FDA AI. Regulatory-grade by design.

Why FDA is a distinctive AI customer

The U.S. Food and Drug Administration is the federal government's preeminent regulator of medical products — drugs, devices, biologics, food, tobacco. FDA is also, increasingly, a sophisticated AI consumer. More than 1,000 AI/ML-enabled medical devices have been authorized by the Center for Devices and Radiological Health (CDRH) as of the last published list, and the pace is accelerating. FDA uses its own ML capacity for signal detection, import screening, adverse event triage, and regulatory science. That creates two adjacent markets: FDA as a buyer of AI systems for its own mission, and FDA as a regulator of the AI products sponsors bring in for authorization — work that sponsors often outsource to specialists.

Precision Delivery Federal LLC (UEI Y2JVCZXT9HP5, CAGE 1AYQ0, NAICS 541512) fits both. We deliver AI/ML with documentation discipline that survives FDA review — whether it is our system deployed at the agency or our engineering embedded inside a sponsor's submission.

FDA centers and offices we target

• Center for Drug Evaluation and Research (CDER) — Sentinel Initiative adjacency, real-world evidence, post-market safety analytics, OSE and OSI data work.
• Center for Devices and Radiological Health (CDRH) — Digital Health Center of Excellence, AI/ML-enabled SaMD, PCCP submissions, Total Product Lifecycle oversight.
• Center for Biologics Evaluation and Research (CBER) — gene therapies, vaccines, blood — increasingly ML-instrumented.
• Center for Food Safety and Applied Nutrition (CFSAN) — inspection targeting, recall analytics, import screening ML.
• Center for Tobacco Products (CTP) — scientific review of tobacco product applications, marketing data analytics.
• Office of the Commissioner, Office of Information Technology (OIT) — enterprise IT modernization, data platform investments.
• Oak Ridge Institute / ORISE-based research programs — where AI/ML regulatory science projects often land.

AI/ML-enabled SaMD — FDA's established framework

FDA's approach to AI/ML-enabled Software as a Medical Device is one of the most mature regulatory AI frameworks globally. The key building blocks are:

PCCP support — where small, specialized firms excel

PCCPs are one of FDA's most consequential AI innovations and a place where sponsors frequently need specialized engineering help. We support PCCPs in four modes:

• Modification Protocol design — defining what model changes are in scope, how they are validated, and what performance criteria must be met before deployment.
• Performance monitoring architecture — the telemetry, drift detection, and subgroup performance tracking that the plan commits to.
• Impact assessment — the documented risk analysis tying modifications to potential clinical and safety implications.
• Submission-ready documentation — artifacts formatted to meet FDA's submission expectations.

CDER and Sentinel-adjacent analytics

CDER operates some of the most sophisticated real-world data and signal detection capacity in the federal government. Sentinel, BEST (Biologics Effectiveness and Safety), and the Catalyst initiative all need modern ML. Our fit:

• Signal detection ML on claims and EHR-derived data in distributed-data environments.
• LLM-assisted case narrative triage for FAERS and adjacent adverse event data.
• Real-world evidence pipelines that preserve privacy while enabling federated analytics.
• Document-AI for regulatory review — summarization, cross-reference, and search over drug application corpora.

CDRH Digital Health and import screening

CDRH's Digital Health Center of Excellence is an active procurer of AI-related services. CFSAN and the Office of Regulatory Affairs also use ML-driven import screening to target inspections efficiently. We deliver operator-facing tooling, anomaly detection on import data, and review-support LLM systems that improve throughput without sacrificing decision quality.

Capabilities mapped to FDA priorities

• Machine Learning — evaluation-first ML, subgroup performance analysis, drift detection, from a Kaggle Top 200 data scientist.
• Agentic AI — LLM tooling for document-heavy regulatory workflows. Every agent step audited and reviewable.
• Data Engineering — lakehouse architectures for regulatory data, distributed-data federation, CDRH/CDER adjacency.
• Cloud Infrastructure — FedRAMP-aligned, FISMA High-ready where required, PII and PHI controls built in.
• Cybersecurity and DevSecOps — HIPAA, 800-53 High, medical device cybersecurity alignment.

Past performance and honest positioning

Our confirmed federal past performance is directly relevant: SAMHSA (HHS) — production ML under ATO on federal health data. The engineering discipline — PHI handling, 800-53 High controls, model governance, audit trails — transfers directly to FDA scope. For FDA specifically, we are targeting and pursuing work via FDA-adjacent HHS/NIH SBIR pipelines, subcontracting to FDA primes, and direct sponsor-side engagements where our engineering supports AI/ML SaMD submissions.

Vehicles and NAICS

• Primary NAICS 541512. Adjacent: 541511, 541519, 541690, 541715 (R&D), 621999 (Other Health Services where relevant).
• Vehicles — FDA-adjacent HHS SBIR, GSA MAS task orders, FDA IDIQs (including data modernization vehicles), direct sponsor engagement for AI/ML SaMD submission support.

If you are an FDA program office, a prime on an FDA IDIQ, or a device/drug sponsor looking for AI/ML engineering and submission support, email [email protected].