INSIGHTS.
Technical perspectives, architecture guides, and whitepapers on AI deployment and federal strategy.
GUIDES & WHITEPAPERS.
Downloadable resources and in-depth guides on our approach to secure AI.
Deploying LLMs in Air-Gapped Government Environments
A practical architecture guide for running large language models in environments with zero external connectivity — from model packaging to offline vector stores and guardrails.
Coming SoonAAOM™: A Governance Framework for Enterprise AI
47 controls across 9 domains covering the full AI lifecycle. How to implement responsible AI governance that satisfies both compliance requirements and operational reality.
Read more →Zero Trust Architecture for AI Services
Applying NIST 800-207 Zero Trust principles to AI inference pipelines, model registries, and vector databases. Microsegmentation patterns for ML workloads.
Read more →AegisAI™: Secure AI Infrastructure for Government
Product brief covering GPU-accelerated inference, compliance-first design, flexible deployment models, and the architecture behind AegisAI™.
Read more →PERSPECTIVES.
Short-form analysis on trends, technology, and strategy.
Why Most AI Projects Fail in Government
The gap between AI experimentation and operational deployment in federal environments is wider than most realize. Here is what actually goes wrong — and how to fix it.
Coming SoonRAG vs Fine-Tuning: What Actually Works
Two approaches to grounding LLMs in organizational knowledge. One works for most enterprise use cases. The other is usually overkill.
Coming SoonAI + Cloud + Security: The Real Integration Challenge
Deploying AI in regulated environments requires more than a model and an API. It requires infrastructure, governance, and security from day one.
Coming SoonHow Primes Can Win More AI Contracts
Most primes lack in-house AI depth for proposals. The ones winning AI task orders are teaming with small businesses that have real delivery experience.
Coming SoonCapability Statement
One-page overview of our capabilities, NAICS codes, certifications, past performance, and leadership.
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