MoralStack is a governance and safety layer for LLM applications. It analyzes user requests before generation, evaluates risk and intent, and decides whether the AI should answer normally, answer safely, or refuse. The goal is to make AI systems more auditable, controllable, and reliable in sensitive or regulated contexts.

Multi-agent, policy-driven AI system for processing sensitive enterprise documents with extraction, analysis, verification, deterministic orchestration, and full audit logging. Designed for regulated environments (banking, finance, insurance).

Practical AI governance patterns for regulated industries — risk assessment templates, model lifecycle controls, EU AI Act alignment

Open methodology for systematic adversarial evaluation of small language models in regulated industry deployments

Curated list of AI/LLM tools, papers, and repos for regulated industries -- finance, healthcare, legal.

A curated list of AI governance frameworks, tools, regulations, and resources for responsible AI deployment

A GitHub template repository for deploying AI in regulated industries — governance docs, CI/CD gates, risk taxonomy, and release readiness out of the box

Compliance-aware LLM workflow for regulated financial environments: policy enforcement, model routing, output validation, and correlated audit trail via LangGraph and OTel.

Reference architecture for trust-first knowledge platforms — the engineering pattern behind Higher Self's commercial builds. Schemas, templates, and architectural decisions for regulated industries. MIT.

Policy-bounded decision traces for AI-assisted financial operations: versioned policy enforcement, human-in-the-loop gating, cost attribution, and auditable case outcomes.

Compliance enforcement middleware for voice AI pipelines: warm transfer state management, PII scrubbing, confidence-gated escalation, and HIPAA/FERPA/EU AI Act enforcement for Pipecat, LiveKit, and Twilio deployments.

Multi-agent system that generates production-grade PRDs for regulated industries using 4 specialized AI agents in parallel

Whitepaper accompanying the agent-centric ML research platform for regulated environments (using MLflow, Pydantic, PyYAML, pre-commit hooks)

AI/LLM security testing library mapped to OWASP LLM Top 10 2025 and MITRE ATLAS. 191 payloads organized by domain with CI/CD integration for regulated industries.

A governance framework for Retrieval-Augmented Generation (RAG) systems in regulated industries. Provides citation tracking, audit trails, human-in-the-loop validation, and compliance patterns.

Multi-model AI deliberation: Claude, Gemini, and GPT-4o cross-examine each other, then produce a structured verdict with a confidence score, dissent, and an append-only governance audit log. Built for regulated EU enterprises. Part of the Dome AI governance portfolio.

Declarative form validation library for React with YAML/JSON configs. Ships with BFSI, healthcare & government ID presets (PAN, Aadhaar, IFSC, GST). Audit-ready, i18n-supported, and runtime-customizable. Build compliance-ready forms without reinventing validation logic.

Finnish law encoded as executable logic with s(CASP) — Unemployment Security Act §6 entrepreneur definition with open-world reasoning and Finnish-language justification trees

FERPA/HIPAA/GDPR-compliant RAG patterns: identity-scoped retrieval, audit logging, and framework adapters for regulated enterprise AI

Python and SQL data analysis of cosmetic products, ingredient patterns, and chemical risk indicators built on a relational SQLite database.