Deliver faster for patients and clinicians, with clinical safety and information governance kept in place.
Your digital teams deliver faster without loosening a single control. AI agents do the routine work of planning, building, testing, releasing and watching your systems, and every step lands in a record your clinical safety, information governance and audit teams can read.
For digital, engineering, platform, security and information governance teams at NHS trusts, integrated care boards, GP federations, NHS England and the health technology suppliers who build for them.
Where the hours and the risk go.
Clinical safety and governance, kept current as you build.
Hazard logs and safety cases drafted
Hazard log entries and safety case updates are drafted from every change to a clinical system, for your clinical safety officer to review and sign off under DCB0129 and DCB0160.
Evidence packs for your DSPT
Change, access and incident records are ready as evidence for the Toolkit outcomes they cover. An AI agent assembles the pack, and a person checks and signs it off.
Incident timelines for NIS reporting
Every incident keeps its severity, timeline, people and post-mortem in one record, so your incident manager starts from the facts when NIS reporting clocks are running.
Change records from ticket to release
Ticket, acceptance criteria, code review, clinical safety sign-off, approvals, build and release are linked, so anyone can follow a live change back to the request.
EPR and FHIR integration
Our engineers connect your electronic patient record, GP clinical systems and integration engine through their APIs and FHIR interfaces, where each supplier and NHS England's onboarding allow.
AI inside your boundary, not shadow AI
One AI workspace with AI models you run and approve, so staff get AI help without patient information leaving your cloud or data centre.
Every change reviewed twice
A reviewer AI agent on a different model checks each change against its acceptance criteria, and a person decides the merge.
Alerts turned into one issue card
Uptime, logs, metrics and traces become one card per problem that names the likely cause, beside the release that caused it and its rollback plan.
Builds from your approved libraries
Builds use mirrored copies of the open-source packages you approve, inside your boundary, and each update arrives as a reviewed change.
The same week, with JuhJuh.
Alerts from separate monitoring tools wait for someone to correlate them, and the runbook is hard to find at that hour.
The SRE on-call AI agent opens the incident, finds the release behind it and sends the on-call engineer a voice note. She approves the rollback with one tap, and results flow again by 2:31am.
The change is built, then the hazard log is updated by hand long after it, and the safety evidence trails the release.
The spec AI agent writes the spec, the engineer AI agent builds it tests-first and the clinical safety AI agent drafts the hazard log update. The clinical safety officer signs it off the same day.
Someone spends days gathering approvals, releases and access records from several tools.
The assurance evidence AI agent assembles it from the record by 5pm, and the head of digital and 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 or approve their own work. A different AI agent reviews it, and a person decides.
- Make a clinical safety decision or sign off a safety case. That stays with your clinical safety officer.
- Deploy to live systems or roll back without a person's approval.
- Decide whether to report an incident. Your incident manager and senior information risk owner decide.
- Use an AI model your rules haven't approved, or reach data the person behind them can't see.
- Send anything outside the organisation beyond the answers you've approved in advance.
Self-hosted inside your trust's boundary
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, and public families such as Claude, GPT and Gemini are added only where your rules allow.
Patient information, code and know-how stay inside that boundary unless you connect an outside service, approve a website for your AI agents or approve sending something out. Our engineers install it with you and stay to build your services, processes and systems into it.
Start with one or two teams.
Talk to us about your organisation, and add the rest of your teams when you're ready. That's the juju.
