NEPA AI Assistant Strategic Plan AI-Powered NEPA Advisor - SBIR Phase I Strategic Plan for Groundbreaker Solutions LLC AirForce EcoIntel: AI-Powered NEPA Advisor; Topic: AF254-0809 – Use of Artificial Intelligence / Machine Learning (AI/ML) Applied to the National Environmental Policy Act (NEPA) Process; Company: Groundbreaker Solutions LLC; Principal Investigator: Jason L. Lind; SBIR Phase I Feasibility Study - 6 Month Program; Proposal Reference: F254-0809-0105; Extracted by Claude AI from proposal document Groundbreaker Solutions LLC GBS urn:uuid:a1b2c3d4-e5f6-7890-abcd-ef1234567890 U.S. veteran-owned small business specializing in AI-driven systems engineering for federal clients, particularly within DoD and mission-critical environments Jason L. Lind Principal Investigator - Founder and lead technologist with 15+ years of software architecture and AI-driven systems engineering experience Air Force Civil Engineer Center (AFCEC) Primary stakeholder and end user for NEPA environmental review processes at Air Force installations Council on Environmental Quality (CEQ) Regulatory authority establishing regulations and guidance for NEPA compliance (40 C.F.R. Parts 1500-1508) Department of Defense (DoD) SBIR program sponsor seeking AI/ML solutions for environmental compliance processes Groundbreaker Solutions envisions a future where AI-assisted NEPA processes dramatically accelerate mission-critical Air Force projects while ensuring thorough environmental compliance, legal defensibility, and transparent public participation—transforming environmental review from a bottleneck into a strategic enabler of national defense readiness. urn:uuid:f6a7b8c9-d0e1-2345-f123-456789012345 To establish the feasibility of an AI/ML-driven toolkit that streamlines the NEPA environmental review process for the Air Force by integrating advanced AI methods in a secure, policy-aware system that assists AFCEC staff and stakeholders in preparing and reviewing NEPA documents with improved speed, consistency, and compliance. urn:uuid:a7b8c9d0-e1f2-3456-1234-567890123456 Transparency Every AI-generated statement traces back to authoritative NEPA provisions, CEQ guidelines, or verified data through provenance-chain ledgers, ensuring legal defensibility and stakeholder trust in all system outputs and recommendations. Security Containerized zero-internet deployment with no external API calls, conforming to DoD security policies for classified and air-gapped environments, ensuring data sovereignty and protection. Compliance Strict adherence to NEPA statutory requirements (42 U.S.C. §§4321 et seq.), CEQ regulations, and Air Force environmental guidelines through policy-aware AI design that grounds all outputs in authoritative legal sources. Collaboration Human-in-the-loop design that positions AI as an assistant augmenting expert judgment rather than replacing human decision-makers, with SME feedback loops integral to system refinement and accuracy. Policy-Aware Generation Policy-Aware Retrieval-Augmented Generation (RAG+) cae17424-672c-466f-8754-c7fd7e1f0f1d 1 Develop a generative AI engine that produces NEPA-compliant analyses and summaries, guided by retrieval of authoritative regulations and guidance, eliminating hallucinations by grounding every statement in NEPA law, CEQ regulations, and Air Force environmental guidelines Corpus Ingestion Ingest Full NEPA Regulatory Corpus 209cc04b-8204-48c8-9766-dfc73b8939fa 1.1 Ingest the full corpus of NEPA law (42 U.S.C. §§4321 et seq.), Council on Environmental Quality (CEQ) regulations, and Air Force environmental guidelines for use as contextual reference in all AI-generated outputs Grounded Generation Generate Legally Grounded Text b2bcce39-74e1-436b-9c81-de73283ef750 1.2 Ensure all generated text (environmental impact summaries, significance determinations, mitigation measures) traces back to relevant NEPA provisions or CEQ guidelines, avoiding factual errors and hallucinations through strict retrieval-based grounding Sandboxed Operation Maintain Confined Sandbox Environment 2a9f8247-97f3-4a40-9957-748b50da362f 1.3 Use only government-provided data (laws, regulations, base-specific data) in a confined sandbox to maintain legal accuracy and compliance with DoD security requirements, eliminating external dependencies Knowledge Graph StratML-Informed Knowledge Graph of Objectives, Impacts, and Mitigations 47e617fa-e886-4f6e-a854-ed73dcd9af72 2 Design and populate a knowledge graph that links strategic objectives to environmental impact chains and mitigation measures, enabling multi-hop reasoning and reuse of knowledge across projects to address the problem that each NEPA review currently "reinvents the wheel" StratML Integration Import Strategic Objectives from StratML 9db63ee4-17ea-4771-a2e2-c0ae5db7aa57 2.1 Leverage StratML-formatted documents (DoD/USAF strategic plans, AFCEC environmental goals) to import high-level objectives into the knowledge graph, creating formal linkages between strategic intent and operational environmental analysis Impact Linkages Link Objectives to Impact Chains a9c7e472-ef5d-4e98-aa1c-d7aa1ac4b91e 2.2 Link strategic objectives to specific resource areas (land, water, air quality, wildlife) and potential impacts from proposed actions with cause-effect relationships encoded as graph nodes and edges Multi-hop Reasoning Enable Graph-Based Reasoning f00d8b88-2082-4e3a-8756-89acf8f663a5 2.3 Structure NEPA knowledge in graph form to enable the AI to perform multi-hop reasoning, tracing how proposed actions fulfill strategic goals or violate constraints, and suggesting mitigations aligned with policy objectives Geospatial Analysis GIS-Aware Impact Scoring via Spatially-Grounded GNNs ae3753c7-23cf-4a7e-88b8-f03b42f10628 3 Implement a geospatial analysis module that assesses site-specific environmental impacts using Graph Neural Networks, integrating GIS data layers into a graph-based environmental model for quantitative risk assessment GIS Integration Integrate Geographic Data Layers ed8851de-a97a-44be-be1f-f678481b78de 3.1 Integrate GIS data layers (for Edwards AFB and similar sites) with geographic features as nodes connected by spatial relationships (distance, watershed flow, species migration corridors) to enable spatially-aware impact analysis Impact Propagation Model Impact Propagation Through Networks ad18f7a6-a086-4ecb-bc02-36eb80a7e875 3.2 Develop spatial GNN to propagate potential impacts through the network (e.g., fuel spills affecting downstream water sources, runway noise affecting wildlife areas) using message-passing algorithms on geographic graphs Risk Visualization Generate Impact Significance Scores 4c344a81-653a-48b0-a5d7-b14148122c8f 3.3 Output impact significance scores for each resource area, providing quantitative and visual heatmaps of environmental risk to determine required NEPA documentation level (Categorical Exclusion vs Environmental Assessment vs Environmental Impact Statement) Provenance Ledger Provenance-Chain Ledger for Auditability 855d1f19-658d-4873-a549-5cc2fe432058 4 Develop a provenance tracking system that logs the AI's reasoning steps in a secure, hashed ledger to bolster legal defensibility and ensure transparency in compliance with trusted AI principles and regulatory scrutiny requirements Reasoning Documentation Log All AI Reasoning Steps f08f3c5a-3a67-4eee-ae15-b1cb899c3389 4.1 Record every key step: data retrieved, rules/thresholds applied, and how conclusions were reached, with timestamps and cryptographic hashes forming an immutable chain of evidence for regulatory and legal review Audit Trail Enable Verifiable Output Tracing f138b74a-79fb-466c-9bbf-8f0bd7c4b075 4.2 Ensure any output can be traced back to source inputs and rationale, providing human-readable audit trails for regulators, decision-makers, and potentially courts to verify facts and assumptions Governance Embedding Embed Governance and Accountability 344c1912-fdab-49c7-a497-4eefa12286ea 4.3 Align with emerging trusted AI principles by embedding governance and accountability into the system's core architecture, enabling confident use of AI-generated analyses with full transparency on reasoning processes Comment Analysis Active-Learning Public Comment Clustering (SME-in-the-Loop) 51f95bcb-50dc-4b8b-bd78-25a4ca84a47c 5 Prototype an AI-assisted public comment analysis tool to manage voluminous feedback received during NEPA public comment periods, using unsupervised ML and active-learning loops with human SME oversight to reduce weeks of manual labor to hours Comment Clustering Automate Comment Triage and Synthesis f7512689-53fa-4fb3-8020-1bf68837da51 5.1 Use natural language processing and clustering algorithms with language embeddings to group similar comments by theme (wildlife concerns, noise complaints, cultural resources) across thousands of public submissions Iterative Refinement Implement SME Feedback Loop 25b1664a-c1cd-4769-9e0b-0e9a2ad1190a 5.2 Enable active-learning loop where human SME (environmental planner or project officer) reviews AI clusters, adjusts or labels them, and AI iteratively refines understanding to maintain accuracy and project-specific relevance Response Generation Generate Draft Comment Responses 674b2783-0422-4cb6-b074-3105d14c23b8 5.3 Demonstrate that RAG+ system can generate draft responses for major concerns, pulling in relevant facts and policy references for SME finalization, dramatically reducing labor while ensuring systematic and transparent addressing of public concerns Secure Deployment Containerized Zero-Internet Deployment 4a3f648a-d10a-4d42-aea1-22e9e0c16a43 6 Validate that all components can run in a containerized, secure sandbox with no internet access, using only government-furnished data, meeting Air Force deployment requirements, DoD security policies, and SBIR topic stipulations Component Packaging Package All Components in Container d8199be8-f76e-47c2-886e-f2b8d9260732 6.1 Package AI models, knowledge graph database, and supporting services into a container (Docker or similar) suitable for classified or air-gapped environments with role-based access control and data encryption Local Model Operation Instantiate Language Model Locally 0121d006-92a1-4637-9acd-64aa44493442 6.2 Instantiate AI language model locally (fine-tuned open-source model or government-provided model) with all reference documents in local datastore, eliminating external API call requirements and ensuring data sovereignty Stand-alone Validation Demonstrate Stand-alone Operation 27a9453c-21df-425a-97a5-225ae63cd806 6.3 Demonstrate prototype runs on stand-alone computing environment (secure cloud enclave or on-premises server) with no degradation in functionality compared to connected setting, conforming to DoD security policies for user authentication and data handling Phase I Execution Six-Month Phase I Work Plan Implementation 467190f1-10d3-4bd7-9976-dd4da35bf412 7 Execute agile, iterative development cycle over 6 months with close attention to risk reduction and alignment with AFCEC environmental planners and legal reviewers' needs, demonstrating all six technical objectives through integrated prototype Requirements Analysis Requirements Analysis and Data Preparation (Month 1) 29df5571-bbea-4e67-9497-a9954a372ab4 7.1 Detailed requirements gathering with AFCEC stakeholders, data inventory of government-furnished information (NEPA statute, CEQ regulations, AF guidance, Edwards AFB environmental baseline), use case definition, and establishment of quantitative success criteria Graph Construction Knowledge Graph and Data Ingestion (Month 1-2) fd9d2e73-f51f-4a38-be63-0e3ad151b0f3 7.2 Schema design for NEPA concepts (Requirements, Objectives, Resource Areas, Impacts, Mitigations, Actions), data ingestion pipelines for regulations/policies/strategic goals/environmental data/past NEPA docs, and graph database setup with access controls RAG Development RAG+ Engine Development (Month 2-3) 2547117d-a357-42c2-b429-4bbbfdc1cd93 7.3 Document indexing and vector store creation combining symbolic and semantic retrieval, prompt construction for policy alignment, LLM integration with fine-tuning on NEPA Q&A tasks, and testing of RAG+ query capabilities with sample questions GNN Prototype Spatial Impact GNN Prototype (Month 3-4) 15d793b2-652d-4cab-a789-e771097826e8 7.4 Spatial data integration from GIS layers, GNN model design using attention-based message-passing for impact scoring, impact analysis output generation with heatmaps, and validation with domain experts on known environmental hotspots Logging Framework Provenance & Logging Framework (Month 3-5) 16e57797-3f2b-4611-9c40-ad0874df3430 7.5 Logging design for traceability events, cryptographic hash chain implementation (SHA-256) for immutability, integration across RAG+/GNN/clustering components, and audit report generation capability for human reviewers Clustering Tool Public Comment Clustering Tool (Month 4-5) 7172a6d3-ea44-437a-9cb4-9731069b09f6 7.6 Unsupervised clustering model development using Sentence-BERT embeddings, interactive SME interface creation for cluster review and adjustment, iterative refinement demonstration (1-2 feedback cycles), and integration with RAG+ for automated response drafting Integration Testing Prototype Integration & Testing (Month 5) bde94548-2bfc-421c-805c-bc04b267a12d 7.7 End-to-end run-through on Edwards AFB test scenario (proposed action through simulated public comment period), performance evaluation against human NEPA practitioner outputs (accuracy, completeness, clarity, time savings), and demonstration preparation for AF stakeholders Phase II Planning Reporting and Phase II Transition Planning (Month 6) 6f0af510-ee25-40fa-98fc-370e0ed9b659 7.8 Comprehensive final report compilation with design details, development findings, and performance assessment relative to Phase I objectives; detailed Phase II proposal outline for operational system scaling; and stakeholder outreach for user feedback on prototype and feature priorities Impact Delivery Measurable Impact and Cost Savings f5ab3db7-e9c7-4252-8912-c41ed72ab555 8 Demonstrate potential to significantly shorten NEPA documentation timelines (saving months per project) and reduce costs across the ~40,000-50,000 annual EAs ($100K-$500K each) and hundreds of EISs ($1-3M each) conducted by federal agencies, with Air Force realizing millions in annual savings Timeline Reduction Accelerate Documentation Timelines bda5861b-d70a-4d88-a061-ea2a287ef09d 8.1 Prove AI can reduce manual workload in time-intensive tasks (public comment analysis taking weeks, cross-checking legal requirements, document drafting) to shorten overall review timelines while increasing analysis quality and consistency Mission Acceleration Accelerate Mission-Critical Projects a2d10a12-10d2-4ea8-a0ac-d56d114d5f42 8.2 Help accelerate mission-critical Air Force projects (infrastructure development, training exercises, base expansions) by streamlining environmental clearances while ensuring thorough NEPA compliance, legal defensibility, and public participation Cost Efficiency Demonstrate Annual Cost Savings f7dbe582-d999-4db1-8bf9-ec729af26f9d 8.3 Show potential for millions of dollars in annual Air Force savings through modest AI assistance efficiencies (e.g., 25% reduction in EA/EIS labor costs), scaled across thousands of environmental assessments and hundreds of impact statements conducted Department-wide 2024-10-01 2025-03-31 2026-02-09 https://multiplex.studio/files/AF254-0809.pdf Owen Ambur Owen.Ambur@verizon.net