Trusted by leading businesses
HLBNGA helps boards, executives and compliance teams use AI with the same control they expect from financial systems: a private LLM that keeps your documents away from public AI services, and practical governance for the AI tools your teams already use.
| Team | Tool | Data | Status |
|---|---|---|---|
| AML analysts | Private LLM | Procedures | APPROVED |
| Onboarding | Private LLM | Client files | APPROVED |
| Finance | Public chatbot | Client data | NOT APPROVED |
| Marketing | Public chatbot | Public info | REVIEW |
Trusted by leading businesses
Audit, risk, and compliance teams face increasing pressure to adopt AI, while regulators demand stronger evidence of control. Requirements for traceability, audit trails, data residency, and clear responsibility allocation (RACI) create significant barriers to uncontrolled LLM usage.
Organizations relying on public or opaque AI platforms often lack visibility into how outputs are generated, how data is handled, and who is accountable — exposing them to regulatory findings, penalties, and reputational damage.
Organizations are adopting LLMs faster than their ability to govern them. This creates unmanaged exposure across financial reporting, compliance decisions, and sensitive data environments. Without a formal LLM control framework, organizations face:
Decisions influenced by AI with no record of the prompt, the model, or the reasoning behind the output.
Sensitive data pasted into public tools leaves your environment — and your control — the moment it is submitted.
No clear allocation of responsibility for AI usage, so nobody formally owns the risk when an output is wrong.
When auditors or regulators ask how AI was used in a decision, there is nothing on record to show them.
Our focus remains to allow clients to benefit from AI — enhanced efficiency, insight generation, automation — without compromising their data privacy or compliance obligations.
Our private LLM runs on infrastructure HLBNGA owns and operates. Your documents and questions are never sent to public AI services such as ChatGPT, and the model does not search the open web.
Answers are drawn from your own policies, procedures and records rather than general internet knowledge, so they reflect the rules you have actually written and are easier to check.
We apply our private LLM to compliance work, from questions about your own AML/CTF program and procedures to the policy checks in Compliance Logger, so outputs stay focused, relevant and actionable.
Critical decisions remain subject to human review. Our private LLM is a tool for analysts and compliance officers, not a black-box decision maker.
We map where AI and LLMs are used across your organisation, including tools staff have adopted on their own, and record each use case in an AI use register. It becomes the single source of truth for your AI governance.
We help you set an AI policy, a list of approved tools and use cases, and clear rules on what data can go into them, so AI use moves from ad hoc experimentation to a controlled capability.
We help you decide what to record when AI supports a decision, such as the tool used, the sources relied on and who reviewed the output, so AI-influenced decisions can be explained when someone asks.
We test your AI controls and prepare an evidence pack, so you can answer an auditor’s or regulator’s questions about AI with records, not reassurances.
We give you structured questions and checks for third-party AI and LLM providers, covering where data is stored, how models are trained, contract terms and ongoing oversight.
When AI needs to work on your own documents, our private LLM runs on infrastructure HLBNGA owns and operates, so your data is never sent to public AI services or the open web.
We run our own private LLM for compliance work, so you can put AI to work on your own documents without handing them to public AI services.