AI is already present
Employees are experimenting. Vendors are adding capabilities. Contracts and data rights are changing. An institution cannot govern what it has not inventoried.
AI readiness for financial institutions
Own your context before AI makes decisions with it.
LLM Squared helps banks, credit unions and fintechs map how the institution actually works—its people, knowledge, processes, data, vendors and AI already in use—then turns that inventory into a foundation the institution owns and can use with any model.
Start with a document, not a platform commitment.
Inventory the institution
People & skills
Knowledge
Processes
Data
Vendors
AI already in use
Context FoundationInstitution-owned · model-independent · ready to govern
A defensible starting point for every AI decision after it
The decision before the decision
Everyone is selling solutions. Almost nobody has taken inventory of the problem.
AI is already inside most institutions—through employees, vendors, core providers and customer-facing tools. The first job is not buying more. It is knowing what is already there, what it touches, who owns it and which decisions actually matter.
The best context is unwritten
Critical thresholds, exceptions and judgment often live with experienced people rather than in policies or systems. That knowledge is both an asset and a key-person risk.
Every vendor sees a fragment
A model can read what it is given. It cannot infer your strategy, decision pathways or risk appetite from a generic implementation. The institution must supply the frame.
The AI Readiness Foundation
Start with the institution you actually have.
We map the operating reality before recommending technology. That produces a portable foundation for strategy, governance, vendor evaluation and deployment—whether the next step involves LLM Squared or not.
PeopleSkills, ownership, readiness, and where judgment actually lives.
KnowledgePolicies, institutional memory, exceptions, and undocumented practices.
ProcessesDecision pathways, bottlenecks, handoffs, and human checkpoints.
DataSources, sensitivity, access, quality, lineage, and contractual rights.
TechnologyCore systems, current tools, integrations, dependencies, and resilience.
AI ecosystemEmployee use, vendor features, models, agents, owners, and approved purposes.
The first deliverable
An institution-owned readiness brief.
A practical record your executive team and board can use without learning a new vocabulary or buying a new system.
- Current AI and automation inventory with named owners
- People, knowledge and key-person dependency map
- Critical process and data-dependency map
- Ranked opportunities, constraints and reasons not to proceed
- Vendor and core-provider questions worth asking now
- Board-ready priorities and a sequenced ninety-day action plan
One foundation, three decisions
Readiness comes first. Building has to earn its place.
The work is intentionally sequenced so each commitment is based on evidence from the step before it.
Map the institution
Inventory context, decisions, dependencies, owners and AI already in use. Produce the readiness brief.
Choose one outcome
Pick a decision or workflow with a clear owner, measurable value and an acceptable risk posture.
Scale what proves itself
Extend the same governed context into executive offices or departments without rebuilding the foundation.
When the map says build
Possible first outcomes—not a catalog of shiny objects.
The readiness work determines whether one of these patterns fits. Each keeps human judgment in the decision seat and begins with the context the institution already owns.
The Office of the CEO
An executive context layer and a small staff of agents that help the CEO see what changed, surface questions and prepare decision-ready briefs.
See the foundation underneathAgentic Credit
A governed pattern for assembling borrower context, checking a draft against policy and preparing the file—while officers and committee retain judgment.
Discuss a credit workflowInstitutional Memory
A structured way to capture how experienced officers actually make decisions, including thresholds and exceptions that appear in no policy document.
See how it fits readinessOwn the context. Rent the engine.
Your institutional context should survive a model change, an outage and the next vendor decision.
LLM Squared separates the durable asset—your institution's knowledge, memory, decisions and standards—from the models that reason over it.
ContextCore
The shared, governed context foundation: institutional knowledge, approved sources, access rules, ownership and evidence.
KnowledgeDataControlsEvidence
thinqOS
The executive and workflow layer: role-specific context, tools and agents operating inside a defined human decision structure.
CEOCreditRiskOperations
Truth before theater: the exact architecture, controls, hosting, data handling and implementation status are documented for the proposed engagement before production data moves. No certification or deployment claim should require a prospect to take our word for it.
Regulatory footing
Inventory, ownership and documented decisions are not inventions.
Current financial-sector guidance and risk frameworks emphasize many of the same basics: know what is in use, understand the purpose and risk, assign accountability, perform due diligence and maintain evidence over time.
Financial Services AI RMFU.S. Treasury's financial-services adaptation of the NIST AI Risk Management Framework.Treasury · 2026 ↗
AI resources for credit unionsRisk, vendor diligence, data security and oversight considerations for credit unions.NCUA · 2026 ↗
Third-party risk guidanceInventory, risk assessment, due diligence, monitoring, governance and documentation.Federal Reserve, FDIC & OCC ↗
NIST AI RMFA voluntary framework for governing, mapping, measuring and managing AI risk.NIST ↗
These public resources provide standards and regulatory context. They do not constitute an endorsement of LLM Squared, and an engagement does not replace legal, compliance or regulatory advice.
Who does the work
We wrote the book on AI governance in financial services.
LLM Squared combines decades inside banking, credit unions, financial technology, enterprise systems and governance. The work starts in the institution's vocabulary, not a generic AI playbook.
Stephen Bishop
Founder · Co-author
Thirty years across financial services, including Jack Henry, AT&T, Citibank, community banking and sponsor banking. Builder of a 115-item enhanced due diligence framework for bank-fintech partnerships.
Tony del Fierro
Co-author · Technology executive
Connectivity Solutions Strategy at Wells Fargo and former SVP and Chief Technology Officer at Sound Credit Union, with experience spanning payments, API architecture and enterprise governance.
Know Your Agent
The Governance Standard for AI in Financial Services. A practical framework for institutions deciding how agents should be identified, classified, owned and monitored.
Explore the book ↗The brand boundary
LLM Squared builds readiness and institutional AI capability. KYA Advisory remains independent.
When the need is broader AI governance, retained or fractional leadership, sponsor banking, embedded finance or fintech pre-diligence, that work belongs with KYA Advisory—not inside a platform sale.
Start with one question
Where is AI already touching your institution—and who owns the decision?
Bring the question, the vendor pitch or the workflow that keeps coming up. We will tell you whether readiness work is the right first move.