Automate banking at scale, without paying a model to re-read your policy.
Every bank can rent the same frontier model. Prompting it with your policy on every case pays for the same reading, again and again. StarnaIntelligence compiles your policies, norms and data into governed Prisms once, so decisions run in code, with no frontier-model tokens on the decision path.
- Ground.Your data, policies, norms and decisions in one governed layer, focused into Prisms for each workflow and role.
- Automate.Don’t prompt your policy, compile it. Code runs the rules, models read, people judge.
- Prove.Hard blocks sit in compiled code, outside any prompt, and every run leaves its evidence.
- Compound.Every Prism and resolved exception enriches the layer, so the next workflow costs less.
The model isn’t the moat
Governed context is.
Your bank can already retrieve its data. Its agents and automations still can’t tell what the data means, which policy governs it, which rule applies, which control blocks an action, or what evidence supports the outcome. That knowledge lives in PDFs, procedure manuals and the heads of long-serving staff, and every AI project rebuilds its own version of it, which nobody can audit.
-
Run-time token taxA frontier model re-reads the policy manual and works out the workflow again on every case. The bill grows with volume, and every case waits on the model.Zero-Token ArchitectureAI helps harvest and compile your knowledge at build time. At run time, compiled rules make the decisions, and small private models do only the bounded reading work.
-
Copilots guess at policyThey read policy text and interpret it again on every run. Two runs can give two answers, and neither can be defended to an examiner.Rules decide, models explainPolicies, procedures and data definitions become versioned, cited, owned objects. Decisions are deterministic; language models summarize and explain.
-
Context islandsEach line of business builds its own AI with its own definitions, prompts and rules, and they drift apart.One truth for people and agentsEach process, person or agent gets exactly the governed slice it needs, and nothing more.
-
Policy change takes monthsSomeone has to find every workflow, prompt and spreadsheet that encoded the old rule.Change is impact-assessed and re-verifiedA policy change lists every affected process and blocks any workflow that no longer passes.
-
Audit by reconstructionAuditors rebuild decisions by hand from logs and emails.Every action leaves its evidenceEach execution links the action to its decision, rule, policy and the facts it used.
What it’s worth to your bank
Scale and control stop being a trade-off.
Straight-through automation
Rule-determined cases run end to end as compiled code, with evidence on every run and no frontier-model tokens spent deciding them. Fewer touches per case, and a run cost that doesn’t climb with a token bill.
Exception routing
“Cannot determine” names the missing inputs and sends the case to the person with the right authority. Less rework and waiting.
Grounded augmentation
Copilots show the facts, the rule, the outcome and the cited clause. Faster decisions, less variance between reviewers.
Change agility
A policy change is impact-assessed, re-verified and scenario-tested before it goes live, at a fraction of today’s effort.
What it means for your role
CEO, business line headExecutive sponsors
AI automation that reaches production, not pilot purgatory.
You don’t need fifty disconnected chatbots. You need business workflows automated end to end. We turn your institution’s know-how into a governed asset that runs cases under control, and every new workflow reuses what the last one built.
What you’d measureTime from pilot to production, and the cost of each new workflow.
COO, head of operationsOperations
Routine cases run straight through. Exceptions arrive ready to decide.
Rule-determined cases run end to end on compiled rules and APIs. When a case is missing an input or needs judgment, the workflow names what’s missing and routes it to the person with the authority, with the policy clause and relevant precedents attached.
What you’d measureStraight-through rate, touches per case, cycle time and rework.
CFO, FinOpsFinance
Grow case volume without growing a token bill.
If a frontier model re-reads your policies on every case, your AI bill rises with every case. Compiled rules make the decisions without model tokens, and small private models handle only the reading steps that need one.
What you’d measureAI run cost per case, and how it moves as volume grows.
CIO, CDO, architectsData and technology
Build the context once, not in every prompt.
Facts stay in your systems of record: we map what they mean instead of copying them into a new lake. Your agents and apps receive governed Prisms through open interfaces, with a private, on-premises option, and language models are kept to the unstructured work, so no model vendor holds your logic.
What you’d measureProjects that reuse a Prism instead of rebuilding their own context.
CRO, CCORisk and compliance
Hard blocks live in compiled code, where no model can talk its way past them.
Regulatory stops such as bankruptcy, SCRA and segregation of duties are compiled into deterministic gates outside any prompt. When a regulation changes, you update the rule once, see every affected workflow, and test it against scenarios before it goes live. No autonomous final approvals.
What you’d measureHard-block bypasses, and the time from a regulatory change to a tested workflow.
Model risk, internal auditGatekeepers
Deterministic decisions, and a replay of every case.
Language models never set a decision or release an action: they read inputs and explain outcomes, and what they read can only make a case more cautious until a person confirms it. Pick any case from any quarter and replay it: the data, the rule and policy versions, the controls that fired, and who signed off and why.
What you’d measureTime to answer an examiner’s question about a case, and decisions that can’t be replayed.
Policy and process ownersDomain experts
Approve your rules once and see them enforced everywhere.
Instead of answering the same questions by email, or watching your manual read three different ways, you review and approve decision rules in one queue. Your rules are cited on every automated run and on every reviewer’s screen.
What you’d measurePolicy questions arriving by email, and shadow spreadsheets still in use.
Front lineUnderwriters, servicers, analysts
No more digging through five systems and three PDFs for one case.
Your Prism brings the customer facts from your core systems, runs the eligibility checks, cites the governing policy section and surfaces relevant precedents, then tells you what’s missing or what needs your sign-off. It never invents a fact.
What you’d measureScreens per case, and agreement between reviewers on the same case.
Ground. One governed truth for every person and agent.
We start from the knowledge your bank already has, wherever it lives today, and build your Enterprise Intelligence Layer: the meaning of your data, your policies, your norms and precedents, your decisions and controls, and the evidence they produce. From it we focus Prisms: deployable, self-contained packages of data meanings, compiled rules, norms, authorities and permitted actions, each scoped to one workflow or role, so your people and your agents work from the same playbook.
Your bank’s knowledge
- Data products and systems of record
- Credit, servicing and compliance policy
- SOPs, runbooks and approval matrices
- Case histories and past exceptions
Enterprise Intelligence Layer
- Meaning and relationships
- Policies and requirements
- Norms and precedents
- Decisions, controls and evidence
Prisms
- Deployable packages, such as Mortgage Servicing or Dispute Resolution
- Compiled rules, norms and authorities
- Permitted and prohibited actions
Hybrid execution
- Compiled code and APIs for rule-determined steps
- Small private models for bounded reading tasks
- People for judgment, with the context assembled
- A full evidence record on every run
Policies decide. Norms guide.
Policies
Hard rules: thresholds, eligibility, deadlines and regulatory stops. They compile into deterministic decisions and blocking gates, and a case can’t pass a gate it fails.
Norms and precedents
How your bank exercises judgment: institutional standards and the pattern of past exceptions. They are put in front of the people and bounded agents who need them, and never set a decision on their own.
AI drafts candidate rules and procedures; deterministic code assembles them; your stewards and policy owners approve every object before anything uses it. A Prism publishes only when every decision it covers is governed.
Automate. Don’t prompt your policy. Compile it.
Prompting a model with your policies, procedures and schemas on every case pays for the same reading again and again, makes every case wait on it, and gives answers you can’t replay. We use AI where it earns its keep, while the workflow is built, and run the workflow as code. We call it Zero-Token Architecture: no frontier-model tokens on the decision path.
AI at build time. Code at run time.
- Build time
Harvest and compile
Models read your documents, propose candidate rules, map what your data means and draft workflows. Your stewards and policy owners verify every object before it enters the layer.
- Design time
Scope and bind a Prism
Each workflow or role gets a verified Prism: the data definitions, compiled rules, norms, authorities and permitted actions it needs, and nothing more.
- Run time
Execute and prove
Routing, calculations, eligibility, regulatory stops and evidence logging run as compiled code, with no model tokens. Small private models handle bounded reading tasks, such as classifying a borrower letter, grounded in the Prism. People step in for true ambiguity or exception authority, with the facts, norms and cited clauses already on screen.
Every process is a decision supply chain
- Trigger
- Work step
- Decision
- Data
- Rule or policy
- Control
- Evidence
- Outcome
To automate a step safely we capture who decided, why, on what data, under which rule and with what proof. Read backwards, the chain is the audit view.
Each step goes to the worker that fits it, the assignment is recorded with its reason, and hard regulatory blocks sit outside any prompt, where no model can talk its way past them.
| Type of work | Best fit | Mortgage servicing example |
|---|---|---|
| Deterministic action reachable by API | Compiled code and API | Calculate days past due; send the scheduled borrower notice |
| Repetitive, rules-based work on a stable screen | RPA | Key a payment arrangement into a legacy servicing screen |
| Document review, summarization, classification | AI agent on a small private model | Classify a borrower letter as a hardship disclosure or a bankruptcy notice |
| High-risk decision needing policy interpretation | AI assist and human approval | Loss-mitigation recommendation from the investor waterfall, approved by a specialist |
| Ambiguous judgment, risk acceptance, exception approval | Human | Foreclosure referral, with dual approval and a segregation-of-duties check |
Rules decide. Models explain. No language model sets a decision or releases an action, and it can only make a case more cautious.
Prove. Shown working, not promised.
We ran one missed-mortgage-payment operations document through the whole loop, against ten simulated servicing systems.
Decisions governed
One operations document became a governed graph with every decision covered and no blocking defect.
Regulatory scenarios passed
Across 125 simulated days: the 120-day rule, SCRA, dual-tracking, bankruptcy and segregation of duties. No hard block was bypassed.
Model drafts blocked
Five models drafted the same graph. The verification gate stopped three, a frontier model’s among them, for leaving decisions ungoverned.
Answered on a laptop
A small private model, grounded in the governed layer, answered servicing questions correctly and invented no identifier. No borrower data left the machine.
Prototype on mock servicing systems and one sample process, re-checked in October 2026. These are not customer results; we publish customer outcomes only once they are measured in production.
Lines we don’t cross
No autonomous final approvals
Prisms can prohibit final approval and policy override, enforced at the control gate outside any prompt.
No AI approving its own context
A model may propose; a named person approves every object before a decision uses it.
No copies of your data
We don’t build a data lake: facts stay in your systems of record and are read at run time.
No fabricated advice
When a model call fails, the record says it failed. It never falls back to a default presented as advice.
Compound. Each Prism costs less than the last.
Exceptions, corrections and verified facts flow back, through review, into attested knowledge, and resolved exceptions become precedents for the next reviewer. Your second Prism reuses the borrowers, loans, authorities and evidence the first one modeled, so the bank’s intelligence grows with every case it touches instead of being rebuilt by every project.
- 2 weeks
One policy document, one verified Prism
The Policy-to-Workflow Sprint, run on your own document.
- 90 days
One Prism in production
A pilot on your systems: your scenarios passing, complete evidence, zero prohibited actions.
- 6 to 18 months
Across the line of business
From servicing to underwriting on the same shared model of your business, with new Prisms built from attested components.
- 18 months on
Your bank’s intelligence foundation
Every agentic AI initiative consumes Prisms, and model risk reviews start from the evidence and scenario tests already on record.
Timeframes are our targets for a first engagement, agreed with each bank at the start of the pilot.
Where it pays most
Processes that are dense with policy, made of decisions and hungry for evidence.
Many rules, many decisions, and an examiner who wants to see why. That is where governed context changes the economics, so that is where we start.
| Start here | Knowledge the Prism governs | What changes | Readiness |
|---|---|---|---|
| Mortgage servicing and collectionsHybrid: automate and augment | The 120-day rule, SCRA, dual-tracking, the bankruptcy stop, the loss-mitigation waterfall, segregation of duties | Rule-determined cases run straight through. Bankruptcy and SCRA stops can’t be bypassed, and loss-mitigation recommendations cite the investor guide and wait for a specialist. | Proven in our prototype |
| Mortgage underwriting and credit riskAugment people | Underwriting and income-qualification policy, fair-lending rules, exception authority | Underwriters see the facts, the policy section and the rule outcome. “Cannot determine yet” names the missing document instead of guessing. | Specified |
| Card dispute resolutionHybrid: automate and augment | Reg Z and FCBA timelines, card network rules, provisional credit policy | Deadlines and provisional credit decided by rule, with every case carrying its evidence for the network and the examiner. | Designed |
Readiness: Proven, run end to end in our prototype on mock systems; Specified, requirements written; Designed, the agents and decisions are designed.
Then across the bank, on the same layer
Regulatory reporting
Reporting instructions, critical data elements and lineage, risk and control self-assessment.
HybridClient onboarding and KYC
Customer identification and due diligence, customer risk rating, enhanced due-diligence triggers.
HybridFinance and accounting operations
Accounting policy, approval authority matrix and SOX controls for reconciliation exceptions.
Mostly straight-throughPayment operations
Sanctions screening policy, payment-rail rules, repair and return procedures.
Mostly straight-throughCustomer service
Product terms, fee-waiver authority, complaint handling and UDAAP norms.
HybridFraud case disposition
Alert disposition rules and the authority for customer contact and account holds.
Augment peopleCommercial real estate review
Concentration policy, appraisal and covenant requirements, regulatory CRE schedules.
Augment peopleTreasury and liquidity limits
Investment and liquidity policy, asset-liability committee limits, counterparty limits.
Augment peopleSecurity operations
Security policies, incident runbooks and notification obligations.
Hybrid
Built by people who have answered to examiners
And we have led the change inside the bank.
Our founding team has spent decades inside regulated industries, driving transformation and intelligent automation where every decision has to stand up years after it was made.
Decades in regulated industries
We have written policy, run and tested controls, and prepared for audits and regulatory exams. We know what model risk and internal audit will ask, because we have been asked.
Transformation and intelligent automation
We have driven operating change and intelligent automation programs inside regulated institutions, and we know why so many AI pilots stall at model risk review.
Governance in our DNA
The same founders run speedupgrc, a governance, risk and compliance consulting firm. StarnaIntelligence is its sister company, built to make governed AI the way a bank works.
How we work
Earn trust in the details
Every claim cites a source, and every result can be reproduced.
Measure the instrument too
A scorer can be wrong as easily as a model. We test our own tests and report negative results beside positive ones.
Meet banks where they are
We start from the documents, systems and rules you have today, not a blank-slate rebuild.
Build to compound
Each engagement leaves reusable, attested intelligence behind, not one-off scripts.
Bring one policy document
In two weeks, see it run under control.
The Policy-to-Workflow Sprint repeats on your own document what we have already run end to end on a sample servicing procedure. Your policy owners approve what we model, and you keep everything we produce.
- Days 1 to 4Your policy, modeled and citedEvery requirement anchored to its clause; your owners approve it.
- Days 5 to 7A verified PrismIt publishes only when every decision it covers is governed.
- Days 8 to 9A compiled workflowEach step assigned to a person, an agent, an API or RPA.
- Day 10A scenario test reportRegulatory scenarios run on a simulated clock, with every control and its evidence.
Three ways to go further
- DeployPrism Packs
Pre-modeled and scenario-tested.
Ontology, rules, controls, workflow specs and regulatory scenario suites, tuned to your policies.- Build with usCo-designed Prisms
Your processes, modeled together.
Our engineers work beside your policy owners, and your stewards approve every object.- TransformIntelligence program
The governed foundation for every AI initiative.
A multi-year partnership to make the layer how your bank grounds its people and agents.