Private AI inside your perimeter for Banks and Financial Institutions
Compliance, KYC/AML, risk, advisory and operations agents on an AI operating system your institution owns: in your own data center, your VPC, or fully air-gapped. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. No need to choose build vs. buy — you get both.
“ibl.ai does amazing work and are great people.”
Everything we do, for financial institutions
One platform, three ways to run it, and engineers who build inside your environment. Every piece ends up owned by the institution, not rented from a vendor.
Private by construction, not by contract
A managed AI vendor can promise not to train on your data. It cannot hand you the source code, let you choose the model, or put the logs where your examiners can read them. ibl.ai is built so the institution holds all three.
| ibl.ai | A managed AI vendor (ChatGPT Enterprise, Copilot, Claude for Enterprise) | |
|---|---|---|
| Where client data goes | ibl.aiStays inside your perimeter: your data center, your VPC, or an air-gapped network | A managed AI vendor (ChatGPT Enterprise, Copilot, Claude for Enterprise)The vendor's cloud, under the vendor's terms |
| Which models | ibl.aiAny model: Claude, GPT, Gemini, Bedrock, or open weights on your own GPUs. Switch per agent, anytime. | A managed AI vendor (ChatGPT Enterprise, Copilot, Claude for Enterprise)The vendor's own models, on the vendor's roadmap and price list |
| Who owns the code | ibl.aiThe institution: a perpetual Full Code License to the complete platform | A managed AI vendor (ChatGPT Enterprise, Copilot, Claude for Enterprise)The vendor; you rent access to it |
| Pricing shape | ibl.aiA flat license or usage. No per-seat fees, so every banker, analyst and servicing rep can use it. | A managed AI vendor (ChatGPT Enterprise, Copilot, Claude for Enterprise)Per seat, per month, whether or not the seat is used |
| Audit and examiner access | ibl.aiEvery prompt, answer and source logged on systems you control, retained on your schedule and open to your examiners and model risk team | A managed AI vendor (ChatGPT Enterprise, Copilot, Claude for Enterprise)The vendor's logs, on the vendor's retention schedule, reached through the vendor |
| What you keep if you leave | ibl.aiEverything: the code, the data, the agents and their memory, still running | A managed AI vendor (ChatGPT Enterprise, Copilot, Claude for Enterprise)An export, if the contract allows one |
MCP Architecture Guide for Financial Services
How to connect your financial systems (core banking, CRM, compliance, trading) to AI agents — with regulatory compliance.
Read the GuideWhat is an AI-native bank or financial institution?
An AI-native bank or financial institution is an institution intentionally designed around artificial intelligence as a foundational capability, integrating bankers and intelligent systems across compliance monitoring, risk assessment, KYC and AML review, client advisory and operations, while keeping fiduciary duty the mission. On ibl.ai you own all the code and the data, so client data never leaves your perimeter.
An AI-enabled bank buys a copilot seat for the analysts. An AI-native bank designs surveillance, onboarding and advisory around agents whose reasoning is reproducible and reviewable, because an examiner will ask how the answer was produced.
The systems of record are the core banking platform — FIS, Fiserv or Jack Henry — plus the CRM, the trade and surveillance systems, and the document stores that hold KYC files.
Model risk management under SR 11-7 is the binding constraint: examiners expect documented models, version control and independent validation. Owning the stack is what makes that documentable rather than a vendor attestation.
The book's governance, operations and infrastructure pillars carry over to a bank, with model validation standing in for academic peer review. See The AI-Native University.
AI Agents for Financial Services
AI Courses for Financial Services
Practitioner training grounded in primary sources. Each course publishes its full design — module outline, learning outcomes, assessment, and the regulations it cites. Request access to run one with your cohort.
AI Model Risk Management for Financial Institutions
Extend model risk governance to generative AI — inventory, validation, challenger testing, and the documentation examiners expect for a non-deterministic model.
KYC and AML with AI: Screening, Alerts, and SAR Support
Apply AI across the BSA/AML program — name screening, alert triage, and narrative drafting — without weakening the audit trail a regulator will examine.
AI Supervision Under FINRA and SEC Recordkeeping Rules
Supervise AI in a broker-dealer or RIA — communications review, books and records obligations, and what happens when an agent talks to a client.
Fraud Detection with AI: Anomalies, Alerts, and False Positives
Build AI-assisted fraud detection where a false positive is a blocked customer — anomaly detection, adaptive fraud, and fair-lending exposure.
AI for Client Advisory Without the Compliance Risk
Research and client content generation inside a regulated advisory business — sourcing, review workflow, disclosure, and the line before personalized advice.
Regulatory Reporting Automation: SOX, PCI DSS, and Audit Trails
Automate regulatory reporting and control testing with AI — evidence collection, narrative drafting, and a control environment that keeps the automation auditable.
How ibl.ai compares
The Agentic AI Operating System for Financial Services
ibl.ai gives banks, credit unions, insurers and asset managers their own Agentic AI Operating System: the platform behind Agentic OS, Agentic Vibe and Agentic LMS, deployed where the institution decides and owned outright.
Client data, trading intelligence and proprietary models never leave the perimeter you control. Any model answers, including open-weight models on your own GPUs, and no vendor can change the price or the terms.
Which banking and insurance systems do the agents connect to?
Core banking, loan origination and policy administration systems, exposed to agents through MCP servers the institution owns.
Agents answer from the system of record, with the citation, instead of from a copy.
Okta and Microsoft Entra ID for identity, Microsoft Teams and email for collaboration, and Bloomberg chat where your desks use it.
Every agent call carries the person's own identity and entitlements.
What do AI agents do at a bank or financial institution?
How does a financial institution keep control of client data and models?
Run in your own data center, your VPC, or on an air-gapped network with no internet path at all. Client records, positions and research never reach a vendor cloud.
The same code runs in every posture.
How can a financial institution run ibl.ai?
What services come with the platform?
What does it cost, and who owns it?
How do we start?
What our partners say about us
Chris Gabriel | Google
Lorena Barba | George Washington University
Dr. Juana Mendenhall | Morehouse College
Juile Diop | MIT
Adam Tetelman | Nvidia
Jason Dom | American Public University System
Benjamin Breyer | Columbia University
Ken Fujiuchi | SUNY
Erika Digirolamo | Monroe College
David Flaten | SUNY
David Vise | Modern States Education Alliance
Linda Wood | ARM Institute (U.S. Department of Defense)
Chris Gabriel | Google
Lorena Barba | George Washington University
Dr. Juana Mendenhall | Morehouse College
Juile Diop | MIT
Adam Tetelman | Nvidia
Jason Dom | American Public University System
Benjamin Breyer | Columbia University
Ken Fujiuchi | SUNY
Erika Digirolamo | Monroe College
David Flaten | SUNY
David Vise | Modern States Education Alliance
Linda Wood | ARM Institute (U.S. Department of Defense)
Frequently Asked Questions
An AI-native bank is designed around AI as a foundational capability across compliance monitoring, risk, KYC and AML, advisory and operations — not a copilot seat bolted onto the existing process.
Ownership is the test: on ibl.ai you own all the code and the data, so model documentation, version history and client data sit inside the institution where examiners expect them.
It is a complete AI infrastructure that lets financial institutions deploy, customize, and control AI agents across every function — compliance, risk, advisory, trading operations, and employee development.
Unlike closed SaaS tools, you own the entire codebase and deploy on your own servers. Client data and trading intelligence never leave your infrastructure.
ibl.ai deploys entirely on your infrastructure — air-gapped if needed. Every agent interaction is logged and auditable for SEC, FINRA, SOX, and PCI DSS requirements.
You own the full source code, control all data flows, and can demonstrate to regulators exactly how AI decisions are made.
ibl.ai integrates with Bloomberg, Refinitiv, and market data feeds. It connects to CRM platforms like Salesforce Financial Cloud, core banking systems like FIS and Fiserv, and risk platforms like SS&C.
SSO is supported via Okta, Azure AD, SAML, and smart card authentication.
Bloomberg AI and similar tools are cloud-based — your proprietary models and client data flow through their servers. ibl.ai runs entirely on your infrastructure with full code ownership.
You control the AI models, the data, and the deployment. No vendor lock-in, no per-seat pricing, and no third-party access to your trading strategies or client information.
Yes. ibl.ai is LLM-agnostic and supports models from OpenAI, Google, Anthropic, and others. You can run models locally on your own GPUs for maximum data sovereignty, or use cloud APIs where compliance allows.
Compliance agents monitor regulatory changes, generate reporting, track certifications, and prepare audit documentation — 24/7 across all offices.
KYC/AML agents automate customer verification, sanctions screening, suspicious activity reporting, and ongoing due diligence — all within your own infrastructure.
A typical pilot deploys agents in one division within a few weeks to demonstrate ROI and regulatory compliance. Firm-wide deployment scales from there.
We start with a free 30-minute assessment to identify the highest-impact opportunities for your organization.
Bring us a hard problem. Keep the code.
Run Financial Services AI on your own terms
Start free on our cloud today, or take the whole codebase into your own infrastructure. Either way our engineers work alongside your team, and you own all the code and the data.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on. · SOC 2 Type II certified
Private AI you own — for financial services
Run a self-hosted, private LLM platform on your own servers — on-premise or air-gapped. You own the code, data, and models, with no per-seat fees.