Bank Ready AI · Governed Build Specification

Internal Knowledge Assistant

Internal Knowledge ManagementCredit Union or Community Bank · $500M–$5BStatus: Configured

This specification is written to be circulated: to risk, to compliance, to technology, and to the examiner. Part I documents the technical build. Part II documents the human capability that makes it defensible.

Part I

The Technical Build

Model & Retrieval

Cloud-hosted LLM (vendor conversational platform)
RAG grounded in the institution's own policy, procedure, and product corpus
Role-scoped retrieval index (teller ≠ lending officer ≠ compliance)

Deployment

Internal staff portal / intranet embedding
Identity-system integration for role-based access — no core-processor dependency
Onboarding & LMS surface for new hires from day one

Controls

Source citation on every generated answer — no ungrounded output
Role-scoped retrieval enforced (a teller and a lending officer never see the same answer)
Human-in-the-loop review queue for sensitive-topic answers (HR, compliance, member data)
PII / NPI handling under GLBA safeguards
Adversarial pilot testing before rollout — findings feed back into the source corpus
Full audit trail on prompt → retrieved source → answer lineage

Channels

Internal staff portal / intranet
Contact-center desktop (first-rollout channel in the field)
Onboarding & LMS

Part II

The Human Build

AI-Augmented Customer Service Lead

Runs the assistant on the front line and owns answer quality.

Visible skills

Prompt Engineering · Change Management · AI Training & Awareness

Governance depth

AI Output Evaluation & Testing · Staff AI Acceptable Use Policy · AI Incident Response

AI Adoption & Enablement Lead

Drives the staged rollout, corpus curation, and staff readiness.

Visible skills

Use-Case Design & Scoping · Change Management · Stakeholder Management

Governance depth

AI Policy Development · Model Monitoring & Observability · Staff AI Acceptable Use Policy

AI Data Governance & Privacy Lead

Governs what institutional content reaches the vendor platform and how it is retrieved.

Visible skills

Consumer Data Privacy & GLBA (AI Scope) · AI Security & Data Privacy · AI Audit & Internal Controls

Governance depth

Third-Party AI Risk Management · AI Model Inventory & Lifecycle Tracking · AI Policy Development

Part III

The Integration Plan

Confirmed adopters of this use case: Ion Bank · Hudson Valley Credit Union · Interra Credit Union · Harvard Federal Credit Union · First Heritage Federal Credit Union · CPM Federal Credit Union · Capra Bank · American Bank of Commerce · Bank of the West (Grapevine, TX) · Texas First Bank · Marine Credit Union · LA Financial Credit Union · Rio Grande Credit Union · iTHINK Financial Credit Union · United Heritage Credit Union · Sharonview Federal Credit Union · Marine Federal Credit Union · Magnifi Financial Credit Union · Affidian Credit Union · TruStone Financial Credit Union · One Nevada Credit Union.

Phase 1 · Foundation — Ground truth and role access before the assistant answers a word

Technical workstream

Curate the answerable corpus and define hard refusal boundaries
Map role-based retrieval permissions against the identity system

Human workstream

Draft the internal AI policy; name the executive champion and accountable owner
Baseline Talent Builder skills: Prompt Engineering, Change Management, Consumer Data Privacy (AI)

Phase 2 · Adversarial Pilot — One department, every answer reviewed — 'try to break it'

Technical workstream

Launch to the contact center with full logging and answer-provenance capture
Adversarial testing surfaces source-content gaps; rewrite procedures where the AI reveals ambiguity

Human workstream

Compliance signs off on the response playbook and sensitive-topic escalation path
QA sampling of transcripts with weekly review cadence

Phase 3 · Production & Scale — Role-scoped rollout with citation and drift monitoring

Technical workstream

Expand by role and department with citation-rate and drift dashboards
Quarterly re-grounding as products, rates, and procedures change

Human workstream

New hires meet the assistant on day one — no outdated habits to unlearn
Quarterly adoption and answer-quality report to the governance committee

Part III

Verification Benchmarks

Every generated answer resolves to a named source document — no ungrounded output in the audit trail
Role-scoped retrieval audited: zero cross-role content leakage between teller, lender, and compliance indexes
Sensitive-topic HITL review queue meets its disposition SLA with documented reviewer rationale
Adversarial-pilot findings traced back into corpus edits — the pilot improves the source, not just the model
Quarterly re-grounding evidence maintained in the examination file

Adopter assets: ~$838M
Members served: 50,929
Adopters / vendors: 21 / 7
New-hire onboarding: Day 1