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IMPERIAL Core — Live Engineering Evidence

Truth boundary: this is a live internal AI-agent runtime, not a mortgage-production client deployment. It is published to show mechanisms that actually run today; no client adoption, fintech production history, revenue, or partnerships are implied.

Checked: ${esc(checkedAt)}

${esc(agents?.n)}
registered agent records

${esc(tables?.n)}
persistent SQLite tables

${esc(audit?.n)}
CRM hash-chain events

${esc(llm?.pass)}
successful logged LLM calls

${esc(training?.active)}
active professionalization learners

${esc(crm?.opportunities)}
tracked revenue opportunities

Relevant shipped mechanisms

Persistent relational state: project-scoped libSQL/SQLite used by the live runtime, with explicit schemas, uniqueness constraints, state transitions and reconciliation.
Idempotent ingestion pattern: INSERT OR IGNORE / ON CONFLICT ... DO UPDATE, deterministic IDs, leases and hash checks are used throughout queue and registry ingestion.
Append-only evidence: revenue events are hash chained; dedicated event stores use database triggers that reject update/delete and enforce predecessor hashes.
LLM outputs as untrusted structured work: model calls are logged with provider/model, purpose, response SHA-256, failure state and authority boundary; model output never grants execution authority.
Failure modes: bounded retries, lease expiry, stale-result rejection, quota states, fail-closed sensitive-input checks and reconciliation.
Multi-agent governance: federated multi-orchestrator architecture; no single global orchestrator; financial/external authority remains gated.

Live evidence snapshot

${esc(JSON.stringify({checked_at:checkedAt,registered_agents:Number(agents?.n||0),persistent_tables:Number(tables?.n||0),crm_event_chain:{events:Number(audit?.n||0),head_seq:Number(audit?.head_seq||0)},logged_llm_pass:Number(llm?.pass||0),revenue_crm:{opportunities:Number(crm?.opportunities||0),proposal_stage_records:Number(crm?.proposals||0)},professionalization:{enrolled:Number(training?.enrolled||0),active:Number(training?.active||0)},append_only_trigger_examples:(appendOnly.rows as any[]).map(x=>x.name)},null,2))}

Bake-off fit: how I would map these primitives

loan_tape CSV -> parse/validate -> deterministic import batch id -> Postgres transaction\n  -> UPSERT by lender_id + business key\n  -> append import_audit + case_event records\nborrower email -> schema-constrained LLM classification\n  -> validate enum/confidence/evidence fields\n  -> append classification event (never overwrite history)\ncase detail -> derive current view from relational state + event history\nactivity log -> event-driven, tenant-scoped by lender_id

Engineering stance: database invariants first; application validation second; LLM output is data, never authority. Idempotency and auditability are correctness properties, not UI features.

Public evidence generated dynamically from the running IMPERIAL Core recovery/runtime database. Sensitive values, credentials, payout addresses, prompts, buyer data and private evidence are intentionally excluded.

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