Insights for decision-makers
Understand AI.
Make Clearer Decisions.
Analysis, decision models, and practical knowledge for companies that want to do more than just talk about AI and actually use it effectively.
What matters in AI projects
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What needs to be clarified before AI becomes productive?
Database, process benefits, risks, and the next logical step: We'll assess what's truly relevant for a sound decision.
Latest Posts
Decision support with concrete work models.
Vom Lastenheft zur Rückfragenliste: Unvollständige Kundenanfragen systematisch klären
Eine praktische Methode, um fehlende Angaben, Widersprüche und Varianten vor dem Angebotsentwurf in klare Rückfragen zu übersetzen.
Product Knowledge in the Quotation Process: When RAG Helps – and When Search Is Enough
A decision guide to structured product data, conventional search and RAG in technical sales.
Knowledge Base for Sales and Engineering: Which Content to Prioritise First
Six practical areas to review when creating a focused, professionally maintained initial knowledge set for sales and engineering.
Data Inventory Before Quotation Automation: 8 Areas to Review in the Workshop
Eight practical review areas reveal which data, documents, roles and handovers should be clarified before an automation pilot. A practical Trixner checklist for the workshop.
Automating Technical Customer Enquiries: From Email to a Verifiable Quotation Draft
Technical enquiries rarely arrive complete and neatly structured. A reliable process connects documents, product knowledge and existing systems, producing a verifiable draft rather than an autonomous quotation.
AI Act Since 2 August 2026: What Companies Need to Review Now
Further parts of the AI Act have applied since 2 August 2026. Companies now need a complete AI inventory, clear roles, appropriate transparency, AI literacy and documented approvals.
How to Plan a RAG Pilot: Scope, Test Questions and Success Criteria
A RAG pilot should not be a large platform in miniature. With clear test questions, it should show whether the data, retrieval and answers genuinely work for a specific business task.
What Data Really Makes a Real Estate Lead Qualified
It's not the sheer volume of data that makes a real estate lead valuable, but the right data. What information truly benefits sales and potential buyers.
AI Agent or Classic Automation? A Decision Matrix for Companies
Not every process needs an AI agent. Often, simple automation is actually the better solution. A practical decision-making guide—without any technical jargon.
Next Step
Should a topic be translated directly into a project?
Then let's not start with theory, but with the initial situation, data sources, process benefits, and a realistic pilot scope.