Why NIST is different
The National Institute of Standards and Technology is not a typical federal buyer. It is the agency that writes the rules that every other federal AI program has to follow. When an agency cites "AI RMF" in a solicitation, they are citing NIST AI 100-1. When a contracting officer asks about 800-53 controls, cybersecurity framework alignment, or post-quantum readiness, they are asking whether you can speak NIST natively.
For a contractor, that makes NIST doubly valuable. Delivering to NIST is a credential. Delivering anywhere else requires fluency in NIST. Precision Delivery Federal LLC (UEI Y2JVCZXT9HP5, CAGE 1AYQ0, NAICS 541512) is built to operate in that fluency by default, not as an afterthought.
The NIST mission surface
NIST is a non-regulatory agency inside the Department of Commerce. It runs several lines of business that intersect AI and data work:
- Information Technology Laboratory (ITL) — home of the AI Risk Management Framework, the Cybersecurity Framework, SP 800 series, and the National Cybersecurity Center of Excellence (NCCoE).
- U.S. AI Safety Institute (AISI) — stood up at NIST to lead federal work on frontier model evaluation, red-teaming, and safety testing. Engagement with AISI is quickly becoming table stakes for serious federal AI vendors.
- Communications Technology Laboratory (CTL) — 5G/6G, spectrum, measurement.
- Engineering Laboratory, Material Measurement Laboratory, Physical Measurement Laboratory — metrology, where ML is increasingly a tool of the scientific method.
- Technology Partnerships Office — runs NIST's SBIR program and Manufacturing USA coordination.
AI Risk Management Framework — how we operationalize it
NIST's AI RMF is not decorative. For a federal buyer, alignment to RMF is the mechanism by which AI risk becomes documentable, auditable, and defensible. We treat the four functions as concrete engineering artifacts:
Govern
Written AI governance policies, role definitions (model owner, security officer, evaluator), incident response playbooks, supplier risk assessment templates.
Map
System context documentation, stakeholder mapping, intended-use statements, misuse taxonomies, out-of-scope declarations written into model cards.
Measure
Evaluation harnesses covering accuracy, fairness, robustness, security. TEVV plans aligned to AISI guidance. Red-team logs. Drift monitoring in production.
Manage
Deployment gates, rollback procedures, continuous monitoring, human-in-the-loop requirements, sunset criteria. Managed like a regulated system, not a science project.
On top of AI RMF 1.0, we operationalize the Generative AI Profile (NIST AI 600-1), which maps 12 GenAI-specific risk categories — confabulation, harmful bias, data privacy, environmental impact, information integrity, information security, intellectual property, CBRN misuse, dangerous or violent content, human-AI configuration, value chain and component integration, and obscene, degrading, or abusive content. Every LLM system we ship has the Profile's risk categories answered, not hand-waved.
Post-quantum cryptography — the migration agencies can't postpone
In August 2024, NIST finalized three post-quantum cryptographic standards: ML-KEM (FIPS 203) for general encryption, ML-DSA (FIPS 204) for digital signatures, and SLH-DSA (FIPS 205) as a stateless hash-based backup. A fourth, HQC, is scheduled for 2027 as a code-based alternative. National Security Memorandum 10 sets a 2035 migration deadline for federal systems.
Most agencies have not started. That is the gap. We support PQC migration work in three modes:
- Cryptographic inventory — static and dynamic analysis of codebases and infrastructure to identify where classical public-key crypto is used. Build the bill-of-materials NSM-10 demands.
- Migration planning — prioritization of systems by data sensitivity, threat window, and interoperability dependencies. Hybrid scheme rollout plans.
- Implementation support — integrating FIPS 203/204/205 libraries into federal software, testing against AISI/CMVP validation paths, documentation that survives audit.
AISI and the new federal evaluation bar
The U.S. AI Safety Institute, hosted at NIST, has rapidly become the center of federal AI evaluation practice. For vendors, that means the old pattern — "we tested it internally, here's a demo" — is not sufficient for any serious federal AI deployment. The new bar is:
- Structured TEVV plans — test, evaluation, validation, verification — with reproducible harnesses.
- Red-team transcripts — documented adversarial testing, including prompt injection, data extraction, jailbreak attempts.
- Bias and fairness measurement — quantitative, not narrative.
- Robustness testing — distribution shift, adversarial inputs, tool-use failure modes.
- Safety cases — written arguments that the system is fit for purpose, supported by evidence.
We build systems so that TEVV artifacts emerge as a byproduct of development, not a last-minute scramble before deployment.
Capabilities mapped to NIST priorities
- Agentic AI — multi-agent systems with governance hooks at every step. Tool-call logs, decision traces, human override surfaces. The substrate of AISI-aligned evaluation.
- Machine Learning — from a Kaggle Top 200 data scientist. Evaluation-first methodology. Model cards and data sheets shipped with every deliverable.
- Cybersecurity and DevSecOps — CSF 2.0 mapping, 800-53 control implementation, SSDF (SP 800-218) software supply chain practices, SBOM generation.
- Cloud Infrastructure — FedRAMP-aligned reference architectures, FIPS 140-3 validated crypto, PQC-ready deployments.
- Data Engineering — governed data pipelines with audit trails, lineage, and classification handling.
NIST SBIR — small but strategic
The NIST SBIR program is smaller than DoD's but strategically important. Topics cluster around measurement science, cybersecurity, AI evaluation tooling, and manufacturing. Our proposal library currently covers 3 NIST topics, with additional topics tracked in our opportunity pipeline. With SBIR reauthorized through September 30, 2031 and new Strategic Breakthrough Phase II pathways for agencies over $100M in extramural R&D (NIST is smaller but its programs are high-leverage), the pipeline is meaningful.
Past performance and honest positioning
We will not hallucinate NIST contracts we have not won. Our confirmed federal past performance is SAMHSA (HHS) — a production machine learning system under full ATO, plus federal health IT lakehouse work. For NIST specifically, we are targeting and pursuing opportunities through the SBIR pipeline, NCCoE collaboration tracks, and potential subcontracting roles with NIST primes. What transfers is the engineering discipline that NIST itself prescribes — control-mapped, evaluation-rigorous, documentation-heavy delivery.
Vehicles, NAICS, and engagement
- Primary NAICS 541512 — Computer Systems Design Services.
- Adjacent NAICS — 541511, 541519, 541690 (Scientific and Technical Consulting — a strong fit for NIST), 541715 (R&D in Physical, Engineering, and Life Sciences).
- Vehicles we're positioned for — NIST SBIR, NCCoE community-of-interest tracks, GSA MAS for NIST task orders, and subcontracting to primes on NIST IDIQs.
If you are a NIST program office, an AISI-aligned research team, or a prime looking for an AI/ML-specialized small business subcontractor fluent in NIST's own frameworks, email [email protected].