Productlive on the App StoreApp Store v1.1.2
Dilly turns changing job requirements into field-specific work students can complete and demonstrate. The Context Graph personalizes each recommendation. Daily moves keep momentum visible. The Skill Engine creates new evidence.

01 · Execution workbench
Where you stand. Today's priority. This week's roles and field shifts. Home and Forecast create a clear re-engagement loop, then point students toward deeper field-specific work.
02 · Context graph · the moat
Interactive profile grid: coursework, project repositories, tools, career goals. Every recommendation explicitly references real-world experience rather than generic industry fluff. Screens write back. The graph compounds.


03 · Forecast
A personal field read: judgment calls AI can't make yet, concrete moves tied to what the student has already built, and a token-gated fresh read when they want an update. This is the education product in consumer form.
04 · Skill Engine · private beta
Dilly turns a student's career goal into field-specific AI work. Each skill requires practical steps: research, AI use, verification, judgment, and a final professional deliverable.
A skill is only demonstrated once the student completes the work and creates proof.
Marketing · Market and Customer Research
05 · Scout · role + AI shift
Nightly crawl across 50+ ATS sources. Each role gets a Scout read: what you've got, what's missing, next moves, plus an honest map of what AI already does in that job and what stays human.



06 · Proof + Truth Ledger
Dilly does more than polish a resume from existing facts. Each applied skill ends in a practical artifact: a research brief, analysis, workflow, recommendation, or work sample. The artifact remains linked to the skill it demonstrates and the jobs it supports.
Architecture · what we run on
Nine slides. The ask, the product, what is verifiable today, and how the round gets spent.