Ignite

Students need guidance that is grounded in verified education data, not generic AI advice.

Career and education decisions depend on programs, requirements, scholarships, exams, timelines, and trade-offs. Messy public information makes those decisions harder than they should be.

Ignite is being designed as a career-advisor product that combines structured education data, retrieval, citations, and AI-assisted guidance.

The product direction requires source tracking, normalized entities, clear update timestamps, and a way to distinguish verified data from incomplete or uncertain data.

Pipeline work is in development. The goal is to extract useful fields from public sources while keeping source URLs and review state attached to each record.

Normalisation is planned around duplicate handling, language variants, category consistency, and stable identifiers.

Validation is a product requirement, not a claim of a completed human-review system. The current direction is to make confidence and review state visible.

RAG is the intended guidance layer: retrieve relevant education records first, then generate advice with grounded context and clear uncertainty.

Current work is focused on the product direction, data model, early pipeline work, and guidance architecture.

Broader source coverage, complete validation workflows, evaluation sets, and public student-facing release remain planned.

Ignite is in development.