Recommendation & Personalisation for Education & EdTech

The popularity baseline is humbling and necessary. Plenty of sophisticated systems fail to beat 'show what is selling', and you should know that before deploying one.

In education & edtech, student data protection and academic integrity constrain what may be automated at all. That single fact reshapes how recommendation & personalisation has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is assessment feedback drafting, usually integrated against assessment platforms. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. You own the code, the models where they are open-weight, and the documentation to run it without us.