Six interconnected subsystems form the connective tissue of the Everest AI platform — the core fabric that Ozarc.ai stands on. Engineered for scale, compliance, and autonomous banking operations from the foundation up.
Every Everest AI deployment is the same six subsystems, wired the same way, governed the same way. What changes from bank to bank is the policy library and the data shape — not the platform.
Neuro‑symbolic engine compiling FDIC, GLBA, BSA/AML and bank policy into the model as mathematical constraints — not soft prompts.
Process IRL discovers the “happy path” from your best officers’ behaviour, then infers the reward function behind their judgement.
A universal banking namespace built on MCP and GraphRAG. Loans, deposits, covenants, customers — all relational, all reasoning‑ready.
Hierarchical swarms of teammates with human‑in‑the‑loop breakpoints for any risk or loan decision. The supervisor pattern, codified.
SMS‑first, voice‑ready. Reaches 97% of a bank’s customers with no app install, no digital banking enrolment, no friction.
Every decision logged with the triggering logic axiom. Aligned with SR 11‑7 and the NIST AI RMF. Examiner‑ready by default.
A bank’s core, LOS, doc imaging, IRS transcripts, Plaid, credit bureaus — every system surfaced as a standardised Model Context Protocol endpoint. One plug, every shape. Plug it in and the platform can reason over the data immediately, securely, and reversibly.
Every endpoint, internal or external, addressed the same way. mcp://core/loans, mcp://irs/transcripts.
The connector reads through interfaces a bank already maintains. The ledger of record is never written to without a human authorising the action.
Cores change every 5‑7 years. The teammates don’t. The MCP layer absorbs the new shape; the workforce keeps working.
FDIC examination criteria, the GLBA Safeguards Rule, BSA/AML provisions and a bank’s own risk-rating policies are compiled into the neural loss function as first-order logic constraints. The result is not a chatbot pretending to know banking — it is a reasoning system that cannot, by mathematical construction, violate the rules it has been given.
Every recommendation traces back to the specific axiom that fired. Plain-English reasoning, every time.
Logic Tensor Networks make policy violation mathematically unreachable in the output space.
Audit trail tied to a specific logic axiom satisfies SR 11-7 model documentation requirements.
The bank’s own credit policy can be loaded as additional axioms — without retraining.
Input, model, output. The input rails strip jailbreaks and NPI before the model ever sees them. The model rails are mathematical constraints inside the network itself. The output rails are a critic agent that checks every response for hallucination and compliance — before it leaves the building.
NeMo Guardrails and Colang policies filter prompt injections, jailbreak attempts, off-topic queries, and any NPI leakage at the gateway. The model never sees what it shouldn’t.
Logic Tensor Networks make policy violation mathematically unreachable in the output space. The model cannot, by construction, produce a non-compliant recommendation.
A dedicated critic agent reviews every response for hallucination, NPI exposure, fair-lending risk and compliance accuracy — and logs the verdict to the immutable audit ledger.
Every teammate operates in one of two modes per workflow. The bank’s risk committee decides which sits where — and can change it at any time, per teammate, per task type.
The teammate gathers data, runs the analysis, drafts the recommendation, and pre-fills the form. A human officer retains final authority. Required for risk ratings, loan approvals, and escalated collections.
Runs autonomously only when model confidence is > 99% and the neuro-symbolic logic check passes. Reserved for routine, well-bounded actions that the regulators have already cleared as decision-support.
The platform is the foundation. The teammates are how a bank actually sees, uses, and benefits from it.