Why EPA is an AI/ML customer
The Environmental Protection Agency is a data-intensive regulator. Every major EPA program generates substantial data flows: Clean Air Act emissions data, Clean Water Act discharge monitoring, RCRA and CERCLA site data, TSCA chemical data, drinking water compliance, and a growing satellite- and sensor-derived measurement layer. Translating those flows into enforcement decisions, regulatory rulemakings, and public health protection is increasingly AI/ML work.
Precision Delivery Federal LLC (UEI Y2JVCZXT9HP5, CAGE 1AYQ0, NAICS 541512) targets the AI, ML, and data engineering workloads that sit behind EPA's scientific and regulatory mission — delivered with the reproducibility and documentation EPA's Scientific Integrity Policy requires.
EPA offices we target
- Office of Research and Development (ORD) — EPA's science arm, with centers covering computational toxicology, environmental solutions, public health research, and environmental measurement. Home of much of EPA's SBIR topic inventory.
- Office of Air and Radiation (OAR) — National Ambient Air Quality Standards, the Clean Air Markets Division (emissions trading and CEMS data), the Greenhouse Gas Reporting Program, and radiation protection programs.
- Office of Water (OW) — Safe Drinking Water Act, Clean Water Act programs, the Integrated Reporting system (303(d), 305(b)), and the ECHO compliance platform.
- Office of Enforcement and Compliance Assurance (OECA) — ECHO, inspection targeting analytics, case selection ML.
- Office of Chemical Safety and Pollution Prevention (OCSPP) — TSCA new chemicals review, risk evaluations, QSAR modeling.
- Office of Land and Emergency Management (OLEM) — Superfund, RCRA, emergency response data.
- Office of Mission Support (OMS) — IT modernization, data platform investments.
Emissions monitoring and GHG ML
Continuous emissions monitoring (CEMS), GHGRP reporting, and the expanding satellite/airborne methane detection ecosystem generate some of EPA's most valuable ML workloads. Our fit:
- CEMS data pipelines — ingestion, QA/QC, gap-filling ML, anomaly detection.
- Methane detection and attribution — ML over MethaneSAT, TROPOMI, Carbon Mapper, and airborne datasets. Source attribution and leak prioritization.
- GHGRP analytics — facility-level emissions analysis, cross-sector comparisons, outlier detection.
- Inventory ML — uncertainty quantification and source apportionment for the National Emissions Inventory.
Air and water quality ML
- Air quality — fusion of AirNow, PurpleAir-class sensor networks, and satellite products for high-resolution AQ maps. ML for exceedance forecasting.
- Surface water — ML over WQX / STORET data, impairment analytics, HAB forecasting, stormwater prediction.
- Drinking water — SDWIS violation analytics, lead service line inventory ML from imagery and records.
Compliance and enforcement analytics
ECHO is one of the federal government's signature compliance data platforms. EPA has invested in inspection targeting analytics for years; we bring modern ML practice to that work:
- Risk-based inspection targeting across CWA, CAA, RCRA.
- LLM-assisted document review on inspection reports, complaints, permits.
- Anomaly detection on self-reported compliance data.
- Transparent model documentation that withstands regulated-entity challenge.
Environmental justice analytics
EJScreen and successor cumulative impact tools translate demographic, health, and environmental data into screening products. Done well, this is rigorous ML with transparent methodology; done badly, it undermines agency credibility. We deliver the rigorous version — subgroup fairness measurement, sensitivity analysis, documented data provenance, and outputs that withstand community and regulated-entity review.
Scientific integrity and reproducibility
EPA's Scientific Integrity Policy and FAIR data commitments shape every deliverable. We build:
- Reproducible pipelines — containerized, parameterized, version-pinned.
- Open code where permitted — EPA increasingly prefers open code for scientific products.
- Data documentation — data sheets, model cards, provenance tracking.
- Peer-review-ready outputs — methodology documents that can survive journal review.
EPA SBIR
EPA runs an SBIR program focused on environmental technology, often including sensor networks, analytics for environmental monitoring, and decision support. With SBIR reauthorized through September 30, 2031 under the April 2026 law, EPA SBIR is a stable channel. We target topics where AI/ML is the core differentiator.
Capabilities mapped to EPA priorities
- Machine Learning — time-series, spatial, satellite image ML, anomaly detection — evaluation-first.
- Data Engineering — lakehouse for emissions, water quality, ECHO-adjacent data.
- Agentic AI — LLM-assisted document review, regulatory analysis, operator productivity.
- Cloud Infrastructure — FedRAMP Moderate-aligned, scientific computing-ready.
- Cybersecurity and DevSecOps — 800-53 mapping, secure scientific computing.
Past performance and honest positioning
Our confirmed federal past performance is SAMHSA (HHS) — production ML, full ATO, plus federal health IT lakehouse work. The engineering discipline translates: reproducibility, governance, documentation. For EPA specifically, we are targeting and pursuing work through EPA SBIR, ORD research opportunities, and subcontracting to EPA primes.
Vehicles and NAICS
- Primary NAICS 541512. Adjacent: 541511, 541519, 541620 (Environmental Consulting), 541715 (R&D), 541690.
- Vehicles — EPA SBIR, GSA MAS task orders, EPA IDIQs, subcontracting to EPA primes on ECHO, CROMERR, and modernization programs.
If you are an EPA program office or a prime looking for an AI/ML-specialized small business subcontractor, email [email protected].