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.
A layer-by-layer walk through an enterprise reference architecture for agentic AI — from the front door of channels, identity and gateways to running agents safely at scale.
Future series will cover cloud architecture on AWS and Azure, AI platforms such as Amazon Bedrock and Azure AI Foundry, application architecture, databases, ML platform engineering, and more — each taken 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?