AI Readiness Assessment for Education & EdTech

Most AI roadmaps fail on data readiness rather than ambition. We audit the data behind each candidate use case, so the sequence reflects what is actually buildable now.

In education & edtech, student data protection and academic integrity constrain what may be automated at all. That single fact reshapes how ai readiness assessment 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 content adaptation by level, usually integrated against LMS. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.