EdTech and LMS Vendors
EdTech platforms and learning-management vendors want to ship AI features into schools, but every school client demands a data-processing agreement that bars sending child data to third-party cloud models. The cloud route stalls the roadmap: each new AI capability reopens DPA negotiation, sub-processor disclosure, and parental-consent questions under UK GDPR Article 8. Mickai white-labels the sovereign engine as an OEM build that runs inside the vendor's own data centre, on hardware the vendor owns under keys it holds, so pupil records are never sent to an external model provider. The vendor gains native AI features with a strong child-data privacy posture and removes the third-party-processor exposure that blocks the sale; the data-controller obligations to its schools remain the vendor's own.
EdTech platforms and learning-management vendors selling into schools and trusts.
Data-processing-agreement friction with school clients blocks AI features, because every capability that sends child data to a third-party cloud model reopens DPA, sub-processor and consent negotiation.
White-label the sovereign engine as an OEM build inside the vendor's own data centre, on hardware it owns under keys it holds, so AI runs without sending pupil data to an external model provider.
Native AI for schools with a strong child-data privacy posture that removes the third-party-processor friction, while the vendor keeps its own data-controller obligations.
Five advantages hold across every sector, and they are architectural, not promotional. The third-party cloud-exposure vector is removed; your own physical, insider, and compliance controls remain yours.
The data never leaves your hardware, so no third party and no cloud-provider employee ever sees it. What happens in the server room stays in the server room.
You own the compute and the capability, so the system runs independent of the internet and of any cloud vendor's pricing, terms, or availability.
The data never crosses a geographical or digital border because it never leaves the building, which removes the cross-border-transfer and third-party-processing friction of UK GDPR, Schrems II, and the sector rules. You keep your own obligations.
Fine-tune and run retrieval on your deepest archives to build a hyper-customised co-pilot, with no risk of your proprietary edge training a public model or leaking.
After the hardware and licence, queries cost essentially electricity. A capital asset you own and depreciate, instead of volatile per-token cloud bills.
There is no third-party cloud path, so no competitor and no vendor insider can scrape, intercept, or subpoena your prompts or your fine-tuned weights from the internet. The trust vault is closed by architecture.
You own the software snapshot on your own hardware, so a change to a cloud vendor's terms, a model deprecation, or an outage cannot reach you. The system stays predictable and auditable on-premise as the rules evolve.
The specific rules that bar mainstream cloud AI from this sector's regulated data. Each one demands a named, auditable perimeter the operator controls, which a shared multi-tenant cloud cannot give.
The kind of organisation this serves, named illustratively from public information to characterise the market. These are target profiles, not customers: Mickai has no relationship, engagement, trial, or endorsement with any of them.
The enterprise studios that lead in this sector, drawn from the eighteen that sit on the one sovereign substrate. Each runs on hardware the organisation owns, under one set of operator-held keys, writing to one Open Audit Record.
Training and LMS
Core OEM surface: powers adaptive learning, content generation, marking support and tutoring inside the vendor's LMS, with pupil data processed in the vendor's own data centre rather than an external model.
Compliance and Regulator Mode
Produces the auditable record schools and the ICO expect, evidencing where child data is processed and that no third-party model provider receives it; the vendor keeps its own controller obligations.
Executive BI
Gives the vendor and its school clients usage, attainment and engagement analytics computed in-house, so reporting dashboards never ship pupil-level data to an outside analytics cloud.
CRM
Manages the school-client relationship, renewals and DPA status against accounts without exposing the underlying pupil records to an external CRM cloud.
HR
Handles vendor staff and, where relevant, school-staff records inside the same sovereign perimeter, keeping employee data off third-party HR clouds.
See all eighteen on the sovereign services catalogue.
Schools are under sustained pressure to adopt AI while their data-protection duties around child data tighten, leaving LMS vendors caught between roadmap demand and DPA gridlock. A sovereign OEM engine that lets a vendor say pupil data never leaves its own infrastructure turns the privacy objection into a differentiator and opens the slower-moving, compliance-led segment of the education market that cloud-only rivals struggle to close.
Money won, money saved, risk removed, on hardware you own.
The vendor unblocks AI features that DPA friction had stalled, shortening sales cycles with privacy-led school buyers and converting deals that cloud-only competitors cannot clear. Pupil data is processed on the vendor's own hardware, removing the third-party cloud-exposure vector and the recurring per-token model spend, while physical and insider controls and the vendor's controller obligations to its schools remain its own.
Map the sovereign stack to your edtech and lms vendors estate.
Briefings are for organisations weighing a sovereign, on-premises deployment. Tell us about your estate and we will walk the pack, the regulatory crosswalk, and the deployment that fits your estate.