We build ITAR-aware AI systems for the Colorado aerospace corridor, from private LLMs on your own network to predictive maintenance models and automated technical documentation.
Book a discovery callColorado is the second largest aerospace economy in the United States. Lockheed Martin Waterton, Ball Aerospace, Sierra Space, L3Harris, Raytheon, United Launch Alliance, Maxar, and hundreds of suppliers across Denver, Boulder, Colorado Springs, and Aurora. Every one of these operations has the same AI constraint: most AI services cannot touch the work. Data is controlled. Compliance is non-negotiable. SaaS AI products that phone home to a third party LLM are a non-starter. That is exactly the work we are built for.
The five pains we hear most often before we build.
You cannot send controlled technical data to a public LLM. You cannot run a workflow through a service that processes tokens outside your boundary. Most AI vendors fail the first compliance review.
Sensor data, maintenance logs, inspection reports, flight test data, all in different formats across Foundry, Maximo, bespoke databases, and paper records. You have the data to predict failures, but pulling it into a model is a project on its own.
Proposal writing, configuration documentation, test reports, ECP packages. Senior engineers spend thirty to fifty percent of their week on words, not work.
You know who you buy from. You do not always know who they buy from. A disruption deep in the chain shows up as a missed delivery with no warning.
High-precision parts require machine vision to catch what humans miss. Commercial vision APIs do not handle defense parts well, and they are not hosted where you can send the images.
The specific services that map to each pain.
Language models trained on your engineering archive, deployed on your own infrastructure or in a SCIF-eligible environment. ITAR-safe by design. You own the weights, the training pipeline, and the inference server. Nothing leaves your perimeter.
Learn more →Predictive maintenance models trained on your sensor and maintenance history. Defect detection models running on-prem on your own vision pipeline. Anomaly detection on flight test data. Every model, every dataset, every training run lives inside your network.
Learn more →On-prem GPU orchestration, air-gapped model serving, CI/CD for ML that runs inside your compliance boundary. Monitoring and automated retraining without any outbound calls.
Learn more →Supplier chain integration layers bridging DOD systems, ERP systems, and your internal tooling. Long-form technical document generation wrapped in your existing review workflow. Everything purpose-built to your compliance regime.
Learn more →Yes when deployed correctly. Our private LLM and on-prem ML patterns are designed so controlled data never leaves your network. The inference server runs inside your boundary, weights are stored on your infrastructure, and there are no outbound API calls. We work with your compliance officer on the specific deployment architecture before any sensitive data touches the system.
Not currently. For work that requires cleared personnel or a CMMC-certified provider, we partner with a cleared integrator. We can still design the system and train your internal team to operate it, and the cleared partner handles the sensitive execution phases.
Yes. The deployment artifact for a private LLM or custom ML system is designed to install without internet access. We deliver full offline installation packages, and your team runs the install inside the secure environment with us on standby for remote troubleshooting over whatever channel your compliance regime allows.
Predictive maintenance on sensor-rich fleets, defect detection on vision pipelines, document generation and review for proposals and technical reports, anomaly detection on flight test data, and knowledge retrieval over engineering archives. We would not recommend AI agents for anything with flight safety implications today.
Specific client stories are covered under NDA on the discovery call. Our public portfolio at /products shows the AI systems we have built and the patterns apply to aerospace engineering directly.
Most aerospace clients start with a two week discovery sprint that produces a scoped architecture, a compliance review, a risk register, and a phased build plan. Then a six to sixteen week build, milestone billed, demoed every two weeks. That pattern clears compliance and procurement cleanly.
Ready to ship AI that respects ITAR, EAR, and your compliance boundary? Book a thirty minute discovery call, free for new clients, and we will scope it inside your constraints.
Book a discovery callServices that pair well with what you just read.
Custom language models on your data, your infrastructure.
Learn more →Prediction, classification, anomaly detection and vision.
Learn more →MLOps, GPU orchestration and model serving at scale.
Learn more →Bespoke automation systems, APIs and cloud infrastructure.
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