Relying solely on prompts has its limits when it comes to establishing a shared understanding between humans and AI to prevent the AI from going out of control. Ontology networks also tend to become overly complex. What is needed, therefore, is an epistemological approach—specifically, conceptual modeling.
Services provided by ConceptMiner
■ ConceptMiner Concept Index （New）
It enables similarity search using only conventional databases like PostgreSQL, without the need for vector or graph databases. It allows for the integrated handling of qualitative information (natural language text) and quantitative data (numerical values and categories). It operates as an extension to existing databases.
■ ConceptMiner Concept query （Add on）
This is an additional feature of the ConceptMiner Concept Index that enables complex data extraction through natural language instructions. For example,
- A customer segment that is highly profitable but carries a high risk of churn
- A group with low price sensitivity and a strong preference for premium products
- A segment that generates a high volume of inquiries but also reports high satisfaction
- A group that responds readily to new products but has a low retention rate
- A niche customer group that is small in number but contributes significantly to sales
Simply giving instructions using expressions like these allows you to extract the appropriate data. This is not just because the AI automatically writes SQL, but because the data is pre-classified into conceptual segments, enabling it to quickly execute complex processes.
■ ConceptMiner Episode Memory （Future Plan）
By recording details of various operational decisions—including the situation, the decision itself, and the outcome—in a card format and constructing a conceptual structural model, the system supports optimal decision-making in similar contexts. Plans are also in place to integrate this with business automation agents.
■ ConceptMiner Knowledge Base Builder
By creating summarized versions (LLM Wiki) from internal documents, you can build knowledge base units capable of providing precise answers based on the content of each specific document. Furthermore, by integrating these units using ConceptMiner’s conceptual structure model, you can construct a comprehensive, cross-functional knowledge base that addresses even the finest details. This system is ideal for use with internal operational manuals and training programs.
■ ConceptMiner Concierge Base Builder
This is an entry-level product derived from the ConceptMiner Knowledge Base Builder, isolating only the component that constructs knowledge base units for use in simple chatbots. It does not utilize a conceptual structure model.
■ ConceptMiner Concept Map
This product is a tool for data mining that utilizes “conceptual structural modeling”—ConceptMiner’s core capability. It enables integrated mining of qualitative information (natural language text) and quantitative data (numerical and categorical values). Even without advanced data science expertise, users can generate new strategies and concepts based on models that the AI itself explains.
■ ConceptMiner Auto Research
This product enables you to use AI to generate ideas and prepare data for constructing conceptual structural models by gathering information such as competitor products and services, sensory experiences of food and beverages, news articles, research paper abstracts, and patent data.
■ ConceptMiner ThinkNavi
An AI chat service focused on supporting thought processes and problem-solving. By constructing conceptual structure models from chat history, it enables the creation of new ideas by combining concepts that had been buried and forgotten within past conversations.