From mandate to mission. Responsible AI inside the agency.
Black Fox helps federal agencies move from AI policy to fielded capability. We stand up the governance, data foundations, model deployments, and workforce enablement that turn an executive directive into a measurable program outcome — inside FedRAMP and authority-to-operate boundaries, not around them.
Each engagement combines a small senior team with the systems, tooling, and partner network the mission demands.
Use-case portfolios, risk classification, and AI governance boards aligned to OMB M-24-10, the NIST AI RMF, and agency-specific directives. Decisions are documented, auditable, and reviewable by Inspectors General.
Authoritative-source identification, data labeling, lineage, and access controls that make models defensible. We close the data gap before we deploy a model, not after.
Secure deployment of foundation, predictive, and document-processing models on FedRAMP High, IL5, and IL6 platforms with monitoring, drift detection, and rollback as runbook — not slideware.
Role-based AI literacy, prompt-engineering enablement, and human-in-the-loop process design so the people who own the mission stay in the loop and in command.
Map mandates, mission outcomes, and risk tolerance to a short list of high-value AI use cases that can be defended in front of leadership and oversight.
Stand up the data, governance, and platform substrate — inside the ATO boundary — before any model touches production.
Deploy production models with monitoring, red-team evaluation, and human-in-the-loop checkpoints. Measure outcome lift against a documented baseline.
Hand operations to agency owners with playbooks, dashboards, and a 90-day shadow period. Empower the workforce to run the capability without us.
A representative slice of recent ai agency implementation engagements across local, state, federal, quasi-government, and private clients. Signed past-performance references — including direct contracting officer phone numbers — are furnished on qualified inquiries.
Established the Chief AI Officer governance structure, AI use-case inventory, and risk-classification rubric aligned to OMB M-24-10; produced first annual AI use-case report on schedule.
Deployed a human-in-the-loop document-understanding pipeline that cut adjudication review time by 38%; included monitoring, drift detection, and an evaluation harness reviewable by the IG.
Identified and labeled authoritative sources, established lineage and access controls inside an IL5 boundary, and produced an evaluation-ready dataset for two operational AI use cases.
Fielded a foundation-model summarization capability for 1,200 daily resident calls; reduced average wrap-up time by 27% with documented bias and accuracy thresholds.
Role-based AI literacy curriculum for 3,400 staff plus an applied prompt-engineering bootcamp for 180 power users; followed by a measurable increase in approved internal use cases.
Designed and deployed a tabular ML model on FedRAMP-equivalent infrastructure; precision improved 22% over the legacy ruleset with full model-card documentation and quarterly red-team review.
Tell us what you are trying to accomplish. We will tell you, in writing, whether we are the right team and how we would attack it.
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