Education & EdTech AI Engineering

Only if it is grounded in approved material and refuses when unsure, which is how we build it. A tutor that answers from the open web can confidently teach a mistake, so we restrict it to vetted course content with citations. Accuracy protects learning, so we design against confident wrong answers rather than hoping they are rare.

Yes, when built for it. We protect student data under FERPA and COPPA, with the strictest handling for minors, and zero-data-retention options so data is not exposed to external model vendors. Student privacy shapes the architecture from the start, because data about children carries the highest duty of care.

No, and we would not build it to. It supports educators by drafting materials, helping learners with routine questions, and surfacing who needs attention, so teachers spend time where they matter most. Grades and judgment stay with the educator. AI is a teaching aid, not a substitute for the relationship at the center of learning.

Safety is a design requirement, not a setting. Content and interactions are kept age-appropriate, grounded, and bounded, because the users are often minors. We build guardrails and human oversight in from the start, since the cost of an unsafe or inappropriate interaction in education is far higher than any convenience it might offer.

The institution does. Your content, student data, trained models, and code remain yours, under strict privacy and access controls. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single a tutoring assistant build is a modest, weeks-long project, while a wider rollout across your LMS and content systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from better learner outcomes, less educator admin, and earlier support for at-risk students, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as a tutoring assistant usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are tutoring assistants, content and grading support, learning analytics, and administrative automation. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.