Federal AI & ML engineering
Deploying AI in government is rarely a modeling problem — it is a trust problem. We engineer machine learning systems for the U.S. Department of Veterans Affairs, the Department of Defense, and other agencies so that a model's output can be put in front of a veteran, a warfighter, or a benefits decision and stand up to an Inspector General.
- Model integration & evaluation — calibration, explainability, and confidence-tiered decisions, including the ability for a model to say "I don't know."
- MLOps & lifecycle — versioning, reproducibility, and monitored deployment on FedRAMP-authorized cloud.
- Drift monitoring — watch input and output distributions and flag, with evidence, before performance erodes.
- Large data & image pipelines feeding models, with validation at the door.
- Applied statistics & data science — statistician-led, so measures are computed and interpreted correctly.
Governed to the NIST AI Risk Management Framework
Our AI work aligns to the NIST AI Risk Management Framework — map, measure, manage, govern — and to VA's own AI strategy direction. Every automated decision is logged as architecture, so lineage, access, and rationale are reconstructable months later for an auditor or program manager. This is auditability by construction, not a report added at the end.
It also matches where VA is going: VA has stated that human-in-the-loop oversight is essential because an AI decision can be technically accurate yet unsafe without business context. Our systems are built for exactly that — human-gated on uncertainty.
Why a veteran-owned machine learning company
VAERESOURCE is SBA-certified as an SDVOSB, VOSB, and WOSB, carrying VA's Veterans First preference (PL 109-461). Delivery is principal-led by a U.S. Army veteran with an active DoD Secret clearance. Security posture: CMMC Level 1 (self), NIST SP 800-171 score 88, delivery on AWS GovCloud / Azure Government, Section 508-conformant. We represent our capabilities and experience accurately, every time.
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