Every merge approved. Every rate change 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, risk, actuarial and pricing teams at insurers, reinsurers, Lloyd's market firms and brokers, 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.
Rating changes tied to pricing decisions
A rating change AI agent writes the change to your rating code from an approved pricing decision, with tests comparing old and new premiums on sample policies. The decision, the code and who approved it sit on one record.
Business services linked to every change
Your important business services are linked to the releases and incidents that affect them, so your operational resilience team sees in minutes which changes touched making a claim.
Incident timelines ready for regulators
Every incident keeps its timeline as you work, so your incident manager starts from the facts for DORA's 4 hour notice, the NYDFS 72 hour notice and PRA and FCA first reports.
Claims and policy systems connected
Our engineers connect your policy administration, claims, billing and rating systems, so AI agents work across them inside the rules you set.
AI governance on the record
Every AI model you allow is listed, chosen per AI agent and recorded on every run, the record your AI programme and your own oversight draw on.
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.
Libraries from your approved copies
Builds use mirrored copies of the open-source packages you approve, inside your boundary, and the dependency AI agent prepares each update as a reviewed change.
The same week, with JuhJuh.
Alerts from separate tools wake the on-call engineer, who works the problem across chat, phone and spreadsheets 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 the service is healthy by 2:31am.
The decision travels by email and spreadsheet, and the code change that carries it out sits in a different tool with no link back.
The pricing lead approves the spec, the rating change AI agent builds the change tests-first, the reviewer AI agent checks every criterion and a senior engineer approves the merge.
Engineers spend days pulling tickets, approvals and deploy logs into a spreadsheet.
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
- Make a pricing decision. That stays with your actuarial and pricing leads.
- Merge a change. A person decides, after a reviewer AI agent on a different model has checked it.
- Deploy to production or roll back.
- 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.
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 policyholder data, claims and medical records, pricing models and rating factors never leave your boundary unless you connect an outside service, approve a website for your AI agents or approve sending them out.
In a connected installation you can add public model families such as Claude, GPT and Gemini for the work your rules permit. Our engineers install it with you and configure it to the controls your regulators expect and your risk and audit functions need.
Bring AI into insurance engineering, inside your boundary.
Start with one or two teams and add the rest when you're ready. That's the juju.
