Every audit-trail entry checked, not a sample
We check every change in your lab and manufacturing systems against your SOPs and approved records. Rules catch the known data-integrity patterns; an AI model spots unusual behaviour no rule covers. Your QA team decides; your eQMS keeps the record.
- ISO 13485
- ISO 14971
- IEC 62304
- IEC 62366-1
- IEC 82304-1
- IEC 81001-5-1
- EU MDR
- UK MDR
- FDA QMSR
- ISO 27001
- UK GDPR
- EU GDPR
- HIPAA
- NHS DSPT
- NHS DTAC
- DCB0129
- Cyber Essentials Plus
- EU GMP Annex 11
- 21 CFR Part 11
- GAMP 5
- ALCOA+
Find data-integrity problems before an inspector does
| Manual review | With Arithmia Assure | |
|---|---|---|
| What gets checked | A sample of entries | Every entry |
| Reviewer time | Hours per batch | Minutes per batch |
| When problems surface | At the next inspection | Within days |
| Spotting the unusual | A reviewer's experience | An AI model trained on your site's normal patterns |
| Evidence | Spreadsheets and folders | One pack a month, in your eQMS |
Fewer inspection findings
Data integrity is a common cause of findings and warning letters. Checking every entry shows a review process that works.
Faster batch release
Review in minutes, not hours, frees stock sooner.
Easier client audits
CDMOs show clients a monthly evidence pack, which helps win and keep work.
Rules and AI check every entry; your team decides
Arithmia Assure
Not an eQMS. Assure reads, checks and flags; your QA team decides; your eQMS keeps the record. The AI model is fixed and validated: it flags, it never decides. Assure is installed in your own Azure from the Azure Marketplace, so your data never leaves your cloud.
What is required
Annex 11, Part 11, your SOPs and specifications.
What looks unusual
A model trained on your site's normal patterns flags what no rule covers, with its reason.
What matters
Your risk assessments, so attention goes to critical data.
What explains it
Approved changes, deviations and maintenance clear events automatically.
Who did it
Roles, training and rosters, to catch shared logins and self-approval.
Everything is read from the systems you already run. Nobody uploads files.
Work out your QA time back
Change the figures to match your site.
- Review time cut
- 78%
- Full-time QA equivalent
- 0.4
Default figures are illustrative. We measure your real numbers in the trial.
Try Assure free for a week
We install a trial in your own Azure; you load one month of audit trails from one system, and within a week you have the findings. Nothing comes to us. In return we ask for your feedback and, if it helps you, a case study.
Install
A trial in your own Azure, loaded with one month of audit trails from one system.
Review
Assure checks every entry; our specialists confirm each flag.
Report
Findings and time saved, within the week.
Decide
If it earns its place, connect your systems and validate.
Your GxP cloud, checked every day
Arithmia Check
Read-only checks of encryption, access, logging, backups and data location against Annex 11 and Part 11, with tamper-proof evidence.
The rules you need
Validation
A validation pack for your cloud, to GAMP 5, renewed every year.
In your own account
Your GxP systems and data never leave it.
Learn from other sites' batches, without sharing your data
Federated AI · PharmaFabric UK
Models train inside each member's own cloud. Only encrypted, protected model updates leave, to build a better shared model. Manufacturers learn from each other without handing over process data, recipes or batch records.
First use case: scaling up antibody processes from 2–50 L to 500–2,000 L bioreactors. Then tech transfer, real-time release, digital twins and supply resilience. Targets are aims, not results; the demonstrator uses synthetic data.
Join the consortium
For manufacturers, CDMOs, biotechs and technology partners. Every member signs an IP-protection charter and keeps their node under their own control.
A digital twin of your process, trained on many sites' batches
Process digital twin · PharmaFabric UK
A model of one manufacturing process, starting with scaling up antibody processes from 2–50 L to 500–2,000 L bioreactors. It learns from your own batch history and, through the consortium, from other sites' batches without any raw data moving. You compare a planned run with what similar batches did before you commit a bioreactor.
Model
Build the twin from your own historical batches, inside your own cloud.
Predict
See the expected yield and the main risks for a planned scale-up run before you run it.
Compare
Check a transfer to a new scale or site against the twin and against similar batches.
Decide
A named owner signs off every model version. The twin advises; your people decide.
A concept in development, not yet a validated GxP system. Each model would be validated and change-controlled before it informs any decision. Any targets are aims, not results.
See what Assure finds in your audit trails
A free one-week trial on one system, for one design-partner site.