Mission-Ready AI for the Tennessee Valley
We build secure, compliant AI and RAG systems for Redstone Arsenal, NASA, and the defense primes. As a veteran-owned firm, we understand the mission.
Commercial AI tools are a liability on federal contracts.
Standard AI solutions send your proprietary or CUI data to third-party servers, creating massive security and compliance risks. They can't parse dense MIL-STD documents, lack auditable trails required for CMMC, and fail inside classified environments. For Huntsville's defense and aerospace contractors, this isn't just an inconvenience—it's a non-starter that jeopardizes contracts and sensitive national security data. Your engineers need tools that work within your secure perimeter, not against it.
- Data spillage of CUI or ITAR information to public AI models.
- Inability to meet CMMC/DFARS audit and logging requirements.
- Generic models hallucinate when processing complex engineering schematics.
- Lack of viable options for air-gapped or on-premise networks.
We deploy AI that respects your security boundary.
- STEP-01
1. Define the Security Perimeter
First, we map your data classification and compliance needs (CMMC, ITAR). We determine the deployment target: on-premise servers, GovCloud, or a hybrid model. Security is the baseline, not an afterthought.
- STEP-02
2. Engineer the Data Pipeline
We connect to your existing data sources—technical manuals, SharePoint sites, CAD repositories. We build a secure RAG pipeline to index this data for the AI model without it ever leaving your control.
- STEP-03
3. Deploy a Vetted LLM
We deploy a suitable model for the mission, whether it's an open-source model like Llama 3 running on your hardware or a secured endpoint like Azure Government OpenAI. The goal is performance without compromising security.
- STEP-04
4. Integrate and Harden the System
The AI is integrated into your team's workflow (e.g., Jira, Teams, custom apps). We implement role-based access controls and comprehensive logging to ensure every query and response is auditable and compliant.
from langchain_community.vectorstores import FAISS
from langchain_openai import AzureOpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain_openai import AzureChatOpenAI
# 1. Set up secure connection to Azure Government
embeddings = AzureOpenAIEmbeddings(
azure_deployment="your-embedding-model-deployment",
openai_api_version="2023-05-15",
)
# 2. Load local, indexed vector store (ITAR/CUI data)
vectorstore = FAISS.load_local("/mnt/secure_storage/faiss_index", embeddings)
# 3. Configure LLM via compliant Azure Gov endpoint
llm = AzureChatOpenAI(
azure_deployment="your-gpt4-deployment-gov",
temperature=0.0,
)
# 4. Create a secure RAG chain
qa_chain = RetrievalQA.from_chain_type(
llm,
retriever=vectorstore.as_retriever(),
return_source_documents=True
)
# 5. Query internal data without exfiltration
question = "What are the torque specs for assembly P/N 78B-42?"
result = qa_chain({"query": question})
print(f"Answer: {result['result']}")
print(f"Source: {result['source_documents'][0].metadata['source']}")A simplified Python script showing a RAG pattern using Azure Government endpoints and a local vector store, ensuring proprietary data is not sent to a public service.
Field FAQ.
→ Can you work with Controlled Unclassified Information (CUI) or ITAR data?
Yes. Our entire approach is built around data security and sovereignty. We design systems using on-premise models inside your network or accredited cloud environments like AWS GovCloud and Azure Government. We architect for least-privilege access and ensure sensitive data is processed entirely within your designated security boundary, meeting federal data handling requirements.
→ How is this different from our engineers just using ChatGPT?
Public tools like ChatGPT send your queries and data to third-party commercial servers, which is a clear violation for handling CUI or proprietary information. Our solutions are private and isolated. They are specifically trained on your internal documentation, providing verifiable, source-cited answers instead of unreliable public knowledge, all while maintaining a strict, auditable security posture.
→ What's a realistic timeline for a pilot project?
A scoped pilot, such as a RAG system for a specific set of technical manuals or program documents, can typically be deployed for initial operational testing within 8-12 weeks. This includes environment setup, data ingestion and indexing, model deployment, and a functional user interface for your team to validate performance and utility.
→ As an SDVOSB, can you engage directly with agencies at Redstone Arsenal?
Absolutely. Our Service-Disabled Veteran-Owned Small Business (SDVOSB) certification provides a streamlined and prioritized contracting vehicle for federal agencies. We are set up to work directly with organizations at Redstone, including AMCOM, MDA, NASA Marshall, and others, helping them meet their small business and mission objectives.
→ What does 'RAG' mean in a practical sense for my team?
Retrieval-Augmented Generation means the AI provides answers grounded in your specific documents. When an engineer asks a question, the system first retrieves the most relevant paragraphs from your approved technical manuals or design specs. It then uses that specific, retrieved text to generate a precise answer, citing the source document and page. It's an expert search tool, not a creative writer.
→ Do you have personnel with security clearances?
As a company founded and operated by veterans, we are accustomed to working in classified environments. We can staff projects with personnel holding active security clearances as required by the contract and statement of work. We handle all staffing and compliance matters with the appropriate discretion and adherence to DoD regulations.
→ How do you handle ongoing maintenance and model updates?
We provide a clear sustainment plan. For on-premise models, this includes a schedule for fine-tuning on new data and managing software updates within your change control process. For cloud-based models, we manage API versioning and perform regression testing to ensure that provider updates do not degrade performance or introduce new risks into your workflow.
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