Insights
Practical thinking for teams building AI beyond the demo.
Perspectives on agentic systems, generative AI, data foundations, evaluation and the operating decisions behind useful AI products.
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Notes from the applied side of AI.
Agentic AI
What agentic AI actually changes inside a business
Agentic AI is most useful when the problem is not just generating an answer, but coordinating a sequence of decisions and tool-based actions.
Generative AI
From chatbot to knowledge system: designing better RAG products
Reliable enterprise generation depends less on clever prompting and more on retrieval quality, evaluation, permissions and product design.
Data Strategy
Why AI projects fail when the data foundation is ignored
The fastest route to a disappointing AI initiative is to treat data quality, ownership and semantics as a problem for later.
Start with the problem
Have an AI question connected to a real business problem?
We are happy to discuss the practical architecture, data and operating choices behind it.