Question 1

What is an AI agent in a banking context?

Accepted Answer

Software that is given an objective, decides the steps to reach it, and operates the systems needed to carry them out, instead of following a fixed script. In a bank the systems it has to drive are usually the ones built long before anyone planned for automation, which is what makes the framework around the agent matter more than the model inside it.

Question 2

How do you automate a system that has no API?

Accepted Answer

Many critical systems in a bank have no clean integration point. Our agents read the screen, decide what to do, and do it, the same way a trained operator would. That brings automation to platforms built long before anyone planned for it, without a re-platforming programme first.

Question 3

What stops an AI agent doing something it should not?

Accepted Answer

The framework around it. An agent on its own is a demo; guardrails, logging and human checkpoints are what make it safe to put in front of real work. Runtime guardrails block unsafe actions, mask sensitive data, and stop prompt injection before it reaches your systems.

Question 4

What does AI model risk governance involve?

Accepted Answer

An inventory of the models and agents you run, controls over them, monitoring for drift and misuse, clear ownership, and an audit trail. Regulators want to see more than a model that works. They want evidence that someone controls it.

Question 5

Where should a bank start with AI?

Accepted Answer

With an assessment of your data, systems and team that picks the opportunities with real return and proves one of them. Deciding where AI is worth the effort matters as much as deciding where it is not. You come away with a roadmap you can act on and one result already in hand.

Question 6

Can we run models on our own infrastructure?

Accepted Answer

Yes. Open-weight models you host yourself, commercial models you proxy, or both. Hosting keeps control of where data goes, which is usually the constraint that decides the design in a regulated environment.

Question 7

What is RAG and where does it help in a bank?

Accepted Answer

Retrieval-augmented generation answers questions over your own documents and data, with citations back to the source. It helps wherever people currently spend hours hunting through systems. The unglamorous parts decide whether it works: ingestion, indexing, access control, and keeping the knowledge current.

Question 8

Why an AI firm with a capital markets background?

Accepted Answer

Because the hard part of AI in a bank is not the model, it is the environment: the systems it has to drive, the data it is allowed to see, and the controls it has to satisfy. Elenjical has been delivering into regulated trading and risk infrastructure since 2013, and the AI practice is built on top of that.

Question 9

Do you build, or do you advise?

Accepted Answer

Both, though the engagements that work best start with building. An engineer or a small pod sits inside your team for the length of a problem, working in your codebase, against your data and inside your constraints, so the person who hears the problem is the person who writes the code for it.

AI agents for banks and financial institutions

The problem is the environment, not the model

Operations and the middle office

Knowledge and documentation

Platform and infrastructure

Governance and control

AI services

Common questions

Start with one problem