AI Intel Brief

The 1M context window is a game-changer for document-heavy government workflows. An entire OPORD, all annexes, and supporting intelligence products fit in a single context. But it's still a cloud API — the real win is when these capabilities run on local hardware. We're watching the open-source models close the gap.

The trickle-down matters more than the flagship. When data center chips get 2.5x faster, last year's consumer cards (RTX 5070, 5080) become the new floor for edge AI. We're already training and running 3B parameter models on an RTX 5070 laptop. Two generations from now, 7B models will run on hardware that fits in a cargo pocket. That's the trend line for deployable defense AI.

Multi-agent is going mainstream. The big players are validating what we've been building: specialized agents that each do one thing well, chained together for complex analysis. The difference is we don't need Azure. Our agents run on a laptop, offline, with zero cloud dependency. When the market catches up to multi-agent, we'll already be deployed where the cloud can't reach.

Three of our core capabilities are in their focus areas: autonomous decision support (our military swarms), edge deployment (our entire architecture), and contested logistics (our data integrity system). This is the kind of solicitation where being able to demo on a laptop — not a slide deck — is the differentiator. Working system beats PowerPoint.

We did exactly this today. Fine-tuned a 3B model on military intelligence doctrine using QLoRA on a laptop RTX 5070. 37 training examples, 16 minutes, and the model went from generic AI responses to producing doctrinal SALUTE evaluations and threat assessments that use proper military terminology. The barrier to domain-specific AI just dropped to zero. Any organization with a laptop and subject matter expertise can build their own specialized model.

This is validation of our core design philosophy. "The AI said so" has never been an acceptable answer in defense. Our graph-based reasoning is inherently explainable — every conclusion traces to specific nodes and edges in a knowledge graph. Every path is auditable. Every result is reproducible. The new guidelines don't require us to change anything. They require everyone else to catch up.

$10B in revenue means $10B flowing to cloud APIs. Every dollar of that is a recurring cost that scales with usage. Our approach is the opposite: knowledge graphs and local models that cost $0 per query after initial deployment. At government scale — millions of queries across thousands of users — the cost difference between per-query API pricing and a local system is the difference between a sustainable program and one that gets defunded in year two.

This is the future of deployable AI. When a 7B model runs at conversational speed on a standard laptop without a discrete GPU, the hardware barrier for defense AI deployment disappears entirely. Our current system runs a 3B model on a laptop GPU. In 12-18 months, we'll run a 7B model on the CPU alone. Every military laptop becomes an AI-capable platform. No procurement action needed.