Build no. 01
Internal Knowledge Assistant
Internal Knowledge ManagementCredit Union or Community Bank · $500M–$5BConfigured
The technical solution — and the human capability to operate it responsibly.
- Adopter assets
- ~$838M
- Members served
- 50,929
- Adopters / vendors
- 21 / 7
- New-hire onboarding
- Day 1
First Heritage FCU, Mar 31, 2026
Across 9 branches in NY & PA
Community-institution deployments of this use case — capability is commoditizing
Assistant is the first source of truth new employees learn
The build, in two halves
Part I
The Technical Build
What the model, retrieval, deployment, and controls must actually do.
Model & Retrieval
Deployment
Controls
Channels
Part II
The Human Build
The roles and skills that keep the deployment defensible under exam.
AI-Augmented Customer Service Lead
Runs the assistant on the front line and owns answer quality.
What makes it defensible
AI Adoption & Enablement Lead
Drives the staged rollout, corpus curation, and staff readiness.
What makes it defensible
AI Data Governance & Privacy Lead
Governs what institutional content reaches the vendor platform and how it is retrieved.
What makes it defensible
Part III
Verification Benchmarks
Proposed controls in Bank Ready AI’s reference design — not tests or thresholds reported by the adopting institution.
- 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
The visible skills overlap across the adoption roles. It is the revealed governance depth that differentiates what each role actually secures — and that turns a technical deployment into a defensible one.
Proven in the field
21 confirmed adopters
Institutions confirmed running this use case in our production research database — this build reflects how it is deployed in practice.
- 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
Make it yours
Where institutions customize
A reference build is a starting point, not a prescription. These are the decisions that make it yours.
- Knowledge-corpus scope: which policies, procedures, and product content the assistant may ground on
- Role-based retrieval map: what each role (teller, lender, compliance, HR) is permitted to see
- Refusal boundaries: topics the assistant must escalate rather than answer (advice, disputes, account actions)
- HITL review-queue thresholds by answer sensitivity (HR, compliance, member data)
- Channel rollout order — contact center first, then branches, then onboarding/LMS
- Re-grounding cadence as products, rates, and procedures change
The integration plan
Every phase moves the technical build and the human build together — the deployment and the capability to govern it arrive at the same time.
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
Human-workstream roles and skills are drawn from the Talent Builder — the same library used to generate role profiles at /create/talent.
20-minute go/no-go call
Identify the next step for your AI specification.
A complimentary 20-minute go/no-go call with Alex Zaboli to discuss one AI use case and make a preliminary decision on the next step: explore further, gather more information, or pause. This is not a conclusive feasibility assessment; technical, operational, and governance diligence is required before an implementation decision.
Microsoft Bookings lets you book as a guest — no Microsoft account is required.