The system in which humans and AI collaborate to create strategies and new concepts　　　

FactCollector

Qualitative research
using AI
Competitive products/services
Sensory evaluation
Online articles
Academic papers
Patent information
obsidian markdown

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ConcpetMap-Text

Conceptual space mapping
Text data obtained from qualitative research is vectorised and used to construct a conceptual network model using our unique ConceptMap technology, providing a foundation for conceptual analysis.

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ConceptMap-Data

Data Mining
ConceptMap technology is applied to conventional numerical and categorical data. This enables the ultimate in information analysis by combining qualitative research and quantitative data. (comming soon)

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CreativeDiagram

Creation of new concepts
Define the position on the ConceptMap as a strategic domain and infer the concept using AI. Furthermore, generate systematic texts such as reports and manuals (Comming soon).

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Integrating qualitative research and quantitative data analysis to provide a framework for collaboration between humans and AI

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

  • 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,…