Every merge approved. Every release traceable. Every incident on record. All inside your firm'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 security, compliance and audit teams can read.
For engineering, platform, security, compliance, financial crime and operations teams at payment and e-money institutions, card issuers, acquirers, processors and fintechs, in the UK, the EU and the US.
Where the hours and the risk go.
Your AI agents do the routine work. Your people keep the decisions.
Fraud rule changes tested and reviewed
Where your fraud rules live in code or configuration, each change is prepared by an AI agent, tested on sample transactions with real card numbers removed and approved by your fraud team before it reaches production.
Card network changes tracked to release
Each card network and regulatory change your compliance team hands over becomes a ticket with its deadline, tracked from ticket to release, with a report on which are on track.
Card numbers masked in messages
Once you turn on message scanning, a card number in a JuhJuh message or an AI agent's reply is masked or blocked before it is saved, or flagged to your managers. Card numbers and security codes are also hidden from the logs of what AI agents do.
Payment page changes reviewed
Every new or changed piece of code on the pages where customers enter card details is checked by the security review AI agent and approved through the merge, so you know who authorised it.
Authorisation and settlement incidents
The SRE on-call AI agent opens the incident, finds the release behind it and writes the timeline as you work, so card authorisation and settlement recover fast and your incident manager starts from the facts.
Evidence for your MLRO and assessor
The compliance evidence AI agent assembles every change to transaction monitoring, sanctions screening and in-scope systems from the record, ready for your MLRO and your PCI DSS assessor once a person signs it off.
A second review on another model
A reviewer AI agent on a different AI model checks every change against its acceptance criteria, and the AI agent that wrote a change never approves it.
Releases a person controls
The release AI agent prepares the change record and rollback plan, and a person approves every deploy to production and every rollback, with blue/green releases and one-step rollback built in.
The same week, with JuhJuh.
Alerts from separate tools wake the on-call engineer, who pieces together logs, recent releases and a call with colleagues before anyone knows what changed.
The SRE on-call AI agent opens the incident, finds the release that caused it and sends a voice note. She approves the rollback with one tap, and authorisations are back to normal by 2:02am.
The rule is changed in a hurry to stop the losses, and nobody can later show who checked it or how it was tested.
She approves the spec, the fraud rules AI agent prepares and tests the change on sample transactions, the reviewer AI agent checks every criterion and a senior engineer approves the merge.
Engineers spend days pulling tickets, approvals and deploy logs from three different tools into a spreadsheet.
The compliance evidence AI agent lists every change to the payment page code and the transaction monitoring system from the record, with who approved each, for a person to sign 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 a reviewer AI agent on a different model has checked it.
- Deploy to production or roll back, including a fraud rule change kept in your code or configuration.
- Change production settings, access or secrets. AI agents prepare the facts for the person who decides.
- Decide whether to report suspicious activity. That stays with your MLRO.
- Decide whether to report an incident to a regulator. That stays with your incident manager.
- Sign off evidence for your assessor, an auditor or a regulator.
Self-hosted, connected or air-gapped
JuhJuh runs as your own installation, 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, so cardholder and transaction data, fraud rules and routing logic never leave your boundary unless you connect an outside service, approve a website for your AI agents or approve sending them out.
You and your qualified security assessor decide what is in scope for PCI DSS. JuhJuh doesn't say whether your installation is in or out of that scope. It gives you the settings and records you and your assessor can draw on, and our engineers install it with you and configure it to the controls your regulators and the card networks expect.
Bring AI into payments engineering, inside your boundary.
Start with one or two teams and add the rest when you're ready. That's the juju.
