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AI FOR SMALL AND MIDSIZED BANKS

Protect trust.
Move work forward.

Your customers trust you with what matters. Give your staff useful AI with a dedicated LLM on your bank’s own hardware, scoped around your data requirements and approved knowledge. Reduce repetitive preparation so your team can focus on customers, decisions, and new opportunities.

Dedicated bank deployment is scoped separately from desktop plans. Controls, services, and commitments are agreed for your institution.

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AI-generated illustration of the fictional Alder & Vale Community Bank, with named signage, teller counters, and an ATM
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PRIVACYCONTROLEVIDENCEUSEFUL WORK
PROVIDENCE. INTELLIGENCE. POSSIBILITY.
Customer privacyStart with the information you must protect
Bank-controlled scopeDefine who, what, and which actions
Evidence to reviewCheck sources, decisions, and results
Capacity for your teamLess assembly. More attention for customers.

YOUR CUSTOMERS. YOUR REPUTATION.

Useful AI should fit
the trust you’ve earned.

AI-generated illustration of a banking professional and a business owner discussing a brief

Small and midsized banks need to accomplish more without adding uncertainty about customer information, staff access, or important decisions.

Start with a defined internal workflow. Establish the data boundary and review process, then evaluate whether the result earns a place in your bank.

See what to establish before a pilot

A DEDICATED LLM FOR YOUR INSTITUTION

Your hardware.
Your requirements.

Scope a deployment that brings a dedicated language model and approved bank knowledge onto infrastructure your bank controls. Define the operating arrangement before customer data enters the workflow.

Explore the bank-hosted offering
01

Map the information.

Agree where processing, documents, answers, indexes, logs, and backups reside, and identify every external dependency.

02

Set access and oversight.

Define user roles, approved sources, permitted actions, and the technical tests that will demonstrate those boundaries.

03

Agree on ongoing care.

Establish support access, updates, recovery, ownership and licensing terms, and an exit path in the deployment agreement.

On-premises hosting is not a guarantee of zero external processing or regulatory compliance. Disconnected operation, retention, data reuse, and individual controls must be confirmed for the agreed solution.

START WHERE VALUE IS EASIEST TO CHECK

Three useful places
to begin.

Help staff find the right answer, assemble evidence, and catch missing information. Begin read-only, with approved or synthetic material.

Illustrative workflows, not reported customer results. Credit decisions, payments, regulatory submissions, and other consequential actions remain outside an initial read-only pilot.

A CLEAR PLAN FOR YOUR BANK

Agree on the boundary.
Then prove the value.

Bring operations, technology, security, and risk colleagues into the same evaluation. You decide what a useful, acceptable result looks like.

  1. 01

    Choose one internal workflow.

    Define the staff problem, the approved inputs, and the work a person should receive. Use synthetic data or material cleared for the evaluation.

  2. 02

    Agree on controls and criteria.

    Scope deployment, access, data handling, review points, and support. Set measurable tests for quality and boundaries before the pilot begins.

  3. 03

    Review the work before expanding.

    Compare preparation time, staff review, corrections, and operating cost with a baseline. Reclaimed staff capacity is not automatically cash saved. Expand only where the evidence supports it.

Start a banking conversation

BANKING RESOURCE / EXECUTIVE GUIDE

A Bank Leader’s Guide to Practical AI

Understand where AI can help bank staff, how preparation differs from taking action, and what a controlled first pilot needs. A worked policy example and a short readiness checklist support the learning.

  • Choose useful work with a checkable result.
  • Understand information boundaries and human responsibilities.
  • Evaluate a controlled pilot before expanding.

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SEE WHAT A REVIEWABLE RESULT LOOKS LIKE

A clear question.
A useful next step.

Explore a policy brief, an examination packet, or a document-review note. Each example shows the result and the unresolved questions, not just an answer.

This interactive illustration uses fictional information. It is not a product recording, live AI session, or completed bank deployment.

Explore the evidence-led workflow
ILLUSTRATIVE BANK WORKFLOW
Thaddeus workspaceEXAMPLE
Bank operations Branch procedure review

The policy. The source. The open question.

Compare these fictional branch procedures. Cite the relevant sections and flag conflicting review steps.

Keep the basis for the answer visible.

Organize the supplied procedures, identify version differences, and prepare a brief for the policy owner.

  1. Identify the supplied versions
  2. Link statements to source sections
  3. Flag the conflicting approval step

Illustrative workflow using fictional information. Not a live product session.

MORE CAPACITY FOR THE PEOPLE WHO KNOW YOUR BANK

Spend less effort
holding every step together.

A trained internal AI coordinator can help staff turn requests into scoped assignments. Plans and project context keep the work organized; source review and named owners keep important decisions with your people.

Explore the internal AI coordinator approach

Feature availability and local execution are confirmed in the bank deployment scope. General desktop capabilities are not automatically included or approved for customer data.

PRIVACY. SAFETY. A CLEAR ACCOUNT OF THE WORK.

Bring your bank’s priorities.
Build the right first assignment.

Start with your workflow and information requirements. We’ll define the deployment and evaluation scope before asking you to expand the use of AI.

Explore our 18 sourced AI risk stories for practical evaluation lessons. No product eliminates every AI risk; assess the controls and agreements for your intended workflow.

Fictional sample output

Branch policy brief

A short, static example of a deliverable. No live work has run.