Distributed Real-time AI Decision Intelligence System
DRAIDIS
AI for the Warfighter
From the command post to the dismounted patrol — decision advantage at the point of contact. Runs offline at the edge — syncs when connected.
0TOPS
GPU Compute
<0ms
Latency
7B–405B
LLM Range
IP67
MIL-STD
Choose Your DRAIDIS
Three form factors, one unified software stack. Pick the configuration that matches your mission.
All DRAIDIS configurations share a unified software stack
Run Any Model, Anywhere
From 7B on a backpack to 405B at the command post — DRAIDIS runs the full spectrum of open-weight and commercial AI models. Swap models per mission, no redeployment needed.
Multilingual reasoning
Meta
Code + math + multilingual
Alibaba
Fast instruction following
Mistral AI
Compact edge reasoning
Microsoft
Real-time object detection
Ultralytics
90+ language ASR
OpenAI
Image understanding
OpenAI / Google
Full-scale when connected
via CHARLIE
How DRAIDIS Works
Seven layers from physical sensors to encrypted sync and AWS integration — every layer runs on-device, with cloud services available when connected.
SENSORS & INGEST
PERCEPTION
AI RUNTIME
DRAIDIS CORE
OPERATOR UI
DATA & SYNC
AWS INTEGRATION
DRAIDIS Core Modules
Purpose-built AI modules that run entirely at the edge.
Sensor Fusion
Correlates feeds from EO/IR, thermal, acoustic, and RF sensors into a unified operating picture. Reduces cognitive load by surfacing only what matters.
Local RAG Engine
Retrieves doctrine, SOPs, and field manuals from an encrypted local vector store. Answers operator questions with cited, authoritative references.
Alert Triage Agent
Prioritizes detections by threat level, proximity, and mission context. Filters noise so operators focus on actionable intelligence.
Operator Copilot
Natural language interface for querying system state, requesting sensor tasking, and generating situation reports. Voice and text input supported.
Sync & Federation
Delta-syncs data between DRAIDIS nodes over constrained links. mTLS encryption, conflict resolution, and policy-based replication built in.
Audit & Explainability
Every AI decision is logged with full provenance - input data, model version, confidence score, and reasoning chain. Meets DoD explainability requirements.
Where Your Teams Use DRAIDIS
Tactical AI across the full spectrum of operations.
Execution Roadmap
From zero to pilot-ready in 90 days.
Phase 1
Prototype
Days 1-30
- •Hardware selection and procurement
- •Base OS and AI runtime installation
- •Single-sensor integration (camera or thermal)
- •YOLO detection + basic alert pipeline
- •Operator dashboard MVP
Phase 2
Fieldable Alpha
Days 31-60
- •Multi-sensor fusion pipeline
- •Local LLM + RAG engine deployment
- •ATAK plugin integration
- •Voice I/O (Whisper + TTS)
- •Encrypted storage and audit logging
Phase 3
Pilot
Days 61-90
- •Field testing with operator feedback
- •Delta sync between DRAIDIS nodes
- •Performance tuning and hardening
- •Operator training and documentation
- •Pilot deployment sign-off
0
Days to Prototype
0
Days to Alpha
0
Days to Pilot
0%
Offline Capable
Edge+Cloud
Hybrid Sync
Frequently Asked Questions
DRAIDIS (Distributed Real-time AI Decision Intelligence System) is a family of edge AI systems designed for military and defense operators. It runs offline at the edge, processing sensor data through local AI models to deliver real-time decision support - from dismounted patrols to vehicle platforms to command posts. When connectivity is available, DRAIDIS syncs data and models with cloud infrastructure for enhanced capabilities.
DRAIDIS is built for disconnected operations - all AI inference, RAG retrieval, and sensor fusion run on local hardware with no connectivity required. When network access is available, DRAIDIS syncs intelligence, model updates, and operational data with cloud infrastructure. DRAIDIS Charlie leverages AWS Outposts for additional compute while maintaining full offline capability in denied environments.
DRAIDIS Charlie runs on AWS Outposts - ruggedized rack infrastructure deployed at the command post. It leverages EC2 GPU instances (AWS GovCloud compatible), Bedrock for foundation models, SageMaker for model training and fine-tuning, Kinesis for real-time data streaming, and S3 for data lake storage. This gives you cloud-grade AI capabilities with on-premises data sovereignty and full offline fallback.
Yes. DRAIDIS includes a native ATAK plugin that overlays AI-generated alerts, detections, and recommendations directly onto the ATAK map interface. Operators see fused intelligence without switching applications or workflows.
DRAIDIS follows a 90-day deployment model: a working prototype in 30 days, a fieldable alpha with real sensor integration in 60 days, and a pilot-ready system with operator training in 90 days. All configurations share a unified software stack, so capabilities proven on one tier transfer directly to others.
See DRAIDIS in Action
Schedule a classified demo or download the solution brief to share with your team.