Axiarete AI · Financial Services

Every application. Every dependency. Every dollar. Every risk. Understood.

Axiarete is the AI-native intelligence system for financial services technology. It reads what your institution already has — code, configuration, logs, tickets, and financials — and rebuilds the complete, living truth of your application estate. So every decision you make about technology is made with evidence, not archaeology.

Purpose-built for regulated financial institutions. Operational in 8–12 weeks. Read-only, additive — no migration required.

  • SOC 2 Type II
  • ISO/IEC 27001:2022
  • ISO/IEC 42001:2023
20–40%of the value of the technology estate is tech debt, CIOs estimate1
15–20%typical savings potential from uncovering redundancy across the portfolio2
$300K+cost of a single hour of downtime at over 90% of mid-size and large enterprises3

Sources: McKinsey; McKinsey via SAP LeanIX; ITIC 2024 Hourly Cost of Downtime. Benchmarks are for orientation — the Axiarete diagnostic quantifies each of these for your own estate.

The moment

Financial services technology has entered its most demanding decade. Five forces are converging on the same weakness.

The pressures on a bank’s technology estate used to arrive one at a time — a rate cycle, a regulation, a merger, a platform decision. They now arrive together, and they all land on the same exposed point: most institutions cannot fully describe the technology they run. Not to their examiners, not to their boards, not to themselves.

Force one

The efficiency mandate has reached the application layer

Margin pressure has already squeezed branches, headcount, and vendors. What remains is the largest unexamined line in the budget: the application portfolio itself. CIOs estimate that tech debt amounts to 20–40% of the value of their technology estate (McKinsey),1 and uncovering redundancy typically carries a 15–20% savings potential (McKinsey, via SAP LeanIX).2 In a business where the efficiency ratio is destiny, a $40M application budget quietly carrying $6–8M of that potential is not an IT problem. It is a shareholder problem.

Force two

Regulators stopped asking whether you have an inventory. They now ask you to produce it.

DORA is in force across the EU. The PRA’s operational resilience deadlines have passed. APRA CPS 230 is live. In the United States, the FFIEC IT Handbook treats an authoritative technology inventory as a baseline control; OCC Heightened Standards and Federal Reserve SR 11-7 assume you can map systems, models, and dependencies on demand; the amended NYDFS Part 500 makes asset inventory an explicit requirement. The global supervisory posture has converged on a single expectation: describe your estate, prove its resilience, and do it today — not after a six-week evidence hunt.

Force three

The people who understood your systems are leaving faster than they can be replaced

The knowledge that holds a bank’s estate together lives disproportionately in the memories of senior engineers. The mainframe cohort is the sharpest edge of this — much of the COBOL workforce is at or near retirement age — but the same erosion is happening in twenty-year-old Java estates, in vendor platforms whose implementation teams disbanded, in every system whose documentation stopped being true years ago. Tribal knowledge is a wasting asset, and it is wasting now.

Force four

Every board wants an AI strategy. No AI strategy survives contact with an unmapped estate.

AI initiatives fail in financial services for a predictable reason: they are scoped against systems and data flows that nobody can accurately describe, then stall in model-risk review because the dependency picture cannot be evidenced. The institutions moving fastest on AI are not the ones with the boldest ambitions. They are the ones with ground truth — a current, provable map of applications, data, and dependencies that Risk and Audit can approve against.

Force five

Consolidation is accelerating, and deals are won or lost in technology diligence

Bank M&A is back, and the pattern is well established: integration overruns and post-close surprises trace back to technology unknowns that were, in principle, discoverable before signing. Most integration blowups were visible in the code before the deal closed. Nobody had a way to look.

Five forces. One root cause. The institutions that solve knowing will outperform the institutions that keep guessing — on cost, on risk, on regulatory standing, and on speed.

The cost of not knowing

The most expensive thing in banking technology is what you don’t know you’re running.

Institutions do not lose control of their estates in a single decision. They lose them one undocumented change at a time — until the day an incident, an examiner, or a migration reveals how much of the map was fiction. The post-mortems of the industry’s most expensive failures read, with unsettling consistency, like the same document:

Case file2018

TSB Bank

£48.65M

Regulator fines, plus £32.7M in customer redress. A core-banking migration disrupted service across all branches and for a significant proportion of its 5.2 million customers. Regulators found the bank failed to organise and control the migration adequately and to manage the operational risks of outsourcing to critical third-party suppliers.4

Source: FCA and PRA Final Notices, December 2022Failure mode: migration and dependency mapping
Case file2012

Knight Capital

$460M+

Lost in a 45-minute trading window. A repurposed flag reactivated dormant code, unused since 2003, still present in production; a deployment reached seven of eight servers, and the orphan logic on the eighth produced more than 4 million unintended executions in 154 stocks. The firm agreed to be acquired that December.5

Source: SEC Administrative Proceeding 34-70694, October 2013Failure mode: dead code and configuration drift
Case file2017

Equifax

147.9M

Consumer records exposed. A patch for a published Apache Struts vulnerability was available the day of disclosure and an internal directive to apply it went out within two days — but the institution had no authoritative inventory of which systems ran the component. The vulnerable dispute portal was never patched; attackers were inside for 76 days before detection. Settlements reached up to $700M.6

Sources: FTC v. Equifax (2019); GAO-18-559Failure mode: component inventory and vulnerability mapping

Three different institutions. Three different failures. One common factor: an incomplete picture of what was running, where, with which dependencies. Every one of these gaps was knowable. None of them was known.

Your tools of record can’t close this gap. They were never built to.

The instruments institutions rely on — CMDB, APM, EA repositories, TBM suites — share three structural defects. They are siloed: each holds a fragment, and connecting them is a permanent integration project. They are manual: built on surveys, interviews, and attestations, they decay the moment they’re published; inventories go stale within months of every reconciliation cycle. And they are passive: they record what someone once typed into a form, and stop there — recordkeeping without judgment, inventory without insight.

The deeper flaw is epistemological. A CMDB tells you what someone once said about a system. The code tells you what the system actually is. Until now, no one could read the code — all of it, continuously, across thousands of applications — and turn it into decisions. That is precisely what changed.

What Axiarete is

Axiarete doesn’t interview your teams. It reads your systems.

Axiarete is an AI-native intelligence system that ingests the artifacts your institution already produces — source code, packaged-app documentation, configuration, runtime logs, tickets, and financials — and from them derives what no survey can: what each application actually does, how it connects, what it costs, where it is fragile, and what should happen to it next.

The result is not another repository to maintain. It is a living model of your estate — rebuilt from evidence, refreshed continuously, and accurate because its sources cannot lie. Code doesn’t misremember. Logs don’t retire. Configuration doesn’t leave for a competitor.

What your institution already has

  • Code
  • Documentation (packaged apps)
  • Configuration
  • Logs
  • Tickets
  • Financials
The Axiarete intelligence core
Living model of the estate
Deterministic models + deep learning + LLMs + agents
Every conclusion traceable to the source artifact that produced it.

What your institution gets, and can act on

  • Purpose
  • Business process
  • Features
  • Architecture
  • Health
  • Risks
  • Inefficiencies
  • TCO
360° portfolio visibility

A complete, continuously current picture of every application — home-grown, packaged, SaaS, and AI tools — including the ones nobody remembers deploying.

Unified risk, quality & cost measurement

One evidentiary standard across the estate. A 300-point technical health check per application, business-risk mapping for every technical issue, and true TCO — comparable, defensible, current.

Intelligent portfolio optimization

Not a dashboard that describes the problem — an engine that surfaces the moves: consolidate these, retire those, modernize this, renegotiate that. Ranked by business impact, feasibility, and effort.

The Technology Expert Digital Twin Signature capability

Every application gets a conversational expert — a Digital SME rebuilt from the system’s own artifacts — that anyone in the institution can question as if the original architect never left. An SME that never resigns, never retires, and never forgets.

Digital SME · Deposit fees (COBOL)Illustrative
What happens to an overdraft fee when the reversing credit posts after the nightly cutoff?

The fee stands for that business date. The reversal is queued and the fee is refunded in the next cycle only if the account’s end-of-day balance is non-negative; a second fee in the same cycle is waived.

EvidenceFEEPOST.cbl § 4200-REVERSEACCTFEE.cpyJCL: NIGHTLY-FEE step 06

Systems of record capture what people said. Axiarete establishes what is true — and keeps it true.

What Axiarete does for a financial institution

One intelligence layer. Six ways it pays for itself.

Continuous Portfolio Intelligence

Know your estate the way regulators, boards, and buyers now expect you to.

The foundational deliverable: an authoritative, evidence-derived inventory of every application and its dependencies — maintained continuously, not reconstructed annually. Incident bridge calls start with a blast radius, not a guess. Examination requests are answered from a system, not a scramble. The estate stops being folklore and becomes a queryable asset.

“Where do we run this, and what’s exposed?” — answerable in minutes, not days.

  • Automated discovery across home-grown, packaged, SaaS, and AI tooling — including shadow and business-line-procured applications
  • Dependency graphs derived from code, configuration, and runtime behavior, not from interviews
  • Business-process-to-application mapping — the language of operational resilience regimes
  • Ownership, lifecycle, end-of-life, and component-level visibility, current at all times
Technical Debt & Risk Management

Every technical issue, priced in business risk.

Most institutions can list their vulnerabilities. Almost none can rank them by what they’d actually cost. Axiarete discovers debt and risk across code, supply chain and OSS, runtime behavior, and change history — then maps each finding to the business processes, transaction volumes, and customer channels it threatens. Remediation stops being a backlog argument and becomes a portfolio decision, with an agent that helps execute the fixes.

A single hour of downtime costs more than $300,000 at over 90% of mid-size and large enterprises (ITIC).3 The debt that causes outages is visible long before it detonates — if something is reading for it.

  • Comprehensive debt and risk discovery: code, supply chain/OSS, runtime, git history
  • Dynamic business-risk modeling — every technical issue tied to its blast radius and dollar impact
  • Remediation Agent for assisted fix execution, with DevOps integration for proactive control
  • Continuous posture: risk found as it’s introduced, not at the annual review
Application & Data Rationalization

Find the money hiding in the portfolio.

Decades of growth, vendors, and mergers leave every institution running applications that overlap, underperform, or serve no one. Axiarete establishes objective ground truth — real usage, real overlap, real dependencies, real run cost — and auto-discovers the rationalization moves, ranked by savings and feasibility. Then it helps execute: business cases, change-management artifacts, migration sequencing, test plans.

Uncovering redundancy typically carries a 15–20% savings potential (McKinsey, via SAP LeanIX).2 On a $40M application baseline, that is $6–8M — recurring, every year it goes unfound.

  • Ground-truth opportunity assessment: retire, consolidate, renegotiate — with the evidence attached
  • Redundancy and capability-overlap detection across business lines
  • License and SaaS waste surfaced ahead of renewal cycles
  • Agentic execution assistance and continuous re-optimization, so savings don’t decay after year one
Modernization & Migration Assistance

Modernize on evidence. Sequence by readiness. Ship with proof.

Modernization programs don’t fail in the build phase. They fail in discovery — when the estate turns out to be different from the plan. Axiarete rebuilds full application intelligence before a single workload moves: feature-level replaceability assessment, cloud-readiness scoring, dependency-safe sequencing, auto-generated execution roadmaps and test plans. And when the honest answer is “this system should stay,” Axiarete says so — with the numbers to defend it.

Large IT projects run 45% over budget and 7% over time on average, while delivering 56% less value than predicted (McKinsey and the University of Oxford).7 The overruns live in the unknowns. Remove the unknowns.

  • Deep app intelligence from code, docs, interfaces, tickets, and databases
  • Feature-level replaceability and migration assessment against packaged and cloud alternatives
  • Modernization business case, roadmap, and artifact builder; agentic market and vendor research
  • Auto-generated execution plans and test coverage grounded in actual system behavior
Featured · Reframing the mainframe

Your mainframe was never the weakness. The shrinking number of people who understood it was. AI just closed that gap.

For decades, boards treated COBOL estates as liabilities awaiting migration. But the anxiety was never about the platform — mainframes deliver uptime measured in decades, deterministic financial consistency, and 12M+ transactions a day without complaint. The anxiety was about comprehension: fragile knowledge, concentrated SMEs, decaying documentation, terrifying change risk.

Generative AI dissolves exactly that problem. Models now read COBOL fluently — parsing logic, tracing dependencies, documenting business rules, translating context for modern developers in real time. Which changes the rational calculus entirely: why spend $30–100M and four years replacing a transaction engine that works, when you can make it permanently maintainable in twelve weeks and redirect the capital to what customers actually see?

No customer cares whether their withdrawal cleared through COBOL or Java. They care about the mobile experience, the API, the speed. Modernize the interface layer. Keep the engine. Put the migration budget where it earns revenue.

Your AI team for the mainframe

  1. Digital SME per applicationConverse with any COBOL system as if its original owner were still in the building
  2. AI Maintenance EngineerImpact analysis, modification guidance, test generation, dependency tracing for every change
  3. AI Security ScannerContinuous auditing against PCI DSS, GDPR, SOX, and agency frameworks; audit-ready documentation on demand, regenerated as code changes
  4. AI Technical WriterLiving documentation, architecture maps, data flows, and business-rule catalogs generated from the code as it exists, updated automatically
  5. AI OptimizerPerformance tuning, selective modernization, retirements, and cost reduction across the environment
The math · Illustrative five-year TCO, mid-size U.S. regional bank ($15–30B assets, 10,000 MIPS, 6M lines of COBOL)
Migration path
$125.7M risk-adjusted
  • Program, parallel running, cloud steady-state, 60% overrun probability priced in
  • Break-even in year 9–11
  • First customer-visible delivery in year 4–5
  • 15 engineers consumed
AI-maintained path
$76.0M
  • Lower TCO from month one
  • Zero cutover risk
  • Core untouched
  • 15 engineers freed for customer-facing delivery

The question is not whether migration can work. It’s whether replacing something that works — at that price, on that timeline, at that risk — is the best use of your next four years.

Technology Due Diligence — M&A, Integration & Divestiture

Diligence at the speed of the deal. Depth no data room can fake.

Technology diligence traditionally means a two-week expert review of management presentations and a sampled code scan — a judgment call dressed as analysis. Axiarete reads the target’s actual estate: every application, its real architecture, its true debt load, its dependency structure, its run cost. Before signing, you know what you’re buying. After close, you have the integration map on day one — overlap analysis, rationalization targets, sequencing, and the synergy case grounded in evidence rather than banker arithmetic. The same machinery de-risks divestitures and carve-outs, where entanglement is the entire problem.

Most integration overruns were visible in the code before the deal closed. Now someone can look.

Read the technology due diligence guide →
  • Pre-signing: full-estate assessment of technical debt, security exposure, key-person risk, and true run-rate — in weeks
  • Post-close: day-one overlap analysis, consolidation targets, and dependency-safe integration sequencing
  • Synergy validation: rationalization savings quantified from ground truth, not top-down assumption
  • Carve-outs: entanglement mapping and separation planning derived from code and configuration
AI Strategy & Roadmap — Built Bottom-Up

The only AI strategy that survives model-risk review is one built on ground truth.

Top-down AI strategies produce impressive slides and stalled pilots. Axiarete builds the roadmap from the bottom up: it ingests your business strategy, process models, application inventory, and data schemas; generates candidate AI use cases enriched with external insight from industry and competitors; then filters them through business impact, feasibility, and effort. What emerges is deployable — because every use case is already mapped to the systems and data it touches, in a form Risk and Audit can approve under existing model-risk frameworks.

The distance between an AI ambition and an AI deployment is an accurate map of systems and data. Start with the map.

  1. InputsStrategy documents, business process models (L1–L4), app inventory, data schemas, architecture diagrams
  2. GenerationThe Axiarete AI Use Case Builder, enriched with industry and competitor insight
  3. PrioritizationBusiness impact × feasibility × effort, scored against your actual estate
  4. DeliveryDeep-dive use cases, shareholder value map, sequenced roadmap, ROI assistant, and implementation guides
Built for the examiner’s table

Regulators converged on one demand: describe your estate, on demand, with evidence. Axiarete is that evidence.

MandateWhat it expectsWhat Axiarete provides
FFIEC IT Handbook United StatesAuthoritative technology inventory as a baseline controlContinuous, evidence-derived inventory — no reconstruction per examination cycle
OCC Heightened Standards United StatesDemonstrable risk governance over technologyPer-application risk quantified and mapped to business impact, current at all times
Federal Reserve SR 11-7 United StatesModel inventory, dependencies, and governanceSystem-and-data dependency mapping that lets AI/ML deployments be scoped and approved under existing model-risk frameworks
NYDFS Part 500 amended · United StatesExplicit asset inventory requirementAutomated, continuously maintained asset and component inventory
DORA European UnionICT risk management, register of information, resilience testingDependency graphs, third-party and component visibility, evidence on demand
PRA / FCA Operational Resilience United KingdomMapping of important business services to supporting systemsBusiness-process-to-application mapping derived from how systems actually behave
APRA CPS 230 AustraliaOperational risk and service-provider managementVendor and SaaS estate visibility, concentration analysis, usage-versus-entitlement evidence
PCI DSS 4.0 GlobalAccurate scoping of the cardholder data environmentComponent-level inventory of where regulated data actually flows

An institution that answers from a system, immediately, is examined differently than one that answers from a scramble, eventually.

Axiarete does not certify your compliance. It produces the evidence your compliance depends on.

For every seat at the table

One system of truth. A different weapon in every chair.

CIO / CTO

Run the estate on evidence.

End the era of decisions made from stale spreadsheets and heroic memory. Every modernization, consolidation, and investment call — grounded, defensible, fast.

CFO

Find the money hiding in the portfolio.

Recoverable spend, license waste, redundant capability, and true TCO — surfaced continuously, quantified credibly, captured with execution support. The efficiency-ratio lever nobody else can reach.

CRO / CISO

Shrink the unknown attack surface.

You cannot defend what you cannot enumerate. Component-level inventory, continuous exposure detection, and blast-radius mapping — the difference between a months-long patching gap and a same-day answer.

Chief Audit Executive / Compliance

Evidence on demand, every cycle.

Stop reconstructing the estate for each examination. One continuously maintained source that answers FFIEC, OCC, DORA, and internal audit from the same ground truth.

Head of Corporate Development

Diligence at deal speed.

Read the target’s actual estate before signing. Walk into integration with the map already drawn and the synergy case already evidenced.

Head of Transformation / Modernization

Sequence by readiness, not by opinion.

Replaceability scored at the feature level, dependencies mapped before they bite, execution plans and test coverage generated from how systems actually work.

Deployment, security and model governance

Built to pass the review that kills most AI vendors.

We ask to read your most sensitive artifacts — code, configuration, logs. We built the platform, and the company, to deserve that access.

Security posture

  • SOC 2 Type II attested; ISO/IEC 27001:2022 certified.
  • ISO/IEC 42001:2023 certified — the international standard for AI management systems, held by few vendors in this category.
  • Axiarete analyzes your artifacts; changes reach production only through your existing pipelines, with human approval and your change control.
  • A dedicated AWS environment for each customer, with AES-256 encryption, customer-managed keys and zero-trust access; data residency defined by agreement.
  • Customer data is not used to train Axiarete or third-party models.

Model governance: the SR 11-7 answer

Your model-risk team will ask how Axiarete’s own AI is governed. Good — that’s the right question.

  • Evidence-linked outputs: every conclusion traces to the specific artifacts — files, commits, log entries, configurations — that produced it. Nothing is an oracle pronouncement.
  • Deterministic core, generative interface: structural analysis rests on deterministic models and deep learning; LLMs and agents operate on top, constrained by that verified base.
  • Human validation built in: SME-validation workflows are part of the product, not a workaround — every material finding is routed through your own experts for confirmation before it becomes a decision.
Read how Axiarete governs AI and data →

Time to value

  • Operational in 8–12 weeks: ingestion, model tuning, configuration, integration, UAT.
  • First ground truth in 30 days via the Portfolio Diagnostic.
  • Additive, not disruptive: no migration, no cutover, no parallel run — your estate keeps running while it becomes understood.
Why Axiarete

Financial institutions don’t lend their names to vendor websites. We serve them anyway — and prove ourselves differently.

You will not find customer logos, borrowed testimonials, or thinly anonymized case studies on this page — and you should be suspicious of vendors in this category who show them. The institutions we serve treat their technology estates, and their vendor relationships, as confidential. We hold that confidence absolutely. So our proof works the way your own diligence does: examine who built this, examine how it can be checked, and then examine what it finds in your estate.

Built by operators

Axiarete was founded and is run by people who have sat in your chairs — former CIOs, platform engineers, and technology strategists who have owned estates like yours, answered to examiners like yours, and defended technology budgets in rooms like yours. The platform is what they wished existed when the decisions were theirs to make.

Engineered to be checked

Every conclusion the platform produces links to the artifacts that produced it — the file, the commit, the configuration, the log entry. There is nothing to take on faith: your architects can trace any finding to its source, your model-risk team can review the documentation, your auditors can follow the evidence chain end to end. And the operation behind the platform is independently attested: SOC 2 Type II, ISO/IEC 27001:2022, ISO/IEC 42001:2023.

Proven where it matters: on your estate

The only proof that should move a regulated institution is proof on its own systems. That is precisely what the 30-day Portfolio Diagnostic exists for: pick a slice of your estate, and judge the platform by what it reconstructs, what it surfaces, and what your own experts confirm. Our reference is the evidence we put in front of you.

Paul Dottle
Paul DottleStrategic PartnerFormer EVP, CIO and CTO, American Express; Global VP, General Mills.
Andrea Shiah
Andrea ShiahStrategic AdvisorFormer Global VP, Business and Digital Transformation, American Express.
Amar Mishra
Amar MishraStrategic AdvisorFormer VP Global IT, Infrastructure Architecture and Operations, MFS Investment; Citi Street; Fidelity.
Michael Benvenuto
Michael BenvenutoStrategic AdvisorGlobal CPO, Ryan Specialty; Aon.
Tom Rodden
Tom RoddenChief StrategistFormer CIO, Varian Medical; GE, Deloitte, PwC. 2021 Bay Area CIO of the Year.
Why now

For fifty years, understanding a technology estate cost more than tolerating the ignorance. That just inverted.

Every institution’s estate was built by thousands of people over decades, and the full picture never fit in any one head — so the industry learned to operate on partial knowledge and call it normal. Stale inventories, discovery phases, key-person risk, examination scrambles: not failures of discipline, but the rational consequence of comprehension being prohibitively expensive.

AI changed the price. For the first time, a machine can read everything — every line of code, every configuration, every log — and hold the whole picture, current, continuously. Which means partial knowledge is no longer normal. It is now a choice.

And it compounds. The institution that establishes ground truth this year makes every subsequent decision — every rationalization, every modernization, every deal, every AI deployment, every examination — faster and better than the institution still doing archaeology. The gap doesn’t close. It widens every quarter.

The estates are equally complex. The difference is who can see theirs.

FAQ

The questions a serious buyer asks

How is this different from our CMDB, EA repository, or APM tooling?

Those are systems of record: they store what people entered and decay accordingly. Axiarete is a system of understanding: it derives the estate from artifacts that can’t go stale in the same way — code, configuration, runtime behavior — and it doesn’t stop at describing. It ranks the moves, builds the business cases, and helps execute them. It also makes your existing tools better, by giving them ground truth to reconcile against.

What does Axiarete need access to?

Read access to the artifacts you already have: repositories, configuration, logs, ticketing exports, and financial data. Each customer runs in a dedicated environment designed for institutions whose security reviews are, correctly, brutal. See security and deployment details.

How do we trust what the AI concludes?

You shouldn’t trust it — you should verify it, and the product is built for that. Every insight links to the evidence that produced it. SME validation is a first-class workflow: every material finding is routed through your own experts for confirmation before it drives a decision. And the analytical core is deterministic; the generative layer operates on verified structure, not vibes.

How does model risk apply to Axiarete’s own AI?

Directly, and we welcome it. The evidence-linked architecture means your validators can trace any output to source. An AI vendor that can’t answer this question shouldn’t be inside a bank.

We’re already mid-migration. Is this still relevant?

Especially then. Migration programs bleed in discovery and sequencing — the phases Axiarete compresses most. It maps what you’re actually moving, sequences by real dependencies, generates test coverage from actual behavior, and tells you honestly which workloads shouldn’t move at all.

Is this an argument against ever modernizing?

No — it’s an argument against modernizing blind. Sometimes the evidence says migrate; Axiarete then makes the program faster and safer. Sometimes it says the transaction engine is the most battle-tested asset you own and the capital belongs at the customer-facing layer. The point is that the decision finally gets made on ground truth.

How fast do we see value?

First ground truth in 30 days through the Portfolio Diagnostic — including quantified, evidence-linked opportunities your own teams can validate. Fully operational in 8–12 weeks. No migration, no disruption — the platform is additive to a running estate.

We’re a mid-size institution, not a global giant. Is this really for us?

Mid-size banks carry the same regulatory burden as the largest institutions with a fraction of the staff — which makes automated ground truth more valuable per employee, not less. The platform deploys in weeks and does not require an army to operate. Broad scope, lean teams, examiner-grade expectations: that is precisely the profile it serves.

See your estate the way you’ll wish you always had.

The strategic briefing

A working session with Axiarete’s team — former CIOs, platform engineers, and technology strategists — applying this framework to your environment: your regulatory posture, your modernization slate, your savings targets, your deal pipeline.

The 30-day Portfolio Diagnostic

Pick a slice of your estate. In 30 days, Axiarete rebuilds its ground truth — purpose, architecture, health, risk, cost, and the opportunities inside it — and you judge the evidence yourself.

Read-only. Additive. Operational in weeks. Your estate keeps running while it becomes understood.

info@axiarete.ai

Sources

The incidents described are public record, cited to regulator and court documents. Benchmarks are industry research, cited for orientation. The five-year TCO comparison is an illustrative Axiarete model. This page contains no customer references.

  1. McKinsey & Company, “Tech debt: Reclaiming tech equity” (2020): CIOs estimated tech debt amounts to 20–40% of the value of their entire technology estate before depreciation. www.mckinsey.com
  2. SAP LeanIX, citing McKinsey: using business capabilities to uncover redundancies, “saving potentials often range from 15 to 20%.” The $6–8M on a $40M baseline is arithmetic. www.leanix.net
  3. ITIC, 2024 Hourly Cost of Downtime Survey: a single hour of downtime costs more than $300,000 for over 90% of mid-size and large enterprises. itic-corp.com
  4. Financial Conduct Authority and Prudential Regulation Authority, TSB Bank Final Notices (December 20, 2022): combined penalty £48.65 million; £32.7 million redress paid. www.fca.org.uk
  5. U.S. Securities and Exchange Commission, In the Matter of Knight Capital Americas LLC, Release No. 34-70694 (October 16, 2013): more than 4 million executions in 154 stocks over 45 minutes; losses of more than $460 million. www.sec.gov
  6. U.S. Government Accountability Office, GAO-18-559 (2018), and the Federal Trade Commission Equifax settlement (2019, up to $700 million with the CFPB and states). www.gao.gov www.ftc.gov
  7. McKinsey & Company and the University of Oxford, “Delivering large-scale IT projects on time, on budget, and on value” (2012), a study of more than 5,400 IT projects. www.mckinsey.com