Every merge approved. Every release traceable. Every incident on record. All inside your 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 and operations teams in banks, payment firms, insurers, defence and government and their suppliers, in the US, the UK, Ireland and the EU.
Where the hours and the risk go.
Faster engineering, with every control in place.
The engineering pipeline inside your boundary
Tickets become reviewed, tested changes on the AI agents' own isolated computer, in your cloud or data centre. Ticket, review, approval, build and release are linked in one record.
Confidential information kept inside
Messages, files and voice notes stay in your installation, and AI processing runs only on models your rules approve. On your own models, no AI company receives your prompts or trains on them.
Builds from your mirrored libraries
Our engineers set up mirrored copies of the open-source packages you approve, so builds never need the public internet for a dependency. Updates arrive as changes for review.
Incidents against DORA and NIS2 clocks
Each incident gets a timeline, the evidence and the people on call as it happens, so your incident manager meets the 4-hour DORA notice and the 24-hour NIS2 early warning from the facts.
Evidence for auditors and regulators
Every action, approval and release is recorded as it happens. The compliance evidence AI agent assembles the record for a control, a period or a release, for a person to sign off.
Model risk support
Every AI model you allow is listed and chosen per AI agent, and every run records which model did what, for your model risk management.
Every change reviewed on another model
A reviewer AI agent on a different AI model checks every change against its acceptance criteria, and a person decides the merge. Authors never approve their own merges by default.
One issue card per problem
AI connects uptime, logs, metrics and traces into one card that names the likely cause beside your release history, so engineers start from an explanation rather than a wall of alerts.
The same week, with JuhJuh.
The on-call engineer works the incident across chat, phone and separate monitoring tools, and the timeline is written up afterwards.
The SRE on-call AI agent opens the incident, finds the release that caused it and sends her a voice note. She approves the rollback with one tap, and the service is healthy by 2:31am.
Someone notices eventually and spends the afternoon working out which library update broke which test.
The engineer AI agent traces it to a test broken by a library update, prepares a fix on its own branch and messages the service owner, who sends it through the usual review.
Evidence is gathered by hand from three different tools.
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, deploy to production or roll back. A person decides.
- Approve their own work. A different AI agent on a different model reviews it, and a person approves.
- Change production settings, access or secrets, or add a library to your approved list.
- Use an AI model your rules have not approved.
- Decide whether to report an incident to a regulator. Your incident manager decides.
- Sign off evidence for an auditor or a regulator.
Self-hosted only, 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 with no connection to the public internet. Open-weight models from the Mistral, DeepSeek, GLM and Kimi families run inside it, and public model families such as Claude, GPT and Gemini are added only where your rules allow.
When your AI agents run only on your own models, your sensitive information, trade secrets and know-how never leave your 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 and your accreditation need.
Bring AI into your engineering teams, inside your boundary.
Start with one or two teams, then add the rest when you are ready. That's the juju.
