Technology & Architecture
Architecture for connected product knowledge
PANTOPIX SPHERE connects systems, semantically links product information and orchestrates data flows via a central platform.
Semantic integration
A central knowledge layer between systems and applications
PANTOPIX SPHERE is an intelligent knowledge platform that links technical information from different systems in a central knowledge graph and makes it usable.
Connectors are used to integrate data from existing systems such as CCMS, ERP or PIM and correlate them using semantic models.
Continuous data flows ensure that information is synchronized, enriched and kept up to date in both directions. In this way, the knowledge base grows with the systems and continuously reflects changes.
On this basis, information can be published consistently in applications, portals and AI systems.
Technical strengths
Why PANTOPIX SPHERE is replacing traditional IT architectures
PANTOPIX SPHERE reduces the complexity of distributed system landscapes and creates a central knowledge base for data and applications. Semantic structuring, continuous data flows and standardized interfaces create a scalable, future-proof architecture.
Central knowledge layer
Semantic consistency
Standardized interfaces
Automated data processing
Context-based AI (GraphRAG)
Core components of PANTOPIX SPHERE
Central components of the knowledge platform
Knowledge graph and knowledge model (ontology)
Connectors for the integration of source systems
Data catalog for transparency and governance
Automatic data processing and orchestration
AI chat as access to the knowledge graph
Core components of PANTOPIX SPHERE
Central components of the knowledge platform
Knowledge graph and knowledge model (ontology)
The Knowledge Graph forms the central knowledge base in which information is semantically structured, linked and mapped as a consistent knowledge model.
Connectors for the integration of source systems
Connectors enable the integration of data from existing systems such as CCMS, ERP or PIM and connect information sources across system boundaries.
Data catalog for transparency and governance
The data catalog creates transparency about data sources, structures and entities. It forms the basis for the controlled and traceable use of information.
Automated data processing and orchestration
Data flows, transformations and enrichments are controlled automatically so that product information is continuously processed, synchronized and kept up to date.
AI chat as access to the knowledge graph
AI-supported access enables natural querying of the central knowledge base and provides contextualized, comprehensible answers.
Automated processes
Central functions of PANTOPIX SPHERE for data flows, metadata and AI access
01 metaSelect for intelligent metadata management
metaSelect enables the distribution of the knowledge model to all systems in which information is generated or used.
- Distributes the knowledge model to all relevant systems
- Manages dynamic API endpoints per user
- Supports the direct processing of SPARQL, XSL or Jq queries
- Enriches the knowledge graph with metadata for linking information units
- Allows you to apply centrally managed metadata to topics in your CCMS
- Supports the automatic filling of facets in a document portal
- Makes AI outputs reliable using metadata
02 dataFlow for automated data flows
With dataFlow, structured and unstructured data can be automatically exchanged between systems, even in large quantities. The associated data repositories are cloud-capable and guarantee high availability and reliability as well as scalability and version control.
- Provides automated process chains for data transfer and data transformation
- Organizes the data flow and ensures efficient processing and transmission between source and destination
- Executes XSL, Python, task files or any dockerized programs
- Supports scheduled, on-demand or event-based execution
- Provides data where it is needed in the required format
03 dataEnrich for automated data enrichment
dataEnrich enriches existing data with metadata or extracts information from this data. Using advanced NLP techniques such as text recognition, entity recognition and text classification, information is extracted and integrated into the knowledge graph, while the knowledge model is continuously curated and expanded.
- Transforms unstructured data into machine-readable information
- Assigns information units to the company-specific knowledge model using taxonomies
- Supports the connection to Azure AI Document Intelligence or Google Document AI
04 chatAssist for intuitive access to knowledge
chatAssist is the AI chat with GraphRAG technology. It combines generative AI with structured knowledge management and thus creates reliable access to company-wide product knowledge. The linking of language models with taxonomies and graph data improves the technical context and increases the relevance and traceability of the answers.
- Enables dialog-oriented access to company-wide product knowledge
- Supports the connection to Azure OpenAI, Mistral or Google Gemini
- Maximizes the reliability of answers through the integration of taxonomies and graph data
- Provides source information for transparency and traceability
Data sources and integration
From data sources to applications via a central knowledge base
PANTOPIX SPHERE integrates information from existing systems via connectors and combines them in a central knowledge base. This allows data to be provided consistently in different applications and forms the basis for reliable AI.
Where the knowledge comes from:
From content delivery portals
From content management systems such as a CCMS
From enterprise systems such as ERP, SPC, PIM or PLM
From databases and existing knowledge sources
From ontologies, vocabularies and semantic models
Where the knowledge is used:
In content delivery portals and website
In custom apps and digital applications
In product navigators or information systems
In AI chatbots and agentic AI applications
Read all use cases for connected technical information here.
Integrated open standards
Our approach
Step by step
to PANTOPIX SPHERE
Start with initial use cases and expand the platform step by step. From the idea to technical validation and a scalable solution, this creates a sustainable basis for your applications and AI.
Consulting
Together, we analyze your initial situation and identify specific use cases. In doing so, we focus on rapid added value and a clear objective for the use of PANTOPIX SPHERE.
Proof of concept
In the proof of concept, we show the technical feasibility and demonstrate how your data can be converted into connected knowledge. In this way, we create a reliable basis for decision-making for the next steps.
MVP & Scale-Up
The first productive use case is created with a minimum viable product (MVP). Building on this, PANTOPIX SPHERE is gradually expanded and rolled out to other use cases and areas.
Contact person
Our expert for PANTOPIX SPHERE
Maximilian Gärber
FAQ
Frequently asked questions about PANTOPIX SPHERE
Which systems/data sources can be connected?
What components does PANTOPIX SPHERE include?
Which modules belong to PANTOPIX SPHERE and what they are used for
What are the requirements for using PANTOPIX SPHERE for me as a customer?
How does the knowledge model in PANTOPIX SPHERE work?
How do I keep the information in PANTOPIX SPHERE up to date?
How are data security and data protection guaranteed in PANTOPIX SPHERE?
How is PANTOPIX SPHERE introduced?
What does PANTOPIX SPHERE cost?
Are support and maintenance included in the price?
Your question is not listed?
We are happy to answer all open questions personally and without obligation.