{"id":27,"date":"2025-07-30T21:56:33","date_gmt":"2025-07-30T12:56:33","guid":{"rendered":"http:\/\/conceptminer.ai\/?page_id=27"},"modified":"2026-04-23T12:11:19","modified_gmt":"2026-04-23T03:11:19","slug":"conceptminer-%e3%80%80%e3%80%80%e3%80%80%e3%80%80%e3%80%80","status":"publish","type":"page","link":"https:\/\/conceptminer.ai\/","title":{"rendered":"ConceptMiner \u3000\u3000\u3000\u3000\u3000"},"content":{"rendered":"\n
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Having the same concept map, whether with other humans or with AI, is the first step to a fruitful dialogue, and fortunately, AI can incorporate this into its systems.\u3000<\/h2>\n\n\n\n

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ConceptMiner Cloud<\/h2>\n\n\n\n

Concept network engine for developing AI apps<\/p>\n\n\n\n

You can use ConceptMiner’s functions from your app via API connection. By organinzing the dialogue between end users and LLM, you can develop advanced AI systems that can recall memories in context. While typical RAG (Search Augmentation Generation) simply searches for information by the shortest distance vector, ConceptMiner models conceptual structure, allowing for more sophisticated context identification.
CoceptMiner Engine for Developers<\/a><\/p>\n\n\n\n

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Thinking Assitance App
Get Started for Free<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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ConceptMiner Local<\/h2>\n\n\n\n

Security-enhanced Concept Network Engine<\/p>\n\n\n\n

ConceptMiner can be installed in your company and incorporated into your system. It can be used for both cloud LLM and local LLM. In the future, as the performance of local LLM improves, advanced AI utilization will become possible, completely within your company. This will enable you to model the conceptual structure of various non-standard text information in your company database, such as “customer feedback” from call centers, meeting minutes, and sales daily reports, and use it for strategic decision-making.<\/p>\n\n\n\n

Enterprise Local Edition is here<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

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This plays a part in AI governance, providing a foundation for human-AI collaboration.<\/h4>\n\n\n\n

Information analysis involves analysing qualitative information expressed in natural language and quantitative data expressed in numerical or categorical values. Traditionally, these were considered to be completely different and incompatible. However, we have finally developed a framework that integrates them.
The progress of modern AI (artificial intelligence) based on deep learning has been remarkable, and there are already whispers that in a few years, AI will be able to autonomously generate innovation, devise corporate strategies, and execute entire organisational tasks. If that happens, will we humans simply accept AI’s decisions and become subservient to AI?
The framework we have developed, which integrates qualitative and quantitative data, should serve as an interface for humans and AI to collaborate on equal footing.
The Basic Principles of ConceptMiner<\/a><\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

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News & Information<\/h2>\n\n\n