Tactical Edge
System Online

Distributed Real-time AI Decision Intelligence System

DRAIDIS

AI for the Warfighter

Modular, upgradable, AI-enabled decision advantage from the command post to the dismounted patrol. Runs at the edge, integrates through open APIs, supports flexible payloads, and syncs when connected.

0TOPS

GPU Compute

<0ms

Latency

7B–405B

LLM Range

IP67

MIL-STD

DRAIDIS is designed for modern defense programs that need modular hardware, interoperable software, and rapid refresh cycles without losing control of the mission architecture.

Modular

Compose the stack by mission need, from payload adapters to AI runtimes and operator workflows.

Upgradable

Ship new models, policies, and features as field refreshes instead of forklift replacements.

AI-Enabled

Decision support, sensor fusion, and reporting automation are built into every tier of the platform.

Interoperable

Designed to exchange data cleanly with ATAK, tactical networks, sensors, and higher-echelon systems.

Integrated

Brings sensors, AI, storage, sync, and operator interfaces into one coherent operating environment.

Open API

Open API adapters reduce custom glue code and make it practical to add mission systems quickly.

Flexible Payloads

Support different cameras, radios, RF tools, and mission payloads without redesigning the software stack.

Rapid Deployment & Refreshes

Go from prototype to pilot in 90 days, then keep iterating with fast software releases in the field.

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.

Llama 3.18B–405B

Multilingual reasoning

Meta

Qwen 2.57B–72B

Code + math + multilingual

Alibaba

Mistral7B–22B

Fast instruction following

Mistral AI

Phi-33.8B–14B

Compact edge reasoning

Microsoft

YOLO v8/v9Detection

Real-time object detection

Ultralytics

WhisperSpeech

90+ language ASR

OpenAI

CLIP / SigLIPVision

Image understanding

OpenAI / Google

Claude / GPTCloud

Full-scale when connected

via CHARLIE

TensorRTTritonllama.cppvLLMONNXGGUFAWQGPTQ

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

EO/IR CameraThermalAcousticCAN/ModbusGPSRFOpen API AdaptersFlexible Payloads

PERCEPTION

YOLO DetectionObject TrackingOCRAnomaly DetectionWhisper ASR

AI RUNTIME

TensorRTTriton Serverllama.cppEmbeddingsFAISS/Qdrant

DRAIDIS CORE

RAG EngineSOP AdvisorAlert TriageMission CopilotSensor FusionModular ServicesAudit

OPERATOR UI

ATAK PluginWeb DashboardVoice I/OTablet UIEvidence Export

DATA & SYNC

Vector DBEvent StoreEncrypted SSDDelta SyncmTLSPolicy Repl.Rapid RefreshesS3 Data LakeKinesis

AWS INTEGRATION

AWS OutpostsGovCloudBedrockSageMakerKinesis StreamingS3EC2 GPUIAM/KMSIntegrated Control Plane

DRAIDIS Core Modules

Purpose-built AI modules that run entirely at the edge.

Sensor Fusion

Correlates feeds from EO/IR, thermal, acoustic, RF, and platform data into an integrated operating picture. The fusion layer is modular and interoperable, so new payloads do not require a new architecture.

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

AI-enabled natural language interface for querying system state, requesting sensor tasking, and generating situation reports. Voice and text input supported across disconnected and connected operations.

Sync & Federation

Delta-syncs data, policies, and software packages between DRAIDIS nodes over constrained links. mTLS encryption, conflict resolution, and rapid software refresh workflows are 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.

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 through open API adapters
  • YOLO detection + basic alert pipeline
  • Operator dashboard MVP with modular services baseline

Phase 2

Fieldable Alpha

Days 31-60

  • Multi-sensor fusion pipeline
  • Local LLM + RAG engine deployment
  • ATAK plugin and interoperable mission-system 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
  • Rapid software refresh packages and field upgrade path
  • 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 plus open API adapters for adjacent mission systems, sensors, and data services. Operators see integrated, interoperable intelligence in the ATAK workflow without switching applications or rebuilding their stack around a proprietary interface.

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, upgradable software stack, so capabilities proven on one tier transfer directly to others and can be pushed as rapid software refreshes instead of full platform rebuilds.

See DRAIDIS in Action

Schedule a classified demo or download the solution brief to share with your team.