Ship faster while your quality system stays in charge. JuhJuh never validates anything; your quality unit does.
Your digital and engineering teams ship faster without loosening a single control. AI agents do the routine work of planning, coding, testing, releasing and watching your live systems, and every step lands in a record your quality, validation and audit teams can read.
For digital, engineering, quality and validation teams at drug makers, biotechs and CROs, in the US, the UK and the EU.
Where the hours and the risk go.
Every change carries its evidence.
GxP validation evidence, drafted for you
The validation AI agent drafts requirements, risk assessment and test results for each change to a GxP system, at the depth your GAMP 5 risk assessment sets, ready for your Part 11 and Annex 11 records. Your validation lead reviews and approves it.
Incident timelines for your deviation process
One incident channel holds the timeline, the evidence and the people on call. Your quality unit decides whether a deviation is needed and starts from the facts.
LIMS and batch record services
Each service, such as your LIMS and electronic batch records, gets its own channel for alerts, deploys, errors and questions. A broken build is traced, fixed on its own branch and sent to the service owner for review.
Change control with quality unit approval
Tickets for GxP systems carry the GxP impact and validation sections your procedure asks for. Where your validation lead or quality unit must approve, their approval sits in the same change record.
Inspection-ready evidence
Ticket, review, validation evidence, approvals, build, image digest and release are linked in one chain. The compliance evidence AI agent assembles the record for a system or period, and a person signs it off.
Supplier audit documents
Our engineers provide the documents your quality system asks of a supplier. One platform to assess and audit instead of a dozen AI tools.
Your science stays inside
Formulations, trial data and submissions stay in your installation. When your AI agents run only on your own models, no AI company receives them or trains on them.
Reviewed on a different model
A reviewer AI agent on a different AI model checks every change against its acceptance criteria. A person decides every merge, and an AI agent never approves its own work.
The same week, with JuhJuh.
Alerts arrive from separate monitoring tools, and the incident is worked across chat, phone and spreadsheets before anyone finds the release behind 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 to the last validated version with one tap, and results are flowing again by 2:31am.
The change is built, then the validation evidence is written by hand afterwards, and the approvals sit in a different tool from the code.
The spec AI agent writes the spec and GxP impact, the change is built tests-first and reviewed, and the validation AI agent drafts the evidence. The validation lead approves it in #validation before the release manager approves the release.
Someone spends days gathering tickets, approvals and release notes by hand ahead of the self-inspection.
The compliance evidence AI agent assembles the record from the change history, and the head of quality signs it off the same afternoon.
Every role gets time back.
A person approves anything consequential.
What your AI agents never do on their own
- Approve validation evidence or declare a system fit for GxP use. Your validation lead and quality unit decide.
- Merge a change or approve their own work. A different AI agent reviews it, and a person decides.
- Deploy to production or roll back without a person's approval.
- Decide whether an incident needs a deviation or a report to a regulator.
- Make a quality decision, such as certifying a batch. Your qualified person decides.
- Use an AI model your rules haven't approved, or send anything outside the organisation beyond approved answers.
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 your boundary, and public model families such as Claude, GPT and Gemini are added only where your rules allow.
Your formulations, manufacturing processes, trial and patient data and regulatory submissions never leave that boundary unless you choose to connect an outside service, approve a website or approve sending them out. Our engineers install it with you, and validating it for your intended use stays with your quality system.
Bring AI into your GxP work.
Start with one or two teams, inside your own boundary, with your quality unit in charge. That's the juju.
