Blog
Edge AI, mesh, and the future of tactical ISR.
DARPA's MOSAIC concept needs a node-level operating layer. Nobody has built it yet.
MOSAIC warfare replaces monolithic platforms with modular nodes composed via AI networks. DARPA's 2026 RFI requests autonomous drone constellations with edge-based computing and collaborative multi-agent operations. The concept is clear. The node-level software layer that makes it work for ISR at the company level does not exist in the current vendor ecosystem.
Chinese EW in the South China Sea already broke your cloud AI architecture
Six paved antenna sites at Mischief Reef. Five vehicle-mounted jammers at Subi Reef. GPS denial across four bodies of water. Russian EW in Ukraine cut precision weapon effectiveness by 90%. Any AI system that sends queries to the cloud is a system the adversary can disable by attacking the link.
The defense AI market is moving toward the tactical edge
Program offices are asking for AI that works closer to the sensor, closer to the operator, and with less dependence on perfect connectivity. That creates a gap for platforms built around local inference, mesh routing, and managed COTS hardware.
The ISR gap below the enterprise layer
Enterprise C2 and ISR platforms serve large programs well. The open space is lower in the formation: teams that need useful AI, mesh, cameras, and device control without dedicated infrastructure or a large program office.
What Ukraine taught NATO about consumer hardware in combat
Recent conflicts have shown how quickly commercial devices, drones, apps, and compute can appear in the field. The lesson is not that COTS replaces every military system; it is that COTS needs a security, management, and mission layer.
AI API costs are about to explode and defense budgets are not ready
Cloud inference pricing is volatile, model demand keeps rising, and defense programs that rely on metered APIs inherit that uncertainty. Local inference gives teams a cost curve they can control.
Mesh networking for the dismounted warfighter: what works and what does not
Dedicated tactical radios still matter. EdgeLance Mesh is a layer above the transport: it uses whatever links are available and routes mission data by priority, bandwidth, battery, and trust.
Local AI keeps working when links get contested
Contested links make cloud-only AI fragile. Local inference on edge hardware is not just a cost optimization; it is how operators retain useful AI when connectivity is degraded.
Why COTS hardware is becoming a serious tactical node
Modern consumer hardware now carries enough local compute for real edge AI workflows. The remaining problem is software: security posture, fleet management, model loadouts, and mission-aware operations.
Why Knox is not enough: classification-aware MDM for contested environments
Enterprise MDM proved consumer hardware can be managed. Tactical operations need a mission-aware layer for data boundaries, emissions controls, night-vision-aware UI, duress workflows, and airgapped updates.
Why AI without source evidence is operationally useless
An AI that says 'hostile contact' without showing the camera clip, RF signature, and detection confidence is asking the operator to act on faith. That is not how tactical decisions work.
Mission continuity in contested comms: why every node has to be the system
Most edge platforms treat disconnected operation as a fallback. EdgeLance treats it as an operating assumption: each node should remain useful when the network is degraded, intermittent, or denied.