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Research & Education

Practical AI engineering labs and courses on the open DBCV platform — for students, teachers and universities.

We develop practical labs and courses in AI engineering on the open DBCV platform. The idea is simple: a student builds a small but complete system — with state, error handling and handoff to a human — instead of learning to “write prompts”. These are the same engineering practices as in production, at teaching scale.

For students

Labs and project practice: AI workflows, orchestration, knowledge retrieval (RAG) and context work, integrations, hybrid model routing. The output is not a demo but a system you can explain and debug.

For teachers

Ready teaching scenarios, a sandbox, methodical materials, worked cases and datasets. A course can be run without rebuilding infrastructure from scratch each semester.

For universities

Joint labs, research and pilot programs, modernization of AI curricula. The open-source platform is suitable for modification, research and theses — with no licensing limits on the core part.

Why it works

Because teaching is done not on toy examples but on the same event-driven and multi-agent systems used in production. The student leaves not with “I called a model” but with an understanding of what a system you can trust with real work is made of.

Next step

Let's design an AI-native automation layer for your operations.

DBCV