Research
Edge AI, mesh, and the future of tactical ISR.
Analysis, whitepapers, and technical research on the operational, architectural, and acquisition questions behind mission software for the tactical edge.
Lattice + EdgeLance: why enterprise C2 and tactical edge AI are better together
Lattice gives command the operational picture. EdgeLance gives the operator mission intelligence. A bridge between them lets the team decide what flows upward, covering the full stack from satellite to soldier without forcing either side to compromise.
Ephemeral missions: how disposable mission data restores operator trust without compromising intelligence
Every device an operator carries is a potential discovery item. Enterprise platforms treat total data capture as a feature. Operators treat it as a liability. Ephemeral missions flip the default: isolated encrypted containers, selective AAR export, cryptographic destruction via hardware secure enclave, and destruction receipts. The team gets the intelligence. The raw data does not survive.
What happens when you wipe: cryptographic mission destruction and why operators will not use anything less
Ephemeral missions are not a feature toggle. They are a cryptographic architecture: destroy one key and every detection, transcription, and recommendation becomes mathematically unrecoverable across every node in the mesh. No forensic recovery, no enterprise copy.
After Anthropic: why sovereign AI means local AI
A vendor in California can revoke AI access to military systems based on its own policy decisions. That makes the architecture the vulnerability. Renting AI capability via API and owning it on your hardware are two different risk postures, and only one of them you actually control.
Field biometrics and AI-powered mission readiness: wearable data as a force multiplier
Every operator wears a WHOOP, Garmin, or Apple Watch. The data sits on their phone and goes nowhere useful. This paper covers how to aggregate wearable biometrics, score readiness with on-device AI, and publish the result in ATAK before step-off. No data leaves the network. The medic gets individual status. Command gets a quantifiable go/no-go.
The $5,000 ISR stack: what a MacBook, four iPhones, four cameras, and a LoRa radio can do
A MacBook Pro, four iPhones, four IP cameras, and a LoRa transmitter. Under $5,000. You get object detection, face recognition, vehicle fingerprinting, mesh networking, ATAK integration, and AI threat assessment. No cloud, no vendor lock-in, and you can field it in weeks.
Why the next C2 acquisition will not be a platform. It will be a layer.
Defense primes keep losing C2 deals because they bid platforms against platforms. The winning move is to own the tactical edge layer that sits underneath any platform. Acquiring that layer is faster than building it.
AirBridge: autonomous drone-assisted mesh for tactical data reachback (R&D concept)
R&D concept paper. A drone is more than an ISR asset. At the right altitude, it becomes a temporary airborne mesh bridge, store-and-forward courier, and reachback handoff point. This paper works through the range math, operational value, and routing model behind the AirBridge concept.
Top-down platforms give command a God view. Operators get a surveillance feed pointed at themselves.
Lattice and Maven are enterprise platforms built for command oversight and total data capture. The operator is not the customer; the operator is the data source. EdgeLance inverts that: mission intelligence belongs to the team, syncs upward by choice, and wipes clean when the op is over.
Designing for the operator who does not trust you
SOF teams reject enterprise tools because every platform they have been handed is built for the people watching them, not for them. Designing for distrust means local-first by default, team-owned data, promotion-based sharing, and a system that proves it can be wiped before anyone trusts it.
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's M5 ships 38 TOPS in a $1,099 laptop and the A19 Pro matches it in a phone. The military that figures out how to run localized consumer AI compute in a coordinated mesh will own the next fight.
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 calls for autonomous drone constellations with edge-based computing and multi-agent operations. The concept is clear, but the node-level software layer that makes it work for ISR at the company level does not exist yet.
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%. If your AI sends queries to the cloud, the adversary can disable it by attacking the link.
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 what it takes to close it.
The defense AI market is moving toward the tactical edge
Program offices want AI that works closer to the sensor and the operator, with less dependence on perfect connectivity. That opens 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 gap is lower in the formation, where teams need AI, mesh, cameras, and device control but have no dedicated infrastructure or program office backing them up.
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 governance policy and operational tooling is where missions stall and legal reviews fail. This paper proposes a field-deployable model governance architecture.
What Ukraine taught NATO about consumer hardware in combat
Recent conflicts proved how fast commercial devices, drones, and compute show up in the field. COTS does not replace every military system. It needs a security and management layer before it belongs in a mission.
AI API costs are about to explode and defense budgets are not ready
Cloud inference pricing is volatile and model demand keeps rising. Defense programs that rely on metered APIs inherit that uncertainty. Local inference puts the cost curve back in your hands.
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 what ISR at the company level and below looks like without enterprise infrastructure.
Mesh networking for the dismounted warfighter: what works and what does not
Dedicated tactical radios still matter. EdgeLance Mesh sits above the transport, using whatever links are available and routing 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 how operators keep useful AI running when connectivity degrades.
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.
Why COTS hardware is becoming a serious tactical node
Modern consumer hardware has enough local compute for real edge AI workflows. The remaining problem is software: security posture, fleet management, model loadouts, and making operations mission-aware.
Why Knox is not enough: classification-aware MDM for contested environments
Enterprise MDM proved consumer hardware can be managed. Tactical operations need more: data boundaries, emissions controls, NVG-compatible UI, duress workflows, and airgapped updates.
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 have reshaped how DoD buys software. This paper maps the acquisition pathways available to nontraditional defense AI vendors.
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 the baseline. Each node stays useful whether the network is degraded, intermittent, or denied entirely.