Distributed Real-time AI Decision Intelligence System
DRAIDIS
AI for the Warfighter
Modular tactical-edge software from the command post to the dismounted team. Put selected AI workloads close to the sensors, connect mission payloads through APIs, and control what synchronizes when a link is available.
0
TOPSPeak edge compute
NVIDIA IGX T5000
<0
msAlert latency target*
Measured during the pilot
7B–70B+
Model size range*
Matched to hardware and workload
IP67
Enclosure target*
Tested on the final enclosure
* Design targets, validated during your pilot against the selected hardware, models, sensors, network, power, enclosure, and security controls in your operating environment.
DRAIDIS Core is the shared decision-support software. Nano, Alpha, Bravo, and Charlie adapt it to different payload, compute, power, and operating needs. The values below are design targets that we confirm during integration and field testing.
One software foundation, tailored and tested for each mission
Battlefield AI Layer
Add BLADE when your teams need commander-ready briefs, COA support, mission handoffs, and explicit approval steps on top of edge intelligence. Together, BLADE and DRAIDIS support local processing and controlled synchronization across constrained links.
DRAIDIS is designed for defense programs that need modular hardware, software that connects through defined interfaces, and a practical way to test and release updates without losing control of the mission architecture.
Modular
Compose the stack by mission need, from payload adapters to AI runtimes and operator workflows.
Upgradable
Plan model, policy, and feature refreshes without replacing the full hardware stack.
AI-Enabled
Decision support, sensor fusion, and draft-reporting modules can be configured for the platform tier and mission workflow.
Integration-Ready
Use adapters for ATAK, tactical networks, sensors, and higher-echelon systems, then test each interface in the program environment.
Integrated
Brings sensors, AI, storage, sync, and operator interfaces into one coherent operating environment.
Open API
Open API adapters give teams a consistent way to connect selected mission systems and payloads.
Flexible Payloads
Add cameras, radios, RF tools, and mission payloads through modular interfaces instead of rebuilding the core workflow.
Phased Deployment & Refreshes
Move from prototype to pilot in phases, with milestones set around hardware, integration, safety, security, and field tests.
Select Models for the Mission Architecture
The families below provide a starting point for model selection. We verify the exact version, license, deployment format, performance, and service availability for your mission before choosing the stack.
Evaluate local or regional deployment
Meta
Evaluate agent and edge workflows
NVIDIA
Evaluate local instruction workflows
Mistral AI
Evaluate compact local workloads
Microsoft
Evaluate mission object classes
Ultralytics
Test language coverage with mission audio
OpenAI
Evaluate image retrieval and classification
OpenAI / Google
Confirm options by service, Region, policy, and connectivity
via selected services
How DRAIDIS Works
Seven layers connect physical sensors to local AI, operator workflows, controlled synchronization, and optional AWS services. Your architecture determines which layers run on the node and which use a network link.
SENSORS & INGEST
PERCEPTION
AI RUNTIME
DRAIDIS CORE
OPERATOR UI
DATA & SYNC
AWS INTEGRATION
DRAIDIS Core Decision-Support Loop
Configure AI modules to observe, orient, recommend, and prepare action at the edge. Operators set the rules, approve high-impact actions, and retain the supporting evidence.
Mission Decision-Support Loop
Runs an observe, orient, and recommend loop locally. DRAIDIS Core uses the sensor events, mission context, SOPs, and operator intent your team selects. People review recommendations and approve high-impact actions.
Sensor Fusion
Brings selected EO/IR, thermal, acoustic, RF, and platform feeds into one operating view. Modular adapters give each payload a consistent integration pattern, and the pilot tests timing, data meaning, security, and performance.
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
Ranks candidate detections by threat level, proximity, and mission context so operators can review the most important events first.
Operator Copilot
Lets operators query system state, request sensor tasking, and draft situation reports by voice or text. The pilot tests which functions remain available under expected link conditions.
Audit & Explainability
Records the source, model version, confidence, evidence, policy checks, and operator response for the alerts and recommendations your team chooses to track.
Where Your Teams Use DRAIDIS
Start with a workflow where local processing, source traceability, and human approval matter.
Execution Roadmap
A sample 30/60/90-day path for a focused pilot. We set the milestones after scoping hardware, integrations, data, safety, security, and field tests with your team.
Phase 1
Prototype
Planning 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
Planning days 31-60
- •Multi-sensor fusion pipeline
- •Local LLM + RAG engine deployment
- •ATAK plugin, mission-system integration, and interface testing
- •Voice I/O (Whisper + TTS)
- •Encrypted storage and audit logging
Phase 3
Pilot
Planning days 61-90
- •Field testing with operator feedback
- •Delta sync between DRAIDIS nodes
- •Staged software update packages and field upgrade path
- •Performance tuning and hardening
- •Operator training and documentation
- •Pilot deployment sign-off
0
Prototype Planning Point*
0
Alpha Planning Point*
0
Pilot Planning Point*
Local
Configured Offline Workloads
Edge+Cloud
Hybrid Sync
* Use these dates to frame the planning conversation. Your schedule will reflect procurement, data and interface access, integration, safety, security, and the field-test plan. You choose the operational data used to evaluate or improve a model. Operational data stays out of shared model training unless your team directs us to use it for that purpose.
Frequently Asked Questions
DRAIDIS (Distributed Real-time AI Decision Intelligence System) is a family of edge-AI designs for military, defense, and commercial operations teams. It brings selected sensor processing, retrieval, and decision-support workloads close to where teams work, from portable and vehicle systems to command posts and industrial sites. Commercial use cases include visual quality inspection, predictive maintenance, energy and utilities monitoring, logistics and warehouse intelligence, and local search across SOPs, manuals, and maintenance logs. Your team controls what data and software move between nodes and the cloud, with workflows scoped and validated during your pilot.
No continuous cloud connection is required for workloads placed on the local node. Your pilot defines what must work locally and what needs a network link, then tests that behavior with the selected hardware, models, data, interfaces, and power profile.
DRAIDIS Charlie can combine AWS Outposts with regional services such as Amazon EC2, Amazon Bedrock, SageMaker AI, Kinesis, and Amazon S3. We map where each workload runs, where data moves, and which services fit your Region and security requirements. Your program team chooses the final service set.
DRAIDIS can connect to ATAK and other mission systems through configured API adapters. During the pilot, we test the exact interfaces, data fields, security controls, and operator display your program plans to use.
A focused pilot can use a 30/60/90-day planning sequence: prototype, integration alpha, then field evaluation. We set the actual schedule after scoping the mission, hardware, interfaces, data access, safety and security work, and test plan.
See DRAIDIS in Action
Schedule a mission-focused demo or download the solution brief to share with your team.