Education.

For education providers, generative AI is half edtech, half compliance. Student data stays in-jurisdiction, models must not produce assessable content for students, and accessibility tooling has to meet WCAG. In practice, the first deployments cluster around document intelligence, internal copilots, structured extraction, agentic workflows: areas where evaluation criteria are objective, data is already in the system, and an evaluation harness can measure quality continuously.

Regulators-of-note for education engagements typically include OAIC, OPC, FERPA, state / territory education-department guidance. The specific regulators that bind a given engagement depend on jurisdiction and the data classes in scope, so we map this explicitly during discovery rather than assume a global posture.

We do not deploy generative AI that produces assessment content for students or interacts with under-18 users without explicit institutional governance. Staff-facing tooling only. This is a deliberate trust boundary, not a capability gap. We are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in this industry does not justify it.

Our education engagements typically sit between Director of Digital Learning, Chief Information Officer, Head of Curriculum and equivalent senior operators. The decision-maker varies by organisation, but the common thread is a leader accountable for both delivery and downside.

In education, generative AI is most defensible when applied to curriculum drafting assistance, accessibility tooling, and administrative document automation. The same techniques look superficially similar across industries, but the data shapes, evaluation criteria, and adverse-event posture differ enough that copy-pasting an engagement from another industry usually produces a system that fails the first audit it sees.

We spend two weeks with your leadership and operators, rank the use cases that are worth doing, and come back with a clear plan and a clear price. We never start with a six-month strategy exercise. We go after the smallest piece that produces real value first.