🛡️ Project Rampart

Technical Due Diligence Primer & Assessment

📊 Executive Summary

🎯 Mission Focus

Project Rampart (founded 2024, Arlington VA) exists to modernize military logistics and sustainment — this is their sole mission, not an afterthought or secondary business focus.

Closed YTD (July 2026)
$12.27M
98% of $12.5M annual target
Total Pipeline
$399M
$28M weighted ACV
Major Contract
$99M
5-year Army Enterprise Agreement
Team Size
61 FTEs
50% military veterans, 70% eng/product
Rampart Mission Statement
Rampart's mission: Optimal leverage of data, AI, and software to ensure victory through precise execution of critical logistics and sustainment operations

🎖️ Leadership

Rampart Leadership Team
Rampart leadership: Combining Silicon Valley tech talent with operational experience

David Tuttle — CEO

Former JSOC Operations Officer, led Anduril C4 business. Cornell BS, Georgetown MBA. Continues to serve in NY Army National Guard.

Peter Goldsborough — CTO

Facebook PyTorch AI core developer at 19, led Anduril ML platform. Forbes 30 Under 30. Technical University of Munich dropout.

Kyle Haire — Head of Growth

20-year USMC F/A-18 WSO, Naval SWDG, built Anduril's $100M+ qualified pipeline. Virginia Military Institute BS, George Mason MBA.

📈 Traction Highlights

🏗️ TyrOS Platform Architecture

💡 Core Insight

TyrOS is a modular, distributed software platform for operational logistics, enabling management of all pillars of sustainment across tactical and operational echelons.

TyrOS Architecture Diagram
TyrOS Architecture: 8 modular components with API layer connecting inputs to outputs
TyrOS Focus Areas
TyrOS integrates logistics as a component of mission command across all echelons

8 Core Modules

1️⃣ Meta (Ontology Layer)

Purpose: Foundational data layer for logistics entities and relationships

  • Units (SRCs), supply classes, platforms (by LIN)
  • DoD standards: NSNs, DODICs, OPCON/TACON/ADCON
  • Sources: MTOE (dynamically updated)

2️⃣ Norn (Mesh Network)

Purpose: Decentralized P2P Layer 3 virtual network

Norn Mesh Network
Norn establishes peer-to-peer connections across tactical echelons
  • Zero-trust: ECDH + AES-256-GCM
  • DDIL-optimized: No centralized infrastructure
  • Adaptive bandwidth, Zstandard compression
  • Multi-hop relay, configurable prioritization
GA

3️⃣ Tracker (Data Aggregation)

Purpose: Real-time + historical sustainment state

  • Ingests LOGSTATs, PERSTATs, combat SLANTs
  • Organizes by supply area (not strictly by unit)
  • Hierarchical aggregation
GA

4️⃣ Forecaster (Predictive Analytics)

Purpose: ML-powered supply/personnel forecasting

  • ARIMA/SARIMA, LSTM, XGBoost ensemble
  • Training: commercial inventory + synthetic military + curated LOGSTAT
  • Online learning from live operations
  • Edge-adaptable (CPU/GPU-aware)
GA

5️⃣ Router (Geospatial Routing)

Purpose: Threat-informed mobility engine

  • OpenStreetMap foundation
  • Integrates MIDB, Maven Smart System threat overlays
  • All routing data cached offline
  • Multi-domain (land, air, sea)
GA

6️⃣ Optimizer (Decision Engine)

Purpose: Solves Inventory Routing Problem (IRP)

  • Methods: LP, MIP, Clarke-Wright, Simulated Annealing, RL
  • Multi-constraint 3D bin-packing (cargo dimensions, weight, NEW)
  • Multi-modal optimization
  • Operational preference models
GA

7️⃣ Raido (Transportation Management)

Purpose: Orchestrates logistics mission execution

  • Full lifecycle: ad-hoc pull → pre-planned push
  • Authoritative scheduler for transport assets
  • GANTT/Kanban views with Sync Matrix
  • Integrates GCSS, TCPT (personnel qualifications)
GA

8️⃣ Planner (Sustainment Planning)

Purpose: Generates ML-driven COAs

  • Leverages Army ST 4-2 planning factors
  • Online learning via Tracker data
  • Planning factors exposed via API/UI
  • Configurable priority of support/supply
GA

🔌 API Layer

HTTP REST API

  • OpenAPI/Swagger documentation
  • RESTful endpoints for all modules
  • Standard HTTP/HTTPS transport
GA

gRPC API

  • Protocol Buffers for serialization
  • High-performance binary protocol
  • Bi-directional streaming support
GA

SDK

  • Client libraries provided
  • Simplifies integration
  • Handles authentication and retries
GA

🚀 Deployment Architecture

Containerized

  • Docker containers for all components
  • Kubernetes orchestration
  • Horizontal scaling support

Cloud-Agnostic

  • AWS, GCP, Azure support
  • FedRAMP High certified
  • On-premise deployment capable

CI/CD Pipeline

  • Nessus vulnerability scanning
  • Aikido security testing
  • Automated deployment workflows
TyrOS Deployment Model
TyrOS deployment model: Scales from edge (mobile devices) to cloud depending on echelon and connectivity
Echelon Hardware Capabilities
Company/Battalion Toughbooks, rugged laptops Basic forecasting, routing, optimization, tracking in disconnected environments
Brigade/Division/Corps Multi-core server blades, mobile data centers Full TyrOS suite with high-fidelity forecasting and optimization
Stationary (with cloud) Cloud-enabled configuration Centralized compute/storage while interfacing with forward edge nodes

🤖 AI & Agent Architecture

🧠 Saga (Generative AI Agent)

⚠️ Documentation Gap

CRITICAL: Saga is mentioned extensively (product tree, screenshots, investor update) but no technical architecture details provided in reviewed materials. This is a major gap for DD.

What We Know About Saga

  • Publicly launched generative AI capability
  • LLM-powered sustainment assistant
  • Appears in product screenshots (chat interface, map integration)
  • Mentioned as part of multi-product expansion (2026 investor update)

What We Don't Know (DD Must Ask)

  • Which underlying LLM(s) power Saga?
  • Guardrails against dangerous/wrong/hallucinated COAs?
  • How do model updates reach 5 kbps edge nodes?
  • Tool-calling architecture? Grounding mechanism (RAG)?
  • Human-in-the-loop patterns?

📊 Forecaster (Predictive AI)

Model Ensemble

  • ARIMA/SARIMA: Temporal trends, seasonality
  • LSTM: Long-range dependencies
  • XGBoost: High-dimensional sparse data

Training Data Lineage

  • Commercial inventory + resupply datasets
  • Synthetic military stockage scenarios
  • Curated historical LOGSTAT data
  • Platform damage/repair from real exercises (PERSTAT, combat SLANT)

Online Learning

  • Ingests real-time LOGSTATs, combat SLANTs, PERSTATs
  • Exponential Moving Averages smoothing
  • Context-aware (scheduled events, mission plans, human guidance)

⚙️ Optimizer (Mathematical Optimization + RL)

Linear Programming (LP)
Mixed-Integer Programming
Clarke-Wright Savings
Simulated Annealing
Reinforcement Learning

Core Problem: Generalized Inventory Routing Problem (IRP) — optimizing over combined space of inventory management, transport routing, load planning across time, geography, and transportation modalities.

🌐 Edge & DDIL Architecture

✅ Offline-First Design Principle

TyrOS is engineered for the tactical edge, caching critical last-known inventory, route, and vehicle data at edge nodes to enable disconnected operation with minimal resource use.

Current Logistics Status Quo
The problem: Current logistics systems use analog processes designed for GWOT, not peer/near-peer conflict
TyrOS Supply Webs
TyrOS creates intelligent "supply webs" — end-to-end logistics nodes across echelons with predictive analytics, vehicle sensor integration, distribution scheduling, and predictive maintenance

🔗 Norn Mesh Network Deep Dive

Network Architecture

  • Peer-to-peer Layer 3 virtual network
  • No centralized infrastructure dependency
  • Multi-hop relay supported
  • Zero-trust: ECDH key exchange + AES-256-GCM encryption

DDIL Optimizations

  • Adaptive bandwidth: Transmits whatever conditions allow
  • Zstandard compression: High ratios, low latency
  • Configurable prioritization: Critical nodes/data types first
  • Tracks transmission state: Resumes interrupted transfers

Prioritization Rules

  • Network topology: Higher-echelon units receive updates before lateral units
  • Data type: Platform readiness, supply changes sent ahead of lower-latency information
  • No fixed bandwidth requirements: Fully adaptive

🔍 DD Critical Question

Has 5 kbps + mesh + auto-conflict-resolution been proven at scale (~15K+ personnel, hundreds-to-thousands of nodes) with some isolated for days, then reconnecting cleanly — in real operations?

Materials show 1CD, 25ID production use but scale validation details unclear.

📦 Scale Adaptation by Echelon

Company/Battalion Level

Toughbooks, rugged laptops — basic forecasting, routing, optimization, tracking in disconnected environments

Brigade/Division/Corps Level

Multi-core server blades, mobile data centers — full TyrOS suite with high-fidelity forecasting/optimization

Stationary Units (Cloud Connectivity)

Cloud-enabled configuration — centralized compute/storage while interfacing with forward-deployed edge nodes

📊 Traction & Deployments

🎯 Pipeline Breakdown

US Army
51%
of total pipeline
US Air Force
25%
of total pipeline
US Marine Corps
9%
of total pipeline
Joint/SOCOM
7%
of total pipeline

🏆 Major Contract Wins

$99M Army Enterprise Agreement

5-year standing contract vehicle usable across Joint Force. Most critical — provides scalable acquisition pathway reducing contracting friction.

Signed July 2026

1st Cavalry Division

Production deployment. Testimonial: "TyrOS proved critical to generating accurate shared understanding and visibility of commodity and combat power."

Production

25th Infantry Division

Production deployment. TyrOS used end-to-end (not as dashboard, but as the system through which logisticians operate).

Production

USMC Project Dynamis

C2 modernization program. PACOM Balikatan 26 exercise with extremely positive reviews. Phase 2 follow-on with Warfighting Lab.

Active

🎖️ Key Validation Points

  • First "SOF-peculiar" sustainment C2 application — validated at SOF Week
  • GCSS-Army write-back approval nearing completion (property accountability integration — significant vote of confidence)
  • 4th Infantry Division joining post-PCC6 (Project Convergence Capstone 6)
  • 1CD contract plus-up in negotiation (more than doubling contract value)

📈 Growth Trajectory

Metric 2025 (Actual) 2026 YTD (July) 2026 Target 2027 Forecast
Revenue (Closed/Won) $1.5M $13.3M $32M $46M
Revenue (Weighted Forecast) $15M
Total Pipeline $114M $270M

Cash on Hand: $23.3M (June 2026)
Monthly Burn: $1.66M (~70% payroll)

✅ Key Recommendations

🎯 Top 5 Priority Questions for DD Session

1. Saga Architecture (CRITICAL GAP)

Request full technical architecture: LLM stack, tool-calling, grounding, guardrails. This is completely undocumented.

2. Scale Validation

Has 5 kbps mesh + CRDT been proven at 15K+ personnel with multi-day disconnects in real operations?

3. Salesforce Data Integration

Can TyrOS data model integrate with Data Cloud? Event streams? Stable identifiers for Agentforce agents?

4. CRDT Implementation Details

Custom or 3P? Scalability testing? What happens under network partition?

5. Customer Reference Calls

Direct access to 1CD, 25ID users for unfiltered feedback on production experience.

✅ Final Recommendations

🎯 Recommendation

Add 7 new sections (H-O above) and augment existing sections with questions on:

  • Data export formats
  • Saga tool-calling architecture
  • Network partition handling
  • Customer reference calls (direct 1CD, 25ID user feedback)

This would elevate the DD agenda from "comprehensive technical audit" to "acquisition-ready diligence covering technical, operational, strategic, and integration dimensions."

🚩 Red Flags to Investigate

  1. Saga architecture completely undocumented — major gap for a product that's "publicly launched"
  2. No IL5/IL6 ATO provenance — materials reference these environments but provide no ATO details
  3. Scale validation unclear — 1CD/25ID production use documented, but has mesh been proven at 15K+ personnel scale with multi-day disconnects?
  4. CRDT implementation not detailed — conflict resolution is make-or-break for DDIL; needs deep technical validation
  5. No SBOM, no open source licensing details — critical for acquisition risk assessment
  6. No current vulnerability status — materials mention scanning tools but not current vuln count

💚 Green Flags (Strengths)

  1. Production deployments with Army divisions — not just demos/experiments
  2. GCSS-Army write-back approval — significant trust signal (property accountability integration)
  3. First "SOF-peculiar" C2 application — differentiated positioning
  4. Modular, containerized architecture — clean separation of concerns, cloud-agnostic
  5. Offline-first design principle — architected for DDIL from day one (not bolted on)
  6. Strong leadership pedigree — JSOC + Anduril DNA, proven operators
  7. 50% military veterans, deep domain expertise — understands the customer
  8. $99M Enterprise Agreement — scalable acquisition vehicle reduces friction

🎯 Priority Artifacts to Request in DD Session

1. Live Demo

Disconnected operation and reconnection (mesh resilience) — must-see for validating DDIL claims

2. Capability Maturity Matrix

Module-by-module breakdown (GA vs. beta vs. roadmap) for all 8 TyrOS components + Saga/ADK/TyrSense

3. Architecture Diagrams

Edge/cloud topology, data flow, hierarchical replication model, conflict resolution strategy

4. Saga Technical Architecture

LLM stack, tool-calling, grounding mechanism, guardrails — major documentation gap

5. CRDT Implementation

Custom or 3P? Scalability testing results at 15K+ personnel scale?

6. Performance Benchmarks

COA generation time, mesh latency under bandwidth constraints (5 kbps, LTE, SATCOM)

7. SBOM

Open source usage, licensing exposure, copyleft contamination risk

8. Vulnerability Report

Current open vulns, severity, remediation timeline

9. IL5/IL6 ATO Documentation

Authorizing agency, scope, expiration dates — critical for classified deployments

10. Customer Reference Contacts

Direct 1CD, 25ID user feedback (unfiltered)

11. Data Model Schema

Published schema/entity model for sustainment objects — Salesforce integration critical

12. Scale Testing Results

Has 5 kbps + mesh + auto-conflict-resolution been proven at 15K+ personnel scale with multi-day disconnects in real operations?

📄 Document Metadata

Generated: August 2026

Project Codename: Rampart

Classification: Salesforce Internal / Acquisition-Sensitive

📎 Source Materials

📄 Technical White Paper 📊 Investor Update - July 2026 📑 Primer Deck 📋 Quick Look - July 2026 📝 Salesforce Internal DD Agenda