Whitepapers
Research and analysis for defense decision-makers.
Technical depth on the operational, architectural, and acquisition questions behind edge AI, mesh ISR, and tactical device management.
The cost of battlefield AI: why localized compute on consumer hardware is the only model that scales
Cloud AI inference costs are rising 30-40% per model generation. Dedicated GPU hardware for the edge costs $15-25K per node with 6-month procurement timelines. Meanwhile Apple is shipping on-device AI in every phone and laptop for $1,299. The military that figures out how to leverage localized consumer AI compute in a coordinated mesh will own the next fight.
Tactical device management: turning 200,000 consumer devices into managed mission nodes
CMMC 2.0 requires 110 security controls across 200,000+ defense industrial base companies. NIST SP 800-124r2 separates MDM from mobile threat defense. No integrated solution handles multi-classification enforcement on consumer hardware with tactical features like RF suppression and duress wipe. This paper examines the gap and how EdgeLance closes it.
AI model governance at the tactical edge: provenance, loadouts, and the auditability gap
DoD adopted five AI ethical principles in 2020. The RAI Toolkit mandates explainability for high-risk decisions. Then the Pentagon banned Anthropic from Maven and forced Palantir to rip out its core AI engine in 180 days. The gap between AI governance policy and operational tooling is where missions fail and legal reviews stall. This paper proposes a field-deployable model governance architecture.
Bottom-up ISR: why the next generation of tactical intelligence starts at the squad
DARPA's MOSAIC warfare concept, Ukraine's decentralized innovation model, and the FY26 budget all point in the same direction: pushing AI, sensors, and decision support lower in the force structure. This paper examines how EdgeLance enables ISR capability at the company level and below without enterprise infrastructure.
Edge AI in contested spectrum: operational requirements for inference under EW denial
Chinese EW installations across the Spratly Islands, Russian GPS denial reducing precision weapon effectiveness by up to 90%, and Iranian autonomous drone production scaling 10x. This paper analyzes how electromagnetic threats drive the requirement for local inference and what that means for system architecture.
Acquisition pathways for edge AI platforms: OTAs, SWP, and the Barrier Removal Board
The March 2025 Hegseth software acquisition memo, the November 2025 acquisition transformation strategy, and DIU's 500+ OTA track record create a procurement environment built for platforms like EdgeLance. This paper maps the acquisition landscape for nontraditional defense AI vendors.