Deep Dives into AI Architecture & Engineering
A guide to sizing hardware and architecting networks when cloud APIs are not an option. Focuses on GPU optimization and VRAM constraints for local inference.
A blueprint for modern software engineering without internet access. Covers "Sneakernet" strategies, local PyPI mirrors, and containerization for Zero Trust.
How to build "Chat with your Data" pipelines using local Vector DBs and embedding models, ensuring 100% data sovereignty with no external egress.
Moving from "vibe-based" AI to deterministic, auditable systems. Techniques for citation forcing and verifiable output in compliance-heavy sectors.
A reference architecture for deploying LangGraph-style multi-agent workflows inside air-gapped networks. Covers sandboxed tool execution and encrypted state management.
Moving from 'Prompt Engineering' to 'AI Engineering'. A practical guide to building verifiable, metrics-driven educational AI for enterprise training.
Exploration of combining deep learning's pattern recognition with symbolic logic to create hyper-personalized, verifiable learning paths.
Case studies on transitioning to DSPy pipelines: Achieving 99% accuracy in clinical extraction and 0.92 human-alignment in subjective essay grading.
This research series addresses the unique challenges of deploying AI systems in air-gapped, classified, and compliance-heavy environments. Drawing from real-world experience architecting solutions for defense, intelligence, and regulated industries, these whitepapers provide actionable frameworks for organizations that cannot rely on cloud-based AI services.