AI & Machine Learning

Yes — with the right architecture. We ground generative systems in your authoritative data through retrieval-augmented generation, add guardrails and human-in-the-loop review where stakes demand it, and gate every release with evaluation. The result is AI that cites its sources and stays inside policy.

Yes. We design for the environment your data requires: FedRAMP-authorized clouds, agency VPCs, and fully disconnected networks. Where hosted model APIs aren't an option, we run open-weight models on your infrastructure — inference, retrieval, and monitoring all inside the boundary.

No. Your data remains yours — we never train shared or third-party models on agency data. Fine-tuning and retrieval indexes are built inside your environment, under your control, and we document data handling to support your records and privacy requirements, including CUI.

We structure delivery around the framework's Govern, Map, Measure, and Manage functions: documented risk context, measurable evaluation criteria before build, and monitoring plus incident response after launch. The artifacts we produce — model cards, evaluation reports, drift baselines — feed directly into your authorization package and OMB AI reporting.

Every deployment ships with monitoring for data drift, prediction quality, and business outcomes, plus retraining pipelines that are versioned and reproducible. Degradation gets caught by alerts, not by end users — that operational backbone is our MLOps practice.

We start with the mission decision the model should improve, then assess your data honestly — most AI projects fail on data, not algorithms. A typical first phase delivers a scoped proof-of-value with evaluation criteria agreed up front, followed by a production path with security and compliance designed in.