VajraNex Knowledge Solutions is a working library of engineering deep-dives on AI systems, infrastructure, and software architecture — written from the point where the demo works and the real project actually begins.
A phase-by-phase series on turning a retrieval-augmented-generation proof of concept into a system that survives real data, real users, and real latency budgets.
Future series will cover agentic systems, ML platform engineering, and other AI and software topics as they move from requirement to production.
Does the design still hold at 100x the volume and continuous change, not just at demo scale?
What happens when a source, a service, or a model call fails — does the system degrade gracefully?
What does this actually cost per unit of work at production volume, not proof-of-concept volume?