AI for Cleared Environments. No Hype, Just Mission-Ready Code.
We are a veteran-owned firm building on-prem AI systems for defense and aerospace contractors in the Springs. Secure RAG on technical docs, anomaly detection on telemetry, and auditable decision support.
Generic AI tools fail when mission data and security are non-negotiable.
Commercial AI platforms are built for public data, not mission-critical operations. They send your data to third-party servers, can't be air-gapped, and choke on specialized telemetry or classified document formats. For NORAD, Space Force, and Fort Carson contractors, this isn't just an inconvenience; it's a non-starter. You need systems that operate on your network, understand your data, and provide auditable outputs without compromising security protocols. Off-the-shelf solutions create more risk than they solve, leaving operators with unreliable tools when the stakes are highest.
- Cloud-based LLMs refuse to run on-prem or in air-gapped networks.
- Standard models lack the domain knowledge for aerospace telemetry or military acronyms.
- Data exfiltration risk from commercial APIs is unacceptable for CUI/classified data.
- Lack of fine-grained audit logs makes outputs useless for official C2 workflows.
Deploying Secure, On-Premise AI Models for Mission-Specific Tasks.
- STEP-01
Mission & Data Scoping
We start with your operational objective and data constraints. We identify the exact document sets, sensor feeds, or tactical data streams that the AI will process, ensuring alignment with security protocols from day one.
- STEP-02
Secure Model Selection & Hardening
We select an appropriate open-source model and harden it for on-premise deployment. This includes disabling all external callbacks and logging every transaction for full auditability.
- STEP-03
Domain-Specific Fine-Tuning & RAG
The model is trained on your specific data—technical manuals, sensor logs, intelligence reports—using a Retrieval-Augmented Generation (RAG) pipeline. This ensures outputs are grounded in your facts, not public internet data.
- STEP-04
Integration with Existing Systems
We integrate the AI into your existing command, control, or analysis platforms. This isn't a new dashboard; it's an enhancement to the tools your operators already use, providing decision support where they work.
- STEP-05
Verification & Accreditation Support
We provide the documentation and system logs necessary for the accreditation process. Our systems are built with ATO (Authority to Operate) requirements in mind, simplifying the security validation phase.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load a model to run locally on your hardware, not a cloud API.
# This ensures data never leaves your network.
model_name = "meta-llama/Llama-2-7b-chat-hf"
# Reference a local model directory to avoid external calls
# local_model_path = "/path/to/your/local/model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto" # Automatically use available GPU
)
# Example query using a secure, local pipeline
prompt = "Summarize key telemetry anomalies from sensor feed 7B based on MIL-STD-1553."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(output[0], skip_special_tokens=True))Example of loading an open-source LLM for secure, on-premise inference. This avoids sending sensitive data to external APIs.
Field FAQ.
→ How do you handle classified or CUI data?
We operate on a 'zero trust' principle for your data. Our systems are designed for deployment inside your accredited network boundaries, including SIPRNet or JWICS enclaves. We do not require data to be exfiltrated. Our personnel possess the necessary clearances to work in these environments, ensuring that both the technology and the team meet stringent federal security requirements from the start.
→ What does 'on-premise inference' actually mean for us?
It means the AI models run on your hardware, inside your data center or secure cloud, completely disconnected from the public internet. Unlike services that require API calls to external servers, our solutions give you full control over the data and the computation. This is critical for maintaining data sovereignty, ensuring low latency for real-time operations, and passing security audits required for defense work.
→ Can your systems be audited for command and control (C2) use?
Yes. Every query, data source, and model-generated response is logged with immutable, timestamped records. This provides a complete, verifiable chain of custody for decision-making processes. We design our logging to meet the rigorous standards required for C2 and other mission-critical systems, where traceability is not just a feature but a fundamental requirement for operational use.
→ How is this different from just using a commercial AI chatbot?
Commercial chatbots are trained on public internet data and are not optimized for specialized domains like defense or aerospace. Our systems use Retrieval-Augmented Generation (RAG) to ground the AI's responses in your specific, authoritative documents—like technical orders, intelligence reports, or engineering schematics. This prevents hallucinations and ensures the answers are relevant, accurate, and based on your controlled information.
→ What makes a Service-Disabled Veteran-Owned Small Business (SDVOSB) a good choice for this work?
As an SDVOSB, we have a deep, personal understanding of the mission and the operator's needs. We've lived the environment our systems are built for. This background informs our engineering—we build practical, durable tools, not science projects. Furthermore, our SDVOSB status provides federal agencies and prime contractors with a streamlined, direct-award procurement path, simplifying the contracting process significantly.
→ What kind of hardware is required to run these models on-premise?
Hardware requirements depend on the model size and performance needs. A typical RAG system for document analysis can run effectively on a single server with one or two modern GPUs like the NVIDIA A100 or H100. For more demanding tasks, a multi-GPU server may be necessary. We conduct a hardware assessment as part of our initial scoping to define a bill of materials that meets your specific mission and budget constraints.
→ How long does it take to get a system operational?
A proof-of-concept system, focused on a specific dataset like a set of technical manuals, can typically be fielded in 8 to 12 weeks. This includes model selection, data pipeline construction, RAG implementation, and a basic user interface. A full production integration into an existing system of record, including hardening and accreditation support, is a more involved process, typically taking 4 to 6 months.
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