CHARLESTON, SC OPERATIONS

AI Integration for NIWC Atlantic & C4ISR Programs

As an SDVOSB, we build secure, compliant AI tools for federal contractors. We deploy RAG systems and LLM-powered workflows on-premises or in GovCloud to handle sensitive program data.

Veteran-Owned SDVOSB
001 / 005 Field Conditions

Off-the-shelf AI tools can't handle secure, air-gapped program data.

Situation

Charleston's federal contractors, especially those supporting NIWC Atlantic, face a unique data challenge. Commercial AI tools are non-starters for CUI or classified data. Teams are left manually sifting through terabytes of technical manuals and program records—a slow, error-prone process. The risk of data spillage is too high, and generic models lack the specific context of complex C4ISR systems. This stalls modernization and keeps valuable institutional knowledge locked in static documents, inaccessible to engineers and analysts who need it.

  • Public LLM APIs are security risks for Controlled Unclassified Information (CUI).
  • Manual data extraction from PDFs and technical documents is slow and inaccurate.
  • Generic AI models lack the domain-specific knowledge for complex C4ISR systems.
  • Lack of audit trails makes it impossible to verify AI-generated outputs for compliance.
100%
On-Premises or GovCloud Deployment
SDVOSB
Prime & Subcontractor Federal Partner
4-8 Weeks
Typical Timeline for RAG Pilot System
002 / 005 Operational Approach

We deploy secure, auditable AI systems for mission-critical workflows.

  1. STEP-01

    Establish Secure Data Environment

    We scope mission requirements and data sensitivity to determine the correct deployment target—on-premises servers or a secure GovCloud instance. This step ensures data never leaves your control and meets all security protocols.

  2. STEP-02

    Build a Private Knowledge Corpus

    We use Retrieval-Augmented Generation (RAG) to index your program-specific technical manuals, CONOPS, and historical data. The LLM learns from your documents only, ensuring relevant, verifiable answers without external data calls.

  3. STEP-03

    Implement Human-in-the-Loop Verification

    For mission-critical outputs, we build workflows that require operator review and approval. The system flags low-confidence answers and requires a human to sign off before the information is used in reports or engineering tasks.

  4. STEP-04

    Deploy with Full Audit Trails

    Every query, response, and source document is logged. This creates a complete, auditable trail to satisfy program security officers and compliance requirements, showing exactly how the system arrived at an answer.

PYTHON PATTERN 
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFDirectoryLoader

# 1. Load program documents from a secure directory
loader = PyPDFDirectoryLoader("./secure_cui_docs/")
docs = loader.load()

# 2. Split documents into manageable chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)

# 3. Create embeddings using a local, air-gapped model
embeddings = OllamaEmbeddings(model="llama3")

# 4. Build a vector store from documents
# This index runs locally, never calls an external API
vectorstore = FAISS.from_documents(documents=splits, embedding=embeddings)
vectorstore.save_local("faiss_c4isr_index")

# Now the RAG system can query this local index for answers.

Example Python script for creating a local, air-gapped RAG index from program documents. This process ensures sensitive data is never exposed to public APIs.

003 / 005 Common Questions

Field FAQ.

→ How does this work with data that's classified or CUI?

Our entire approach is built for sensitive data. We deploy models and data indexes on your hardware, inside your network, or within a secure GovCloud environment. There are no external API calls, so your data never leaves your security boundary. This on-premises or private cloud deployment is essential for handling CUI, FOUO, and supporting systems that process classified information.

→ What kind of models do you use? Are they secure?

We select models based on mission requirements, prioritizing open-source options that can be run locally. Models like Llama 3 or Mistral can be deployed on-premises, completely air-gapped from the internet. This avoids reliance on third-party APIs and gives you full control over the model weights and data processing, which is critical for security and compliance.

→ Our engineers are skeptical of AI. How do you ensure accuracy?

Skepticism is healthy. Our systems are built on Retrieval-Augmented Generation (RAG), which means every answer is directly tied to a source document. We display the source citations with each response, so an engineer can immediately verify the original technical manual. We also implement human-in-the-loop workflows for critical tasks, requiring operator sign-off.

→ What does being an SDVOSB mean for contracting?

As a Service-Disabled Veteran-Owned Small Business (SDVOSB), we are eligible for sole-source and set-aside federal contracts. This can significantly streamline the procurement process for government agencies like NIWC Atlantic. It allows program managers to acquire specialized software engineering services directly, reducing acquisition friction and accelerating mission timelines. We are registered in SAM.gov and ready to engage.

→ Can you integrate this AI with our existing software like Jira or Confluence?

Yes. A key part of our work is integrating LLM capabilities into existing workflows. We can build custom connections to systems like Jira for automated ticket analysis, Confluence for intelligent search, or even integrate with Navy-specific systems. The goal is to augment your current tools, not force you to adopt a completely new platform that disrupts established processes.

→ How long does it take to get a pilot system running?

A typical pilot project, focused on a specific document set and use case, can be operational in 4 to 8 weeks. This includes setting up the secure environment, ingesting and indexing the initial data corpus, and deploying a basic user interface for queries. From there, we iterate based on user feedback to expand the system's capabilities.

→ What kind of hardware is required to run these models on-premises?

The hardware requirements depend on the model size and performance needs. For many RAG applications, a single server with a modern GPU like an NVIDIA A100 or H100 is sufficient. We provide detailed hardware specifications based on the selected model and expected user load, ensuring you procure the right equipment for the mission without overspending.

→ Do your engineers have security clearances?

Our team consists of veterans and engineers with experience in the defense sector. Many of our personnel have held clearances or are clearable. For specific contract requirements, we provide cleared engineers to work on-site or in secure facilities, ensuring all personnel handling sensitive information meet the necessary government security standards.

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