SAMMY AI compiles the regulatory universe into deterministic legal engines. For agencies, supervisors, and ministries, that means modeling impact before publication, surveilling compliance across every supervised entity, and cross-referencing your own code against the full federal and state universe. Sovereign deploy. Inside your perimeter.
A new rule lands. Dozens of lawyers interpret. Engineers translate the interpretation into supervisory code. QA runs hundreds of automated checks, then hundreds more by hand. The CFPB's own cost estimate for the Section 1071 small-business lending rule was roughly three hundred million dollars in one-time implementation, plus another three hundred and seventy million annually. The American Bankers Association's survey of four hundred and seventy nine banks found the real cost was substantially higher, with eighty eight percent of respondents needing to hire new full-time staff for that one rule alone.
Five years after final publication, compliance deadlines still have not reached all tiers. The same rebuild runs in parallel inside every supervised institution, each one arriving at a slightly different answer to the same statute. The agency that issued the rule cannot see, in aggregate, how the industry is actually applying it.
The CFPB's own Automobile Finance Examination Procedures direct examiners to use, quote, "judgmental or statistical sampling." The supervised universe holds more than one hundred million active auto loan accounts and one point six trillion dollars in outstanding debt. Full-population review is not an option the playbook contains. Every supervisory finding is a mathematical projection from a subset, and every projection becomes a methodological target in the response letter.
The 2013 indirect-auto-lending bulletin used a surname-and-geocoding proxy for race. The agency's own internal analysis, later reviewed by the House Financial Services Committee, showed the proxy correctly identified roughly nineteen of every one hundred African-American applicants in datasets where race was verifiable. Congress rescinded the bulletin in 2018 under the Congressional Review Act, the first time the statute had ever been used against informal agency guidance. Indirect auto fair-lending enforcement has not recovered since.
Westlake Financial is a useful case. Six federal and state enforcement actions across eight years: CFPB in 2015, the Massachusetts Attorney General in 2016, the Massachusetts Division of Banks in 2016 and 2017, the DOJ Civil Rights Division in 2017 and again in 2022. The 2022 DOJ addendum, covering two hundred and fifty additional servicemembers, was discovered not by new investigation but by monitoring compliance with the original 2017 order. Supervision catches what supervision is running against, continuously, and no more.
The D.C. Circuit vacated a one hundred and nine million dollar CFPB penalty against PHH Corporation for retroactively applying a new interpretation of RESPA. The Bureau moved, years later, to vacate its own settlement in Townstone Financial. A federal judge refused and called the disavowal, in the opinion, "an act of legal hara-kiri that would make a samurai blush." CFPB v. Navient took more than seven years from filing to resolution, settled only after the defendant had already exited the business.
A methodology that does not hold up the first time becomes an industry letter, then a Congressional hearing, then a vacatur. Cooperation degrades with every iteration. The next finding takes longer to produce and is fought harder. A regulator's credibility compounds in the direction it's already moving.
SAMMY AI is not a SaaS login for agencies. It is a foundational model for law, deployed into your environment as sovereign infrastructure. The model ships with the full federal and state statutory universe already compiled into deterministic, citeable rules. Your own code, once loaded, is cross-referenced against everything adjacent.
Impact modeled before publication. Supervisory surveillance running continuously across every entity in scope, against every file rather than a sample. Every verdict reproducible and traceable to the issuing section. Conflicts caught in drafting, not in court.
This is not prompt engineering. This is infrastructure.
When a rule is issued, its logic compiles alongside the source text. No multi-year rebuild project. No parallel army of lawyers re-interpreting it inside every supervised institution. The day the rule takes effect, the exam runs against it. When the statute moves, the engine recompiles.
SAMMY AI evaluates every file, every account, every transaction, against every applicable rule. Sampling becomes a choice, not a capacity constraint. Findings are not projections. They are counts. Methodology stops being the litigation surface.
No proxy variables. No opaque model outputs. Every verdict cites the statute, regulation, guidance note, or precedent that produced it, down to the § and sub-paragraph. Walk the chain from verdict to source. Publish it alongside the finding. Reproducibility is the new credibility.
SAMMY AI runs inside your agency's own environment. On-premise, private cloud, or air-gapped network. Your data, your policies, your operational records never leave your perimeter. Ever.
We work directly with agency leadership. If your team is thinking about modeling its own code, supervising an industry in real time, or harmonizing across jurisdictions, write to us.