Every merge approved. Every release traceable. Every incident on record. All inside your bank's own boundary.
Your engineering teams ship faster without loosening a single control. AI agents do the routine work of planning, coding, testing, releasing and watching production, and every step lands in a record your risk, compliance and audit teams can read.
For engineering, platform, security, operations, risk and resilience teams at retail, commercial and investment banks, building societies and their technology suppliers, in the UK, Ireland, the EU and the US.
Where the hours and the risk go.
The controls your regulators expect, built into the work.
Your engineering pipeline inside your boundary
Tickets become reviewed, tested changes built and released inside your own cloud account or data centre. A person decides every merge and every deploy to production.
Changes linked to important business services
Releases and incidents are linked to the important business services they affect. Your resilience team can ask which changes touched card payments this month and get the list from the record.
A model risk inventory
Every AI model you allow is listed and chosen per AI agent. Every run records which AI agent acted and on which model.
Incident reporting clocks
The incident timeline is written as you work, so your incident manager starts from the facts for the DORA initial notice within 4 hours and the NYDFS notice within 72 hours.
Information barriers for deal teams
Keep inside information in projects and channels open only to the people who need it, with only AI agents on your own models available there.
Evidence for audit and resilience teams
Ticket, review, approvals, build and release are linked in one chain per change. The compliance evidence AI agent assembles a pack for a control, a period or a release, for a person to sign off.
A reviewer on a different model
A reviewer AI agent on a different AI model checks every change against its acceptance criteria. By default the person who wrote a change can't approve its merge.
Libraries from your approved copies
Builds use mirrored copies of the open-source packages you approve, inside your boundary. Adding a new library or source stays with a person.
The same week, with JuhJuh.
Alerts from separate monitoring tools wake someone up, and the incident is pieced together across chat, phone and spreadsheets before anyone can classify it.
The SRE on-call AI agent opens the incident, finds the release that caused it and sends the on-call engineer a voice note. She approves the rollback with one tap, and the incident manager classifies it at 9am with the timeline already written.
Someone cross-references change tickets, deploy logs and the service map by hand.
The compliance evidence AI agent lists the changes from the record.
Evidence is gathered by hand from three tools before the audit can start.
The compliance evidence AI agent assembles the record, and the head of engineering signs it off.
Every role gets time back.
A person approves anything consequential.
What your AI agents never do on their own
- Merge a change. A person decides, after the reviewer AI agent and your branch checks have passed.
- Approve their own work. A different AI agent on a different model reviews it, and a person approves.
- Deploy to production or roll back without a person's approval.
- Change production settings, access or secrets. AI agents prepare the facts for the person who decides.
- Decide whether to report an incident to a regulator. That stays with your incident manager.
- Sign off evidence for an auditor or a regulator, or use an AI model your rules haven't approved.
Self-hosted, connected or air-gapped
For a bank, the whole workspace runs inside your own boundary: in your own cloud account or your own data centre, connected or fully air-gapped. Open-weight models from the Mistral, DeepSeek, GLM and Kimi families run inside it, and in a connected installation you can add public families such as Claude, GPT and Gemini only for the work your rules permit.
When your AI agents run only on your own models, your customers' data, your trading and pricing logic and your know-how never leave that boundary unless you connect an outside service or approve sending them out. Our engineers install it with you and configure it to the controls your regulators expect.
Bring AI engineering inside your bank.
Start with one or two teams inside your boundary and add the rest when you're ready. That's the juju.
