Note: The following is an excerpt. To download the full white paper, use this link.
Federal agencies face persistent dilemmas when implementing regulatory programs. Policy manuals run to hundreds of pages. Business rules are embedded in dense regulatory prose. Technical teams must translate requirements like “the proposed action shall not significantly affect unique characteristics of the geographic area” into functional software.
The challenges compound across the implementation chain. Applicants must assemble input data and supporting documentation without always understanding which regulatory provisions apply. Agency decision makers must then interpret complex regulations consistently—a particularly difficult task when determinations must align across dozens of state offices and multiple program areas operating under different timelines and priorities.
This translation process is costly and slow. Policy updates require new development cycles. Interpretation questions demand manual review of regulation text. When new versions of regulations are published, implementation often begins from scratch. The result: weeks-long determination processes, variable outcomes for similar projects, and mounting backlogs when regulations change.
There is a more efficient approach.
Federal regulations already contain structured logic, it simply needs to be made explicit. Consider this regulatory text from Natural Resource Conservation Service (NRCS) environmental compliance:
“Replacing and repairing existing culverts, grade stabilization, and water control structures and other small structures that were damaged by natural disasters where there is no new depth required and only minimal dredging, excavation, or placement of fill is required.”
This single sentence encodes conditional logic:
IF the structures are existing (not new construction) AND
IF damage resulted from natural disasters AND
IF no new depth is required AND
IF only minimal dredging/excavation/fill is needed THEN
The action may qualify for categorical exclusion under this provision.
Our approach makes this implicit logic explicit and machine-executable. Rather than translating regulations into software after the fact, we structure policy rules in formats that both humans and computers can interpret directly.
Rather than translating regulations into software after the fact, we structure policy rules in formats that both humans and computers can interpret directly.
Most policy automation initiatives focus on AI’s ability to read and understand regulations. Natural language processing, semantic analysis, and large language models are technologies that dominate the conversation.
But reading regulations is not the innovation.
The innovation is the transformation architecture—taking human-readable policy and making it machine-executable without writing custom code for each rule.
We built that architecture before AI existed as a practical tool. The SIF LexerSM parses regulatory language, recognizes patterns (“must,” “shall,” “required when”), extracts validation logic, and generates executable schemas. Deterministic rules, not neural networks. It works because specifications follow linguistic patterns.
What changed is the extraction layer. The SIF LexerSM handles structured CMS specifications with predictable formats. Large language models handle unstructured CFR text with variable phrasing. But both produce the same output: JSON schemas that validation engines execute.
The hard problems remain solved: 1. Concurrent policy versions without code deployment (SIF runs v2.0, v3.0, v4.0 simultaneously) 2. Consistent execution across all users and locations (schema defines truth) 3. Rapid updates when regulations change (days, not months) 4. Audit trails showing exactly which rules applied to each determination.
SIF proved the architecture works. AI accelerates the extraction. The validation engine, the schema approach, and the deployment model remain unchanged. What we built for healthcare assessments applies directly to categorical exclusions. The domain expertise changes (NEPA specialists instead of clinical experts), but the transformation pipeline is identical: regulatory text ➡ schema generation ➡ deterministic validation.
To learn more about how Cadmus is transforming federal environmental regulations into machine-executable rules with policy-as-code, access the full white paper using the button below.
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