Forward-Deployed AI & Frontier Transformation

Enterprise AI engineering. Embedded, secure, and compliant.

Embedding secure, offline frontier AI systems and data governance directly into high-stakes operations. We replace conversational guesswork with the same systems discipline I learned maintaining tactical weapons.

DIB Projects Run

Weeks→Hours

SSP Generation

VPC

Safe Deployments

DoD

Grade Security

Discuss a Project Explore Applied Work

Forward Deployed Engineer

Frontier Transformation Lead

AI Governance Specialist

Systems Auditor

Submariner

Who I Am

Bringing submarine-grade reliability to language models.

I started my career in the weapons department of U.S. Navy Trident submarines. Serving on the Lafayette-class USS Casimir Pulaski (SSBN 633) and the USS Ohio (SSBN 726), I helped maintain Trident C-4 tactical missiles and weapons systems. Submarines are environments where systems require absolute reliability. If a system fails, you don't file a bug ticket; you put 130 lives at risk.

U.S. Navy Enlisted Submarine Warfare Insignia (Silver Dolphins)

Submarine Warfare Qualified: Attained through systems walkthroughs and board review

Today, I apply that same systems discipline to Large Language Models. Most AI systems are built as demonstration software; they behave unpredictably and hallucinate facts. When building for defense contractors, government agencies, and regulated spaces, that unpredictability is a failure state.

I build RAG systems that treat facts as source code. By enforcing strict retrieval boundaries, chain-of-verification prompting, and local VPC hosting, we ensure every word the model outputs is traceable back to raw evidence. No speculation. No leakage.

"We do not build chatbot demos. We build secure, verifiable AI pipelines that ground LLM output in source truth, reducing months of compliance drudgery to hours."

Frontier Transformation

Enterprise AI Engineering. Embedded.

We embed directly inside secure environments to deliver compliant AI transformations and change management.

FDE

Forward Deployment Engineering

Hands-on on-premise and private cloud (GCC High) systems integration. We embed directly to configure secure AI pipelines where data residency and compliance controls are non-negotiable.

Governance

AI Change Management

Establishing the guardrails and operational policies required to safely adopt LLMs. We train workforce teams, implement data-hygiene rules, and audit inputs to prevent CUI leaks.

Engineering

Offline & Private Architectures

Deploying secure, open-weights frontier models, local vector databases, and air-gapped retrieval-augmented generation (RAG) pipelines that run entirely within your private perimeter.

Compliance

NIST/CMMC AI Mapping

Aligning AI system boundaries directly to CMMC 2.0 and NIST SP 800-171 objectives, automating SSP compilation and evidence gathering via secure local AI models.

Demonstration Blueprints

Applied value: Local data vault demonstrations.

These open-source blueprints demonstrate how local RAG architectures and secure private LLMs solve high-stakes document parsing and information audit challenges without relying on external cloud APIs.

VA Claims

VA Claims Audit Blueprint

Illustrates deterministic parsing of high-volume medical histories and exposure logs using local vector vaults.

View Blueprint Guide →

Legal Assets

Asset Security Blueprint

Demonstrates private modeling of personal legal assets, custody parameters, and estate logs using offline LLMs.

View Blueprint Guide →

Bylaws Audit

HOA Covenants Blueprint

Demonstrates indexing, structuring, and querying complex community bylaws and resolutions via private local AI.

View Blueprint Guide →

Philosophy

Engineering over experimentation.

We treat LLM outputs as systems problems. Here is the process we use to ensure reliability:

Grounding in Source Truth

Zero-tolerance retrieval boundaries

Every output must be traceable. We index your raw evidence and force models to cite their sources. If the data isn't in the provided document set, the model returns a structured null. Hallucination is treated as a systems failure.

Source Traceability Null Handling Context Enforcement

Human-in-the-Loop Verification

Interactive review interfaces

We don't automate critical decisions. We build tools that present side-by-side evidence mappings, allowing human specialists or assessors to verify model claims in seconds.

Side-by-Side Review Citation Verification Draft Intervention

Private Observability

VPC hosting & secure auditing

Deployment within secure networks using audit logs, strict access parameters, and token cost tracking to ensure safe, observable operations.

Current Focus

Active research areas.

We study how to make Large Language Models reliable instruments for high-stakes, evidence-based compliance operations.

Our research applies automated knowledge retrieval directly to cybersecurity auditing. We build patterns that allow defense contractors to prepare System Security Plans that match the strict objectives of NIST SP 800-171A and 800-172 without months of writing.

Core Focus Areas

Architectures

Advanced RAG Agentic Workflows VPC Model Hosting

Logic & Parsing

Chain-of-Verification Structured Output Parsing Inline Citation Engines

The Proof

Vetted credentials and real experience.

Barry's AI matches advanced automation with federal compliance realities. We understand the exact evidence parameters assessors look for, and build pipelines that synthesize data to match those objectives.

Since 2022, I have served as a Cybersecurity Assessor with DCMA/DIBCAC, participating in more than 80 assessments. I am not a fan of paper certifications; true qualification is earned through hands-on practice. Real security is an operational discipline, not a paper chase.

🔐

U.S. Government Vetted

Active federal background investigation & suitability

Submarine Warfare Qualified (SS)

Attained through cross-functional systems walkthroughs and a rigorous evaluation board

U.S. Navy Submariner | Weapons Department

USS Casimir Pulaski (SSBN 633) & USS Ohio (SSBN 726) · Trident C-4 systems

Methodology Coverage

Compliance Automation

NIST SP 800-171A NIST SP 800-172 CMMC 2.0

AI Engineering

RAG Pipelines Agentic Workflows Local LLM Hosting

Deterministic Design

Citation Engines Structured Parsing Verification Loops

The Invitation

Discuss an AI Engineering Project

Let's talk about building secure, deterministic AI pipelines for your organization. No slide decks or high-pressure pitches. Just practical engineering.

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