AI that earns trust, not just accuracy.

AI/ML & Data Analytics

SimIS applies artificial intelligence, machine learning, and big data analytics as a core competency: building, integrating, and evaluating data-driven capabilities for defense and federal missions rather than reselling someone else's.

The work is grounded in applied research. SimIS operates its own Intelligent Robotics and Autonomous Systems (IRAS) Laboratory and maintains an active research and development partnership with the Virginia Modeling, Analysis and Simulation Center (VMASC), keeping the practice hands-on rather than theoretical.

AI/ML runs through the rest of the portfolio: perception and autonomy in unmanned systems, data generation and analysis in modeling and simulation, and analytics that turn test and evaluation data into acquisition decisions. If your requirement touches data, the entry points are many; the team is the same. The MDA SHIELD and USV Family of Systems IDIQs already carry this work to customers.

Building an AI/ML capability and not sure how to prove it's safe to field? That's exactly the conversation to start.

Cutting-Edge AI, Built for Your Mission

SimIS operates its own large language models on internal infrastructure, hardened and running in production today rather than sitting in a lab. The same environment carries SimIS's own work and its customers' missions.

Nothing routes through an outside cloud, and nothing leaves the environment your team controls. That is what separates an AI capability you can field from one you are only renting.

We're happy to walk through how this environment is built and what standing one up would look like for your program.

Ask Us Anything

Where AI/ML Lives in the Mission

Sensor to model to decision. SimIS runs all three stages because it builds the platform the sensor sits on, and that chain is running in the field today.

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Data becomes a model becomes a decision, all inside the platforms SimIS already fields.

Most AI/ML pitches stop at the model. Ours starts with the sensor and ends with a decision someone actually signs off on, because a model that never leaves the lab isn't worth building. The IRAS Laboratory and our VMASC partnership exist to keep that pipeline honest: every stage runs against real telemetry, real simulation output, and real test events, not synthetic data dressed up to look convincing.

Autonomy You Can Already See Working

Machines that decide, not just execute. A Human Type Target moves itself across rugged ground without a track or an operator, and it judges what happened when a round goes past. Both of those decisions ride onboard.

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Our Human Type Targets run the same loop on every engagement. Sensors register a hit or a near miss, onboard behavioral logic evaluates what that engagement actually was, and the target responds in real time while LOMAH records where the round went. What comes out is measurable training data feeding the after-action review, not just a target that moved. That loop runs today on platforms fielded with special operations units, police departments, and allied militaries, and the same perception work reaches the water through the USV Family of Systems IDIQ, in RiverScout and SAMS.

AI Assurance: Earned at Build Time

AI assurance isn't a checkbox added after the model ships. Every program is about to face the same question, and it isn't whether the model works. It's whether you can prove it holds up when an evaluator, an auditor, or an adversary goes looking for the weak seam.

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Two disciplines, one assured result.

That's exactly where our two practices already meet. Our cybersecurity team knows how to stress a system until it breaks. Our compliance team knows how to document a program so it survives scrutiny. Put both on an AI/ML platform, aligned to the NIST AI Risk Management Framework and ISO 42001, and the model gets fielded with the same rigor as everything else we build, not audited after the fact. A CMMI Maturity Level 3 appraisal and a workforce where more than 80 percent hold active clearances back that rigor with independent proof. We run that practice against our own AI systems first. If you need the same assessment against yours, that is work we take on.

Most of the DIB is still treating AI governance as paperwork for later. We're building the practice now, on purpose. The pressure everywhere is to field faster, and the programs that move fastest are the ones that don't have to stop and prove themselves after the fact.

Ask Our AI Anything

On the roadmap: a large language model trained on SimIS's own capabilities, research, and past performance, deployed publicly so anyone can ask it directly what SimIS can deliver.

It is the same internal AI infrastructure described above, pointed outward: a knowledge partner instead of a gatekeeper, answering from what SimIS has actually built rather than what a brochure claims.