AI Integration for Naval Operations and Defense Logistics.
We're a veteran-owned firm building secure AI systems that actually work for fleet maintenance, supply chain, and cleared data analysis. No hype, just deployment.
Your maintenance and logistics data is useless in disconnected silos.
Technical manuals, maintenance logs, and supply chain data are locked in legacy systems—PDFs, SharePoint sites, and outdated Oracle databases. Analysts spend their days manually collating reports instead of finding failure patterns. The result is reactive maintenance, surprise parts shortages, and an inability to forecast fleet readiness. Off-the-shelf AI tools can't handle the data complexity or meet security requirements for CUI and classified information, leaving you stuck with the status quo while operational demands increase.
- Predictive maintenance models fail due to unstructured data from CASREPs and 2-Kilos.
- Analysts can't query across multiple document repositories (JPL, NATEC) for root cause analysis.
- Supply chain forecasts are inaccurate, based on historical averages, not real-time conditions.
- Security protocols block the use of commercial AI APIs, creating a dead end for innovation.
We deploy secure, auditable AI systems against your existing data.
- STEP-01
Establish Secure Data Enclave
We deploy a secure environment, on-prem or in GovCloud, to process CUI or classified data. All access is logged and auditable from day one, meeting DoD requirements.
- STEP-02
Connect and Index Data Sources
Our engineers build connectors to your specific systems—from SharePoint document libraries to legacy maintenance databases. We extract and index text, tables, and images for unified analysis.
- STEP-03
Build and Tune Models
We implement and fine-tune models for specific tasks: predictive failure analysis on machinery data or RAG systems for querying thousands of technical manuals and directives.
- STEP-04
Deploy User-Facing Application
We deliver a simple, functional interface for your operators and analysts. The goal is not a science project, but a tool that provides answers and integrates into existing workflows.
from transformers import pipeline
from faiss_client import FaissDBClient
# Connect to secure vector database of Navy tech manuals
db_client = FaissDBClient(host="10.0.1.55", port=9090)
# Initialize a QA model pipeline
qa_pipeline = pipeline(
"question-answering",
model="distilbert-base-cased-distilled-squad",
tokenizer="distilbert-base-cased-distilled-squad"
)
def find_maintenance_procedure(query: str, doc_id: str):
"""
Queries indexed documents for a specific maintenance procedure.
"""
# Retrieve relevant context from the vector store
context = db_client.fetch_document_chunk(doc_id, query)
if not context:
return "No relevant procedure found in the specified document."
# Run the model to find the answer within the context
result = qa_pipeline(question=query, context=context)
return {
"answer": result['answer'],
"confidence": round(result['score'], 4),
"source_document": doc_id
}
# Example query from a maintenance tech
query = "What is the torque spec for the main rotor bolt?"
document = "NAVSEA S9086-VD-STM-010"
procedure = find_maintenance_procedure(query, document)
print(procedure)A Python snippet demonstrating a secure Retrieval-Augmented Generation (RAG) system. It queries a local vector database of technical manuals to provide precise answers for maintenance crews, ensuring data never leaves the secure network.
Field FAQ.
→ How do you handle classified or CUI data?
We deploy exclusively within your specified environment, whether it's on-premise hardware or a secure government cloud instance like AWS GovCloud. Our entire team consists of U.S. citizens, and we follow strict data handling protocols compliant with NIST 800-171. Your data is never processed by commercial, public-facing AI services. We build security and audit trails in from the start.
→ What makes VooStack different from a large defense prime contractor?
As a small, veteran-owned (SDVOSB) business, we provide senior engineers directly to your project without layers of bureaucracy. Our focus is on rapid delivery of a functional capability, typically in weeks or months, not years. You work directly with the people building the system, ensuring requirements are met without misinterpretation. We are built to be agile and responsive to mission needs.
→ Can your AI models integrate with our existing maintenance software?
Yes. Our primary goal is to augment, not replace, your current systems of record like NEMAIS or OOMA. We build custom API connectors and data pipelines that read from your databases, run the analysis, and can push results back into your existing dashboards or generate alerts. This minimizes disruption to your team's established workflows and reduces training burdens.
→ What kind of hardware is required to run these models?
The hardware depends on the model's complexity and performance requirements. Many document extraction and RAG systems can run effectively on standard server-grade CPUs. More intensive predictive models may require specific GPUs like NVIDIA's A100 or H100 series. We perform a full analysis of your requirements to specify the minimum viable hardware, ensuring you don't over-provision.
→ How do you ensure the AI's recommendations are reliable and auditable?
For critical systems, we prioritize model explainability, showing which data points led to a prediction. Every query, data source, and model output is logged, creating a complete audit trail crucial for validation, troubleshooting, and demonstrating compliance. We avoid 'black box' solutions where the decision-making process is opaque and indefensible.
→ Are you eligible for federal contracts as a Service-Disabled Veteran-Owned Small Business (SDVOSB)?
Yes, VooStack is a certified SDVOSB. This provides federal agencies, including the DoD, with a streamlined acquisition pathway to engage our services. Contracting officers can leverage SDVOSB sole-source and set-aside opportunities to meet mission requirements quickly while also fulfilling federal contracting goals. We are registered in SAM.gov and prepared to engage.
→ What is a typical project timeline for a proof-of-concept?
A typical proof-of-concept to demonstrate feasibility on your data takes between 8 to 12 weeks. This includes setting up a secure environment, ingesting a sample dataset, tuning a baseline model, and deploying a simple user interface for evaluation. This allows stakeholders to validate the approach and measure potential impact before committing to a full-scale production deployment.
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