CIVIC SCALE

Public institutions face AI mandates that private-sector tooling was never designed for — records systems holding millions of documents, statutory audit requirements, procurement rules, and zero tolerance for fabricated output. Production AI in the public sector requires deterministic validator gates, provable audit trails, and data that never leaves institution-controlled infrastructure.

iSimplifyMe builds public-sector AI as infrastructure, not chatbots: records-scale data and retrieval layers, deterministic validator gates on every surface that requires the same answer every time, and audit-ready operations on private AWS. The architecture is documented in our published Layer 3, Layer 4, and Layer 5 reference papers.

Records-scale AI starts at the data layer: source ingestion with provenance, permissioned retrieval that enforces role-based access before a model ever sees a document, and context engineering that keeps answers grounded in the institution's own records. Our Layer 3: Data + Retrieval paper documents the eight-stage pipeline we deploy.

Every iSimplifyMe deployment runs in a dedicated AWS account with VPC isolation, encryption at rest, IAM-scoped execution, and zero data-retention configuration at the model layer. Audit logs persist for seven years. Model inference never leaves institution-controlled infrastructure, and no institutional data enters a third-party training corpus.

Not every AI surface may be probabilistic. Statutory lookups, fee schedules, deadlines, and eligibility rules are deterministic-required surfaces: they need named validator gates — schema gates, pattern gates, structural rule evaluators, threshold scorers, and bounded critics — so the same question produces the same answer, every time, provably. Our Validator Gap Audit maps every surface in an existing stack to its required gate.

iSimplifyMe engagements are structured for institutional procurement: fixed-scope phases with named deliverables, written architecture documentation, defined SLAs for operated systems, and review artifacts a compliance office can evaluate. iSimplifyMe engagements begin at $50,000; infrastructure builds typically run into six figures and ship in phased, auditable increments.

iSimplifyMe operates production AI today: a 24-tenant orchestration platform, an autonomous monitoring layer with investigate-only agents, and an 80+ site infrastructure modernization — all on private AWS, all documented in public Labs dossiers and whitepapers. The systems that would run a public-sector mandate already run in production.

Every iSimplifyMe agent architecture keeps humans in authority: agents investigate, draft, and propose; named staff approve before action fires. Escalation paths are deterministic, every recommendation carries structured context, and every action is auditable end to end. Autonomy is earned in measured stages, never assumed — the promotion framework is published in The Trust Ladder.

iSimplifyMe has spent 15+ years building systems inside regulated constraints — healthcare privacy, legal advertising rules, financial compliance — where the cost of a wrong answer is institutional, not cosmetic. Public-sector constraint is a discipline we already practice, not a new market we are learning.