Add similarity search to your existing RDB.

ConceptMiner Concept Index
Scheduled for release in August 2026

ConceptMiner Concept Index adds similarity search capabilities to existing relational databases such as PostgreSQL. Without introducing a new vector database or graph database, it enables similarity search over business data that includes text columns.

It can be used to extract semantically similar groups from data such as VoC records, inquiry histories, open-ended survey responses, sales notes, and case summaries.

Note

ConceptMiner Concept Index is designed for extracting semantically similar data groups and conceptual segments.
It is not intended primarily for pinpoint document retrieval or question-answering use cases like RAG.

For building an inquiry-response system or a knowledge-base chatbot, please use ThinkNavi Knowledge Base Builder.

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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,…