Education & EdTech

Deploy JaiCode into education and EdTech product missions where learner data boundaries, accessibility, and gated review matter as much as feature velocity.

Learning platforms and campus products must move faster without treating student data, accessibility, or institutional review as afterthoughts. JaiCode puts Plan before Build and path grants around sensitive stores so GenAI accelerates product work while campus IT and product leads keep control of what agents can see and what merges.

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Missions

Challenge → Platform → FDE → Outcome

↳Learning product thin slice with Plan before Build

Challenge: An EdTech product team wanted GenAI velocity on a new learning workflow, but prior chat-sidebar codegen skipped scope lock, broke accessibility assumptions, and created expensive rework before instructors ever saw a stable slice.
Platform: Ask mapping of learner journeys; context packs from product briefs and accessibility requirements; Plan unlock; Build with role agents; Agent Review before every drop. BYOK under institutional procurement.
FDE: FDEs Align on success signals with product and campus stakeholders, Prove a thin learning slice, Ship MLP stories with Definition of Done that includes Plan and Review disposition.
Outcome: A shippable learning MLP with less rework after Plan, clearer evidence at each gate, and a cadence the product squad could keep.

↳Brownfield campus tooling modernization under path grants

Challenge: Campus tooling lived on brittle legacy systems. Student and faculty data made unbounded agent access unacceptable, yet leadership still needed modernization without a multi-year rewrite freeze.
Platform: Local-first agents against the client checkout; path grants and run policy away from sensitive student stores; Plan strangler cuts; Agent Review on every generative cut.
FDE: Embedded FDEs mapped as-is in Ask with campus IT, Planned thin strangler slices, and paired until client eng owned path grants and review ceremonies.
Outcome: Thin modern slices in production with bounded risk and generative history campus security could audit.

↳Enablement so client eng keep Plan → Review cadence

Challenge: Leadership wanted GenAI delivery without becoming dependent on an outside ticket factory once the first MLP shipped.
Platform: AI Boutique Shop / AI in a Box-style enablement path: modes, context packs, companion plan history, review ceremonies, provisioned seats under the org.
FDE: FDEs paired on Ask/Plan/Build and Agents Window until campus and product eng ran gates without babysitting.
Outcome: Practice transfer—gated GenAI cadence sticks after the embed, not just a one-off feature drop.

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