference Materials.” Researchers can read AI-generated answers under “Response,” trace connections among related scientific publications and medical subject headings (MeSH) through a graph under “Explore,” and check the original sources under “Reference Materials.” Selecting a publication in the graph displays its type, title, source, and content. Build on Research Findings by Topic For each research topic (“My Research”), users can organize conversations, bookmarks, and referenced materials in one place. This allows participants to review their investigations and findings by topic throughout the PoC period. Illustrative Use Cases Exploring hypotheses for new research topics：Ask questions about drug targets or disease mechanisms, then trace connections among related publications and medical terminology in the graph to identify candidate hypotheses worth investigating. Comparing previous internal investigations with current findings：Import internal research reports and experimental records and query them together with published literature to re-examine the conditions and decision-making behind experiments that did not produce the expected results, in light of the latest findings. Conducting literature reviews with verifiable evidence：·Review the sources associated with each answer, return to the original materials to confirm the content, and organize the research findings accordingly. Note: The above are illustrative use cases. The scope of each PoC will be defined according to the participant's research topics. Data Handling During the PoC For the cloud-based version used in the PoC, document data is stored in a cloud environment in Japan (Tokyo region) and managed separately for each organization. Generative AI is accessed through APIs under terms whereby input data is not used for model training. The platform also incorporates access permission controls, encrypted communications, and operation logging, among other mechanisms for handling research data securely. Confidentiality agreements and the handling of data after the PoC will be agreed upon before the program begins. For organizations that wish to operate Research Navi without transmitting research data outside their own environments, Ingenta can separately discuss the on-premises version, which runs on customers' internal servers. Future Plans Through the PoC program, Ingenta will work with researchers at participating organizations to identify both the situations in which Research Navi delivers value and the challenges that remain. Feedback collected during the program will be reflected in future product development. The official subscription-based cloud service is scheduled for release in 2027. About Ingenta Inc. Ingenta Inc. is an AI technology company specializing in achieving the accuracy required for real-world deployment of advanced AI technologies and providing end-to-end implementation support. In addition to deep learning, Ingenta has applied Graph AI to analyze and utilize complex knowledge structures in multiple R&D and business process improvement projects. Ingenta develops solutions that balance accuracy and practical implementation in highly specialized fields, including healthcare, pharmaceuticals, and manufacturing. Drawing on expertise from Japan and overseas, the company provides flexible AI implementation support tailored to each organization's specific challenges. In 2026, Ingenta was selected as a finalist for the GENIAC-PRIZE, a generative AI social implementation initiative led by Japan's Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO). https://www.ingenta.ai/ Contact Information Ingenta Inc. Mitsuyoshi Okamoto, Business Development Manager Email: info@ingenta.ai The information contained in this press release is accurate as of the date of publication and is subject to change without prior notice.

[TOKYO, October 27, 2026] Also Selected for the Jury's “My Favorite G Mark 2026” — Supporting Researchers’ Decision-Making by Providing Evidence Alongside AI-Generated Answers Ingenta Inc. (Headquarters: Minato-ku, Tokyo; hereinafter “Ingenta”) is pleased to announce that its pharmaceutical research support software, “Research Navi,” has received the 2026 GOOD DESIGN AWARD, organized by the Japan Institute of Design Promotion. Research Navi has also been selected for “My Favorite G Mark 2026,” a special exhibition featuring award-winning designs personally chosen by GOOD DESIGN AWARD jury members. Research Navi is an AI platform that uses Graph AI to structurally connect knowledge distributed across scientific publications, patents, internal documents, and experimental data. It supports hypothesis exploration, literature research, and evidence verification in pharmaceutical research. Rather than simply providing answers, the platform is designed to enable researchers to verify the information and knowledge relationships underlying AI-generated responses. Evaluation by the GOOD DESIGN AWARD Jury The GOOD DESIGN AWARD jury provided the following evaluation: One of the strengths of generative AI lies in its ability to interpret, match, and organize vast amounts of unstructured text while taking context into account. These are tasks traditionally performed by the human brain, but generative AI makes it possible to carry them out beyond human limitations. Research Navi can be regarded as an exemplary system that effectively utilizes this strength of generative AI. By visualizing connections among knowledge in the exploration and reasoning processes of pharmaceutical research, the platform is expected to make a significant contribution to advances in the field. Also Selected for “My Favorite G Mark 2026” Research Navi has also been selected for “My Favorite G Mark 2026,” a special exhibition in which GOOD DESIGN AWARD jury members personally choose award-winning designs that captured their attention during the second round of judging. This year, 88 designs selected by 101 jury members will be presented across three exhibition periods at GOOD DESIGN Marunouchi. Susumu Namikawa, the jury member who selected Research Navi, provided the following comment: One of the strengths of large language models is their ability to handle not only numerical data but also vast amounts of knowledge expressed in natural language. Research Navi takes advantage of this capability to structure relationships across extensive scientific literature and perform reasoning. I believe this approach has the potential to be applied not only to pharmaceutical research but also to a wide range of other research fields. GOOD DESIGN AWARD Jury Member Chief AI Officer / Executive Creative Director, dentsu Japan Susumu Namikawa In addition, a talk event featuring Chih-Chuan Lin, Producer of Research Navi, and jury member Susumu Namikawa will be held at GOOD DESIGN Marunouchi on Tuesday, November 10. For details, including the event schedule, please refer to the “Exhibitions and Events” section later in this article. Background Behind the Development of Research Navi In pharmaceutical research, valuable insights that may lead to new hypotheses are found not only in published scientific papers and patents, but also in internal research reports, experimental data, and records of past investigations. However, this information is accumulated across different locations and formats, requiring considerable time and specialized expertise to locate relevant materials and understand the relationships among them. Even experiments that did not produce the expected results can provide valuable clues for future research when their conditions and decision-making processes are reviewed. Ingenta developed Research Navi to help researchers make effective use of distributed information and accumulated records of trial and error. Key Design Features of Research Navi Capturing Connections Among Distributed Knowledge with Graph AI Research Navi structurally connects knowledge contained in scientific publications, patents, internal documents, and experimental data. Rather than searching for documents individually, researchers can explore relationships among information sources to identify clues for developing and evaluating hypotheses. For example, the platform enables researchers to cross-reference scientific literature related to a particular research topic with internal experimental records. Providing Verifiable Evidence Alongside AI-Generated Answers To effectively utilize generative AI in research, it is essential to verify which sources were referenced and how an answer was derived. Research Navi presents relevant scientific literature, internal data, and connections among knowledge alongside AI-generated responses. This enables researchers to examine the original sources and make informed judgments based on their own expertise. Designing Around Researchers’ Workflows Research Navi supports the research process as a continuous workflow, extending beyond literature review to hypothesis exploration, evidence verification, and planning for subsequent validation. By reducing the burden of searching for and organizing information, the platform aims to give researchers more time to think critically and conduct experiments. Two Deployment Options for Protecting Research Data While the use of generative AI in business continues to expand, the handling of confidential information remains a major challenge. Research Navi offers both a cloud-based version accessible through web browsers and an on-premises version that can operate on customers’ internal servers. In the cloud version, document data is stored in a cloud environment located in Japan (Tokyo region), with data managed separately for each organization. Generative AI is accessed through APIs under terms that prohibit the use of input data for model training. The platform also incorporates security measures such as access permission controls, encrypted communications, and operation logs to protect research data. The on-premises version enables organizations to use a domestically developed large language model (LLM), customized by Ingenta for medical and pharmaceutical applications, within their own internal environments without transmitting research data outside the organization. From “AI That Provides Answers” to “AI That Supports Decision-Making” The goal of Research Navi is not to have AI determine research conclusions. Instead, it aims to create an environment in which researchers can connect distributed knowledge, verify supporting evidence, and make their own informed decisions. Ingenta will continue to develop Research Navi through practical applications in research environments, helping transform accumulated data and lessons learned through trial and error into valuable insights for future research. Commenting on the development of Research Navi, Chih-Chuan Lin, Vice President of Ingenta and Producer of Research Navi, stated: "What mattered most to us in designing Research Navi was not getting researchers to 'trust' AI-generated answers, but enabling them to 'verify' those answers. In pharmaceutical research, every decision is supported by extensive scientific literature and countless rounds of trial and error, including unsuccessful experiments. We have continuously explored how to naturally guide researchers through the supporting evidence so they can apply their own expertise and make informed decisions. We are deeply honored that these efforts have been recognized. Moving forward, we will continue listening to researchers in the field and further developing AI that supports their decision-making." Expanding the Graph AI Engine Behind Research Navi into Other Industries Ingenta's Graph AI engine, which forms the core of Research Navi, is also being applied across a variety of industries and business operations beyond pharmaceutical research. These applications include transforming tacit knowledge into explicit knowledge in manufacturing, conducting impact analysis based on relationships among components and products, utilizing knowledge in architectural design, and supporting educational content management and AI-assisted learning for professional certification examinations. Through these initiatives, Ingenta continues to expand the practical application of structurally connected knowledge across industries. Ingenta InsightCraft, which utilizes the same Graph AI engine, is a service designed to extract insights from customer feedback. It was developed in collaboration with the call center and marketing departments of a major pharmaceutical company, incorporating practical expertise from their operations. Users can ask questions in natural language and trace the answers back to the underlying customer statements and records. Ingenta InsightCraft has already been introduced through individual implementations and co-development projects, with plans for a full-scale SaaS rollout in the future. Exhibitions and Events GOOD DESIGN EXHIBITION 2026 Dates: October 31 (Sat) – November 4 (Wed), 2026 Venue: Tokyo Midtown (9-7-1 Akasaka, Minato-ku, Tokyo) Details: https://www.g-mark.org/ Research Navi will be featured in a panel exhibition and is also scheduled to be presented in the organizer's “My Favorite G Mark” exhibition area. GOOD DESIGN AWARD Jury Selection: My Favorite G Mark 2026 — Period 2 “Making Things That Create the Future” Dates: October 31 (Sat) – November 13 (Fri), 2026, 11:00–20:00 (until 17:00 on the final day) Venue: GOOD DESIGN Marunouchi (1F, Shin-Kokusai Building, 3-4-1 Marunouchi, Chiyoda-ku, Tokyo) Admission: Free, no reservation required Details: A working demonstration of Research Navi will be available at the venue, allowing visitors to experience the platform firsthand. Talk Event Information ”Structuring a Sea of Literature — What Does It Mean for AI to ‘Read’?” As part of the related programming for the GOOD DESIGN AWARD jury selection exhibition “My Favorite G Mark 2026,” a special talk event focusing on Research Navi will be held. The event will feature Chih-Chuan Lin, Producer of Research Navi, and Susumu Namikawa, the GOOD DESIGN AWARD jury member who selected Research Navi for “My Favorite G Mark.” • Date & Time: Tuesday, November 10, 2026, 19:00–20:00 • Venue: GOOD DESIGN Marunouchi • Topic: “Structuring a Sea of Literature — What Does It Mean for AI to ‘Read’?” • Speakers: Chih-Chuan Lin (Producer, Research Navi) Susumu Namikawa, (GOOD DESIGN AWARD Jury Member) • Participation: No reservation required Award Overview About the GOOD DESIGN AWARD Established in 1957, the GOOD DESIGN AWARD is Japan's only comprehensive design evaluation and recommendation program. It recognizes a wide range of tangible and intangible designs, including products, architecture, services, systems, and regional initiatives. The G Mark, the symbol of the award, is widely recognized as an emblem of good design. The year 2026 marks the 70th edition of the award. (Organizer: Japan Institute of Design Promotion) About Research Navi Research Navi welcomes inquiries from companies and research institutions involved in pharmaceutical research regarding implementation and deployment. Information regarding applications for Proof of Concept (PoC) implementations will be announced soon. About Ingenta Inc. Ingenta Inc. is an AI technology company specializing in achieving the accuracy required for real-world deployment of advanced AI technologies and providing end-to-end implementation support. In addition to Deep Learning, Ingenta has extensive experience in implementing solutions for research and development and business optimization through the analysis and utilization of complex knowledge structures using Graph AI. Ingenta develops solutions that balance accuracy and practical implementation in highly specialized fields, including healthcare, pharmaceuticals, and manufacturing. Drawing on expertise from Japan and overseas, the company provides flexible AI implementation support tailored to each organization's specific challenges. In 2026, Ingenta was selected as a finalist for the GENIAC-PRIZE, a generative AI social implementation initiative led by Japan's Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO). Contact Information Ingenta Inc. Mitsuyoshi Okamoto Business Development Manager Email：info@ingenta.ai The information contained in this press release is accurate as of the date of publication and is subject to change without prior notice.

[TOKYO, March 24, 2026] Redefining R&D productivity: Ingenta’s proprietary "LLM x Graph" technology transforms tacit research knowledge into scalable organizational assets. About the GENIAC-PRIZE The GENIAC-PRIZE is a strategic initiative by METI and NEDO designed to enhance Japan’s generative AI development capabilities and promote real-world social implementation. Official Website: https://geniac-prize.nedo.go.jp/ Ingenta, a pioneer in specialized AI solutions, announced that it has been selected as a finalist for the GENIAC-PRIZE, a flagship national project led by Japan’s Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO). Out of 58 competitive applicants, Ingenta was recognized as one of only eight finalists in the "Externalization of Tacit Knowledge in Manufacturing" category. Ingenta stands as the sole finalist representing the pharmaceutical sectors, marking a significant milestone for AI implementation in deep-tech industries. Turning "Failure" into Fuel for Innovation In collaboration with Biomimetics Sympathies, Ingenta proposed a groundbreaking system: "Autonomous Collaborative Hypothesis Formulation via Proprietary LLM x Graph Technology." While traditional R&D often overlooks "failed" experimental data, Ingenta’s solution structures this untapped information alongside research logs and scientific literature. By integrating Large Language Models (LLMs) with Knowledge Graph technology, the system enables: Preservation of Tacit Knowledge: Seamlessly capturing the intuition and expertise of veteran researchers. Accelerated Hypothesis Generation: Providing AI-driven decision support to streamline the drug discovery process. Scalable R&D Intelligence: Transforming fragmented data into a cohesive, searchable, and actionable asset. "Being selected as a GENIAC-PRIZE finalist validates our vision of a future where AI doesn't just process data, but truly understands the nuances of scientific discovery," said Dennis Hsueh, CEO of Ingenta. "By capitalizing on 'failure data' that was previously discarded, we are empowering pharmaceutical leaders to reach breakthroughs faster and more reliably." "In the high-stakes fields of drug discovery and regenerative medicine, critical insights are often hidden not just in our successes, but in the history of failed trials and complex decision-making processes," said Keisuke Onishi, CEO of Biomimetics Sympathies. "Historically, this invaluable knowledge remained fragmented across scattered research notes, experimental logs, and individual expertise, making it nearly impossible to leverage at scale. Through our collaboration with Ingenta, we have successfully demonstrated that this disparate data can be structured into an actionable asset that directly fuels hypothesis formulation. What makes this technology truly transformative is its ability to align with the actual thought processes of researchers, going far beyond simple search or summarization. We are confident that this approach will not only revolutionize R&D in life sciences but will serve as a high-impact solution for any industry navigating complex technical documentation and trial-and-error histories." About Ingenta Inc. Headquartered in Tokyo, Ingenta is a deep-tech AI startup specializing in the structural transformation of technical knowledge. By leveraging its proprietary "LLM x Graph" technology, Ingenta helps R&D-intensive industries—such as pharmaceuticals and advanced manufacturing—convert vast amounts of unstructured data and tacit expertise into actionable intelligence, driving the next generation of industrial innovation. About Biomimetics Sympathies Inc. Biomimetics Sympathies is a leading biotechnology firm dedicated to advancing drug discovery and regenerative medicine. By combining cutting-edge biological insights with strategic partnerships, the company focuses on developing innovative therapeutic solutions to address unmet medical needs and improve patient outcomes globally. Contact Information Ingenta Inc. Mitsuyoshi Okamoto | Senior Business Development Manager Email: info@ingenta.ai.

[Taipei, TAIWAN - December 4, 2024] Ingenta, a leader in Graph AI solutions, shares that its Chief AI Officer, Dr. Kuo-Ching Liang, recently delivered a talk at Fu Jen Catholic University’s College of Medicine. The presentation, titled “Graph Neural Networks in Healthcare: From Mental Health Diagnosis to Drug Innovation,” explored how advanced AI technologies are transforming the medical field. The event, held on campus, began with an opening introduction by Professor Ping-Keung Yip, Dean of the College of Medicine, who highlighted the significance of interdisciplinary collaboration in advancing healthcare innovation. The invitation was extended by Professor Yi-Nou Shih of the MS program in transdisciplinary long-term care, recognizing Dr. Liang’s expertise in Graph AI and its potential to inspire new approaches in healthcare. Dr. Liang’s talk centered on the transformative role of Graph Neural Networks (GNNs) in healthcare, with a focus on their applications in understanding patient behavior and accelerating medical innovations. GNNs, as a cutting-edge AI technology, enable the analysis of complex relationships in data, offering new possibilities in fields such as mental health care and drug discovery. Dr. Liang shared insights on how these networks can uncover hidden patterns and enhance the integration of data-driven approaches into healthcare systems, driving better outcomes for patients. “Graph Neural Networks allow us to uncover hidden patterns in data that were previously out of reach,” Dr. Liang remarked. “This opens up new opportunities to better understand patient behaviors and develop targeted treatments, ultimately improving outcomes across the healthcare spectrum.” Ingenta extends its gratitude to Fu Jen Catholic University and its esteemed faculty for the opportunity to share insights. The talk highlighted the symbiotic relationship between academia and industry, emphasizing the role of AI in building a healthier future for all. For more information about Ingenta’s innovative work in Graph AI or to explore collaboration opportunities, please visit our website or contact us directly. About the College of Medicine, Fu Jen Catholic University (FJCU) Established in 1961, the College of Medicine at FJCU is one of Taiwan’s leading medical schools, known for integrating academic excellence with compassionate healthcare. The college fosters interdisciplinary collaboration and is committed to advancing medical research, clinical training, and patient care solutions. Within the college, the MS Program in Transdisciplinary Long-Term Care, launched in 2012, addresses the global aging challenge by combining expertise from various fields to enhance long-term care services, with a focus on big data and biomedical advancements to improve patient outcomes and care efficiency. About the Speaker, Dr. Kuo-Ching Liang Dr. Liang is an AI researcher with 18 years of experience developing novel algorithms in the fields of communications, bio and medical informatics, and a wide range of real-world business applications. He holds a PhD from Columbia University, an MS from the University of Pennsylvania, an MEng from Cornell University, and a BS from the University of California, Berkeley. Prior to the startup venture, he served in several research and faculty positions in world-renowned organizations including Mayo Clinic, the University of Tokyo, and the Keio University School of Medicine.