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.　

　

ConceptMiner Cloud

Concept network engine for developing AI apps

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.

ConceptMiner Local

Security-enhanced Concept Network Engine

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.

Enterprise Local Edition is here

This plays a part in AI governance, providing a foundation for human-AI collaboration.

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

News & Information

  • Emergency Announcement: Introducing ConceptMiner Enterprise Local
    It has been announced that Viscovery Software GmbH, a long-established provider of data mining systems based on self-organizing maps (SOMs) since 1994, will be closing down. SOM is one of the well-known data mining techniques, and although open source and commercial software exist, most of them are incompletely implemented and are insufficient for practical data…
  • ConceptMiner Competitive Analysis with GPT-5
    Authough ConceptMiner offers competitive analysis that integrates qualitative and quantitative data, so we ran a competitive analysis of ConceptMiner itself using GPT-5 to see what happens: ConceptMiner’s Competitive Positioning: Standing Out in the Knowledge Discovery Landscape In the fast-evolving world of AI-driven knowledge discovery, visualization, and analysis, the competitive field is becoming increasingly crowded. From…
  • Added machine learning software to shared models page
    Added a Machine Learning Software Competition Map to the Shared Models page.
  • About the launch of the service
    ConceptMiner currently has two web application modules: FactCollector and ConceptMap-Text. These modules were developed using Python+Streamlit. However, in order to provide paid services, licence management, payment processing, and external storage are required, which we are developing as a hub system using Next.js+Stripe API+CloudeFlare R2. By integrating the Hub system and web application modules via API,…