Tactical Edge
All DRAIDIS Products
Command Post · Powered by AWS

“Charlie is your CP — runs on AWS Outposts.”

The command-post tier of the DRAIDIS family. Cloud-grade AI on AWS Outposts with full fleet management of all field nodes. On-premises data sovereignty with optional cloud sync.

GPU

EC2 GPU

AI Stack

Bedrock

Fleet

All Nodes

Compliance

IL5

DRAIDIS CHARLIE command post with AWS Outposts rack blueprint illustration

Full Specifications

PlatformAWS Outposts rack
GPUEC2 GPU instances (GovCloud compatible)
StorageAmazon S3 data lake + EBS
AI ServicesSageMaker, Bedrock, Kinesis
Network10GbE + SATCOM uplink
FleetManages all DRAIDIS Alpha and Bravo nodes
SyncPolicy-based delta sync, auto when connected
SecurityIAM, KMS, FIPS 140-2, GovCloud
ComplianceFedRAMP High, IL5, GovCloud
Connectivity10GbE, satellite uplink capable

Capabilities

Cloud-grade services running on-premises via AWS Outposts.

Fleet Management Dashboard

Real-time status of all DRAIDIS field nodes — battery levels, sensor status, AI workload, storage capacity, and network connectivity from a single pane.

Model Training & Fine-Tuning

SageMaker-powered model training on mission-specific data. Push updated models to field nodes via delta-compressed packages with staged rollouts.

Data Lake Aggregation

Correlate detections and intelligence from all Alpha and Bravo nodes into a fused S3 data lake. Full intelligence archive with provenance tracking.

Real-Time Streaming Analytics

Kinesis-powered ingestion from all field nodes. Process sensor telemetry, detection events, and operational data at scale with sub-second latency.

Policy Distribution

Distribute mission-specific ROE, detection thresholds, and operational policies to all connected DRAIDIS nodes. Version-controlled with audit trail.

Theater-Wide COP

Common operating picture aggregating all DRAIDIS node detections, positions, and intelligence into a unified map-based display for command staff.

Deployment Scenarios

Command-level AI infrastructure for theater-wide operations.

Division / Corps TOC

Full-scale fleet management with theater-wide intelligence aggregation, model training pipelines, and real-time streaming analytics for command decisions.

Forward Operating Base CP

Deployed at the FOB to manage all nearby DRAIDIS nodes, aggregate local intelligence, and provide enhanced AI capabilities via Bedrock foundation models.

SATCOM-Connected HQ

Satellite-linked command post with cloud region sync. Maintains full offline capability while leveraging cloud services when SATCOM bandwidth permits.

Multi-Site Operations Center

Federated CHARLIE nodes across multiple sites with cross-site intelligence sharing, model synchronization, and unified fleet visibility.

TOC Architecture

DRAIDIS CHARLIE TOC with connected field nodes and satellite uplink

0+

Field Nodes

10GbE

Network Fabric

IL5

Impact Level

0%

Offline Capable

Frequently Asked Questions

No. CHARLIE runs on AWS Outposts — physical rack infrastructure deployed at your command post. All AWS services (EC2, S3, Bedrock) run locally on the Outposts hardware. When connectivity to the AWS Region is available, CHARLIE syncs additional capabilities, but it operates fully offline in denied environments.

CHARLIE leverages EC2 GPU instances for AI inference and training, S3 for data lake storage, Bedrock for foundation models, SageMaker for model fine-tuning, Kinesis for real-time data streaming, and IAM/KMS for security. All services are GovCloud compatible.

CHARLIE is designed to manage entire DRAIDIS fleets — typically 10-50+ Alpha and Bravo nodes simultaneously. It handles model distribution, intelligence aggregation, policy replication, and delta synchronization across all connected nodes.

CHARLIE runs on AWS Outposts 42U rack infrastructure. A standard deployment uses 1-2 racks depending on GPU and storage requirements. Outposts racks are designed for data center environments but can be deployed in hardened tactical facilities.

Deploy DRAIDIS CHARLIE

Request a demo or download the DRAIDIS CHARLIE solution brief for your team.