Real projects show what dependable AI solutions actually require.
These insights make tangible how existing knowledge, structured data and clear operational logic turn into solutions that stand up in real-world use.
References, practical insights & real implementations
Blog & References
Public references, technical insights and grounded project experience around the AI-Concierge. The focus is on real implementations that show how content, product knowledge, data sources and service processes can be structured so AI becomes understandable and genuinely useful in day-to-day use.
AI projects need more than a single tool
AI projects do not emerge from one tool alone. They depend on a clear combination of goal definition, knowledge structure, technical implementation and ongoing alignment. Together with our customers, we define which information can be used, which tasks the AI-Concierge may take on and where clear boundaries or handoffs are needed. This is how solutions become technically reliable, understandable and meaningful in the context where they are used.
- Goals, responsibilities and limits are clarified early.
- Sources, data access and approval logic remain traceable.
- Integrations are evaluated by practical value, not by tool promises.
- Operations, data protection and handoffs are considered from the start.
What makes references genuinely useful
Project insights become valuable when they make the starting point, implementation logic and practical limits of a system easy to understand.
A concrete starting point
Which task actually needed to be solved and why generic answers would not have been enough.
Traceable implementation
Which data, content structures, interfaces or approval rules mattered for a reliable solution.
Transferable value
Which learnings can also help other organisations or similar use cases make better decisions.
Selected references
Project examples from real implementations
The following references highlight different application areas of the AI-Concierge: tourism information logic, large content landscapes and product search in complex assortments.
Reference
Tourism
Tourism data
Alpbachtal: Making tourism knowledge available where guests actually ask for it
This reference shows how an existing AI-Concierge was extended with faceted search so tourism content can be used not only in webchat, but also in voice setups, hotel channels and other digital guest services.
Focus: Faceted search, structured entries and a reusable knowledge layer
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Reference
Tourism
Knowledge access
Lower Austria Tourism: Reorganising access to a large tourism content landscape
Instead of creating a new content world, this project focused on giving visitors a more direct access layer to existing knowledge and making it usable in the right regional context.
Focus: Regional context logic, a large knowledge base and iterative quality improvement
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Reference
eCommerce
Product search
ADLER: Finding products even when the exact name is missing
This reference shows how product knowledge, Pimcore data and search intent can be connected so users can reach suitable products through use case, material or problem statement.
Focus: B2C/B2B orientation, search quality and natural-language product search
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