Confidential - Investor Materials

Problem Validation Report

Milsim.AI: Synchronized Multimodal Dataset Platform

Version: 1.0 Date: January 2026

Milsim.AI addresses a critical bottleneck in AI development: the severe shortage of synchronized, real-world multimodal datasets for training embodied AI systems. By leveraging the global airsoft/milsim community as a voluntary data collection network, we create high-quality, ethically-sourced training data that defense AI, robotics, and simulation companies desperately need.

Problem Statement

The Core Problem

AI companies cannot build effective embodied intelligence systems because they lack access to synchronized, multi-agent, real-world operational data.

The development of autonomous systems, military AI, and advanced robotics is fundamentally constrained by data availability. While computer vision has ImageNet and language models have the internet, embodied AI has no equivalent large-scale, multimodal dataset.

Why This Problem Exists

Barrier 01

Real Military Data is Classified

Defense departments cannot share operational footage for commercial AI training. AAR data is restricted, and multi-sensor battlefield recordings are state secrets.

Barrier 02

Synthetic Data Has Limits

Domain gap between simulation and reality causes model failures. Synthetic data will supply 60% of training data, but the remaining 40% real-world data is the bottleneck.

Barrier 03

Existing Datasets Are Inadequate

DROID: only 75k episodes. Driving datasets focus on vehicles. No existing dataset captures coordinated multi-agent tactical scenarios.

Barrier 04

Collection is Prohibitively Expensive

Video licensing: $1-4/minute. Annotation: $1-5/item. Multi-view setups cost millions. Coordinating hundreds of participants is impossible.

Market Signals Indicating Demand

Defense AI Funding Explosion

Company	Valuation	Recent Funding	Focus Area
Anduril	$30.5B	$2.5B (2024)	Autonomous weapons systems
Shield AI	$5.6B	$540M (2024)	AI pilots for aircraft
Scale AI	$14B	-	Defense data labeling
Helsing	~$5B	$450M (2024)	European defense AI

Sources: Fortune - Anduril, Shield AI

$1.8B

DoD AI Budget FY2025

+63.6%

YoY Increase

$3B

Defense Tech VC 2024

+11%

YoY Growth

Robotics Companies Need Real-World Data

NVIDIA released thousands of hours of multi-camera video for physical AI training in March 2025
Generalist AI is building "the largest and most diverse real-world manipulation dataset ever"
RealMan launched RealSource to address "industry shortage of fully aligned real-world data"

Training Data Costs Are Skyrocketing

"AI training data has a price tag that only Big Tech can afford" - TechCrunch, June 2024

Complete datasets cost $1,000 to $50,000+ depending on domain and volume
Data cleaning and preprocessing: $5,000 to $30,000 per project
Manual labeling: $10,000 to $100,000+ for large datasets

3D/4D Reconstruction Requirements

3D Gaussian Splatting requires synchronized multi-camera footage with known camera positions
Training time reduced from 48 hours (NeRF) to 35-45 minutes with proper data
4D dynamic scene reconstruction is emerging but lacks real-world training data for human activities

Customer Pain Points (Validated)

Pain Point 1: Data Scarcity

Critical

Who: Defense AI companies, robotics startups, simulation developers

"Industrial robotic applications face a fundamental challenge: each new task effectively creates a new domain requiring fresh data collection" - Label Studio

Cannot train models without data. This is the #1 blocker for embodied AI development.

Pain Point 2: Synchronization Challenges

High

Who: Multi-agent system developers, 3D reconstruction researchers

DROID uses identical hardware across all 13 institutions to ensure consistency. 4D Gaussian Splatting requires precise temporal alignment across viewpoints. Unsynchronized data is unusable for many applications.

Pain Point 3: Ethical Sourcing

High

Who: All AI companies facing regulatory scrutiny

"Companies training on unlicensed footage are running many risks" - TechCrunch

EU copyright framework requires consent for training data. Legal exposure and reputational risk are mounting.

Pain Point 4: Scenario Diversity

Medium-High

Who: Military simulation companies, game developers

MAN TruckScenes created specifically because autonomous driving datasets don't cover trucks. Models trained on limited scenarios fail in deployment.

Problem Quantification

Total Addressable Problem

Sector	Annual Data Spend	Data Gap
Defense AI	$500M+	Multi-agent tactical scenarios
Autonomous Vehicles	$300M+	Edge cases, human interactions
Robotics	$200M+	Real-world manipulation
Game Development	$150M+	Motion capture, realistic AI
Total	$1.15B+

Cost of the Problem

For Defense AI Companies:

6-12 months delay in model development due to data limitations
$2-5M spent on custom data collection per project
40% of synthetic training data fails to transfer to real-world performance

For Robotics Companies:

NVIDIA invested in creating free datasets because the problem is so severe
Generalist AI building massive data collection infrastructure from scratch
Each company duplicating data collection efforts independently

Why Now?

Technology Enablers

Enabler 01

GPS Atomic Clock Precision

Nanosecond accuracy enables frame-perfect synchronization. At 60fps (16.67ms per frame), well within GPS sync tolerance. QR codes encode timestamps for post-hoc alignment.

Enabler 02

Smartphone Sensors

Modern smartphones have GPS, accelerometer, gyroscope, magnetometer, barometer. High-quality cameras capable of 4K/60fps. Sufficient for training data requirements.

Enabler 03

Affordable Action Cameras

GoPro-class devices: $200-400. Adequate quality for 3D reconstruction. Rugged enough for airsoft operations.

Enabler 04

AI Infrastructure Maturity

Cloud processing for video at scale. Established pipelines for multimodal data. Growing ecosystem of data marketplaces.

Market Timing

Defense spending surge: U.S. DoD AI budget up 63.6% YoY
Robotics investment wave: Major releases from NVIDIA, RealMan, Generalist AI in 2025
Regulatory pressure: EU AI Act pushing for traceable, ethical training data
Airsoft market growth: $2.2B market growing at 7.8% CAGR

Competitive Landscape

Approach	Cost	Scale	Quality	Ethical Sourcing
Military exercises	Very High	Limited	Excellent	Classified
Professional actors	High	Limited	Good	Yes
Synthetic generation	Medium	Unlimited	Domain gap issues	Yes
Scraped internet video	Low	Large	Variable	Legal risk
Milsim.AI	Low	Large	High	Yes

Conclusion

The problem is validated across multiple dimensions:

Market signals: Billions in funding flowing to companies constrained by data

Customer pain: Documented across defense, robotics, and simulation sectors

Timing: Technology enablers and market conditions align

Competitive gap: No existing solution addresses multi-agent, synchronized, multimodal tactical data

The AI industry needs what we can uniquely provide: ethically-sourced, perfectly-synchronized, multi-agent operational data at scale.