Capability · AI & Machine Learning
Federally responsible AI — engineered for the mission, governed for the audit.
PRNX builds production-grade AI systems for federal agencies: generative AI assistants, retrieval-augmented generation against agency knowledge bases, predictive ML pipelines, and computer vision. Every system is delivered against the NIST AI Risk Management Framework, OMB M-24-10, and the agency's own AI use-case inventory — so the work that ships is the work that gets adopted.
Outcomes
What you should expect from us.
We frame every engagement around the agency outcome that justifies it. Below are the outcomes we are accountable for in this capability area.
- Production AI systems aligned to the NIST AI Risk Management Framework, with documented impact assessments and risk tiering.
- Retrieval-augmented generation (RAG) over authorized agency content — with citation-level traceability the analyst and the auditor can both follow.
- ML model lifecycle (MLOps) instrumented for drift, bias, and performance — not retrofitted after deployment.
- Generative AI pilots that move past the demo: scoped, evaluated, accredited, and integrated into the workflow that justified them.
- Responsible AI governance written into the SDLC, not the marketing deck — including red-team review, model cards, and human-in-the-loop where the impact level requires it.
How we engage
A delivery model that respects your audit trail.
Use case viability and risk tiering
We start with the decision the AI is meant to influence, the data that supports it, and the impact level under OMB M-24-10. Use cases that fail viability or risk review do not move forward.
Model and governance architecture
Model selection, prompt and retrieval architecture, evaluation harness, and governance controls — designed against the system's authorization boundary and AI risk classification.
Build, evaluate, accredit
Training, fine-tuning, or RAG implementation with rigorous evaluation. Section 508 accessibility, security, and privacy reviews run in parallel with model development.
MLOps and responsible monitoring
Drift detection, bias monitoring, retraining cadence, prompt-injection telemetry, and incident response — operated as part of the system, not as an afterthought.
Tools we operate fluently
The toolchain behind the work.
We are platform-pragmatic: we recommend what your environment, your authorization boundary, and your existing investments support.
Spotlight engagement
RAG assistant for authorized technical doctrine retrieval
PRNX delivered a retrieval-augmented generation assistant against the component's authorized doctrine corpus. The system was authorized on the existing system boundary, surfaces citation-level provenance for every answer, and is governed under the agency's NIST AI RMF playbook. Average doctrine lookup time fell by 82% in the first quarter of deployment.
Related capabilities
Most engagements span more than one discipline.
Ready when you are
Bring us in early. We deliver predictably.
Whether you have an active RFP, a recompete on the horizon, or a program in trouble, our capture team responds within one business day.