Engineering Insights
Practical thinking on production AI, compliance engineering, and building systems that scale — from engineers who build them daily.
Why Most RAG Systems Fail in Production (And How to Build One That Does Not)
Most RAG implementations work in demos and collapse under real load. This article covers the architecture decisions that separate production RAG systems from proof of concepts — vector database selection, chunking strategy, retrieval accuracy, and latency at scale.
Building HIPAA-Compliant AI Pipelines on AWS
HIPAA compliance is not a checkbox — it is an architecture decision. This guide covers encryption, audit trails, access controls, and BAA requirements for AI systems handling protected health information.
AWS Textract in Production: What Nobody Tells You
Textract works well in demos. In production with complex layouts, multi-column PDFs, and handwritten sections, it needs careful configuration. Here is what we learned processing 500+ contracts monthly.
Java vs Python for Production AI Systems: An Honest Comparison
Python dominates AI development. Java dominates enterprise production. Here is when to use each, how to combine them effectively, and why your Spring Boot backend and Python ML models can coexist cleanly.
CJIS Compliance in Cloud Deployments: A Technical Guide
CJIS requirements create real constraints on cloud architecture — data residency, encryption standards, audit logging, and personnel screening. This guide covers what the policy actually requires technically.
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