{"id":633,"date":"2026-07-04T15:32:37","date_gmt":"2026-07-04T06:32:37","guid":{"rendered":"https:\/\/conceptminer.ai\/?page_id=633"},"modified":"2026-07-04T16:16:18","modified_gmt":"2026-07-04T07:16:18","slug":"product","status":"publish","type":"page","link":"https:\/\/conceptminer.ai\/?page_id=633&lang=en","title":{"rendered":"Products"},"content":{"rendered":"\n
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ConceptMiner Products<\/div>\n

Concept-centered AI products for knowledge, data, and decision-making.<\/h1>\n

\n Prompt engineering alone is not enough to establish a stable shared understanding between humans and AI.\n ConceptMiner provides a product family built around conceptual structural modeling \u2014 an epistemological approach\n for organizing business data, knowledge, and operational experience.\n <\/p>\n

\n View Products<\/a>\n Contact Us<\/a>\n <\/div>\n

\n From concept indexing and natural-language data extraction to knowledge base construction and thought-support AI.\n <\/p>\n <\/div>\n\n

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DB<\/div>\n
\n Business Data<\/strong>\n RDB, text records, documents, operational data<\/span>\n <\/div>\n <\/div>\n
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MAP<\/div>\n
\n Conceptual Modeling<\/strong>\n Concept nodes, clusters, pathways, segments<\/span>\n <\/div>\n <\/div>\n
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KB<\/div>\n
\n Knowledge Base<\/strong>\n LLM Wiki, document-level answers, integrated knowledge<\/span>\n <\/div>\n <\/div>\n
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AI<\/div>\n
\n AI Interface<\/strong>\n Concierge, query, research, and thought support<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
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Why ConceptMiner<\/div>\n

AI needs more than prompts. It needs a shared conceptual structure.<\/h2>\n

\n When humans and AI work together, relying only on prompts often leads to unstable interpretation.\n On the other hand, ontology networks can become too rigid and overly complex.\n ConceptMiner takes a different approach: it builds concept-centered models that help AI systems\n understand, organize, and navigate business information.\n <\/p>\n <\/div>\n\n

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\n Beyond prompt-only interaction<\/strong>\n Prompts are useful, but they do not by themselves create a durable shared understanding.<\/span>\n <\/div>\n
\n Beyond rigid ontology networks<\/strong>\n ConceptMiner avoids overly complex manually designed knowledge structures.<\/span>\n <\/div>\n
\n Conceptual structural modeling<\/strong>\n Business data, documents, and experiences are organized as navigable conceptual structures.<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
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Product Lineup<\/div>\n

Services provided by ConceptMiner<\/h2>\n

\n ConceptMiner products can be used individually or combined as a broader AI knowledge infrastructure.\n <\/p>\n <\/div>\n\n

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\ud83c\udd95<\/div>\n

ConceptMiner Concept Index<\/h3>\n

\n Enables similarity search using conventional databases such as PostgreSQL, without requiring a separate\n vector database or graph database. It supports the integrated handling of qualitative information,\n such as natural-language text, and quantitative data, such as numerical values and categories.\n <\/p>\n

\n Learn More<\/a>\n <\/div>\n <\/div>\n\n
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\ud83d\udcac<\/div>\n

ConceptMiner Concept Query<\/h3>\n

\n An add-on feature for Concept Index that enables complex data extraction through natural-language instructions.\n Because data is pre-classified into conceptual segments, the system can quickly execute complex extraction tasks.\n <\/p>\n

\n Examples<\/a>\n <\/div>\n <\/div>\n\n
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\ud83e\udde0<\/div>\n

ConceptMiner Episode Memory<\/h3>\n

\n A future product concept for recording operational decisions, situations, actions, and outcomes in card format.\n By modeling these episodes conceptually, it will support better decisions in similar future contexts.\n <\/p>\n <\/div>\n\n

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\ud83d\udcda<\/div>\n

Knowledge Base Builder<\/h3>\n

\n Builds knowledge base units from internal documents by creating summarized LLM Wiki content.\n These units can provide precise answers based on the content of each document and can be integrated\n through ConceptMiner\u2019s conceptual structure model.\n <\/p>\n <\/div>\n\n

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\ud83e\udd16<\/div>\n

Concierge Base Builder<\/h3>\n

\n An entry-level product derived from Knowledge Base Builder. It isolates the component that builds\n simple knowledge base units for chatbot use. It does not use the conceptual structure model.\n <\/p>\n <\/div>\n\n

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\ud83d\uddfa\ufe0f<\/div>\n

ConceptMiner Concept Map<\/h3>\n

\n A data mining tool using ConceptMiner\u2019s core conceptual structural modeling technology.\n It enables integrated mining of qualitative and quantitative data, even for users without advanced\n data science expertise.\n <\/p>\n <\/div>\n\n

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\ud83d\udd0e<\/div>\n

ConceptMiner Auto Research<\/h3>\n

\n Uses AI to gather information such as competitor products, services, sensory experiences,\n news articles, research abstracts, and patent data in order to generate ideas and prepare data\n for conceptual structural modeling.\n <\/p>\n <\/div>\n\n

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\ud83e\udded<\/div>\n

ConceptMiner ThinkNavi<\/h3>\n

\n An AI chat service focused on thought support and problem-solving. By constructing conceptual structure\n models from chat history, it helps users rediscover and combine concepts buried in past conversations.\n <\/p>\n <\/div>\n\n

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\u2699\ufe0f<\/div>\n

Integrated AI Knowledge Infrastructure<\/h3>\n

\n The ConceptMiner product family can evolve from simple database extension and knowledge base construction\n toward a broader infrastructure for AI-assisted decision-making, research, and knowledge navigation.\n <\/p>\n <\/div>\n\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Concept Query<\/div>\n

Natural-language instructions for conceptual data extraction.<\/h2>\n

\n Concept Query is designed to extract business segments that are difficult to define with ordinary SQL conditions alone.\n It works by using conceptual segments prepared in advance by Concept Index.\n <\/p>\n <\/div>\n\n

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Highly profitable but high churn risk<\/h3>\n

\n Extract customer groups that appear valuable but require careful retention strategies.\n <\/p>\n <\/div>\n

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Low price sensitivity and premium preference<\/h3>\n

\n Identify groups that are more likely to respond to high-value or premium offerings.\n <\/p>\n <\/div>\n

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High inquiry volume but high satisfaction<\/h3>\n

\n Discover segments where frequent contact does not necessarily indicate dissatisfaction.\n <\/p>\n <\/div>\n

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Responsive to new products but low retention<\/h3>\n

\n Find groups that are active and curious, but may require different follow-up strategies.\n <\/p>\n <\/div>\n

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Small niche group with high sales contribution<\/h3>\n

\n Surface valuable minority segments that may be hidden by conventional aggregation.\n <\/p>\n <\/div>\n

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Conceptual segments, not just SQL generation<\/h3>\n

\n The value of Concept Query is not merely that AI writes SQL, but that the underlying data has already\n been organized into meaningful conceptual groups.\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Product Architecture<\/div>\n

From data indexing to knowledge navigation.<\/h2>\n

\n ConceptMiner products can be understood as layers that gradually expand what AI can do with business information.\n <\/p>\n <\/div>\n\n

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\n Data Layer<\/b>\n RDB, text records, documents, numerical and categorical data<\/small>\n <\/div>\n
\n Concept Layer<\/b>\n Concept Index, Concept Map, conceptual structural modeling<\/small>\n <\/div>\n
\n Knowledge Layer<\/b>\n LLM Wiki, document summaries, integrated knowledge bases<\/small>\n <\/div>\n
\n Interaction Layer<\/b>\n Concept Query, Concierge, ThinkNavi chat interface<\/small>\n <\/div>\n
\n Decision Layer<\/b>\n Episode Memory, operational learning, future automation agents<\/small>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
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Positioning<\/div>\n

ConceptMiner is not just another chatbot product.<\/h2>\n

\n Many AI products focus on generating responses from prompts. ConceptMiner focuses on the structure behind the response:\n how data, documents, experiences, and ideas are organized into concepts that humans and AI can navigate together.\n This makes it suitable for applications where knowledge, classification, similarity, and decision context matter.\n <\/p>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Recommended Entry Points<\/div>\n

Choose the product according to your first use case.<\/h2>\n

\n If you are not sure where to start, the following entry points can help clarify the best product path.\n <\/p>\n <\/div>\n\n \n \n \n \n \n \n \n \n \n \n \n
Use Case<\/th>\n Recommended Product<\/th>\n Main Value<\/th>\n <\/tr>\n <\/thead>\n
Extract similar groups from free-text business data<\/td>\n Concept Index<\/span><\/td>\n Add conceptual similarity search to an existing database.<\/td>\n <\/tr>\n
Use natural language to extract complex business segments<\/td>\n Concept Query<\/span><\/td>\n Query pre-classified conceptual segments with natural-language instructions.<\/td>\n <\/tr>\n
Build a knowledge base from internal documents<\/td>\n Knowledge Base Builder<\/span><\/td>\n Create LLM Wiki units that answer based on specific document content.<\/td>\n <\/tr>\n
Add a simple chatbot to a website or service<\/td>\n Concierge Base Builder<\/span><\/td>\n Build an entry-level knowledge base for chatbot responses.<\/td>\n <\/tr>\n
Explore qualitative and quantitative data together<\/td>\n Concept Map<\/span><\/td>\n Generate strategies and concepts from integrated data mining.<\/td>\n <\/tr>\n
Collect information for research and modeling<\/td>\n Auto Research<\/span><\/td>\n Gather and prepare external information for conceptual modeling.<\/td>\n <\/tr>\n
Support thinking and problem-solving through chat history<\/td>\n ThinkNavi<\/span><\/td>\n Use past conversations as a conceptual memory for new ideas.<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n <\/section>\n\n\n
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Build AI systems around concepts, not just prompts.<\/h2>\n

\n ConceptMiner provides a concept-centered foundation for business data, knowledge bases,\n research, and AI-assisted decision-making. Contact us to discuss which product best fits your use case.\n <\/p>\n

\n Contact Us<\/a>\n Back to Home<\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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