How the AI produces the report — honestly.
Everything in your report traces to one of two places: something in your student's uploaded documents, or a piece of institutional data we maintain for the school in question. Here's exactly how that works.
What the AI actually reads
The AI ingests the documents you upload: your student's transcript, test score reports, AP/IB scores, activities list, and high school profile. It extracts structured information: every course and grade by semester, every test sitting and section, every activity's hours, years, and role descriptions. It does not read Common App essays. It does not read letters of recommendation. It does not receive demographic fields like race or ethnicity.
How the AI reasons about admissions
The AI uses a detailed admissions-domain prompt we've built and iterate on continuously. The prompt encodes how admissions officers actually read files at selective schools: academic rigor first, then achievement within rigor, then extracurricular depth and leadership, then context (school profile, geography, first-gen status). Every section of the report is produced by the model walking through that evaluation explicitly, not by pattern-matching on a training set of prior outcomes.
How probabilities are produced
Probabilities are computed by a deterministic, catalog-anchored function — not by the AI. The AI identifies which qualitative factors apply at each school (named accomplishment, ED, first-generation status, legacy ties, etc.); the server then computes the probability from a closed-form formula: a per-major base admit rate, plus tier-aware adjustments for GPA fit, test fit, and the factors the AI flagged. The same student profile produces the exact same probability run-to-run.
Live institutional data
Every probability is anchored to a structured catalog row that the server owns. The base catalog covers 1,500+ US four-year colleges loaded from the US Department of Education's College Scorecard — admit rates, ACT and SAT middle-50% bands, urbanicity, public/private, enrollment. On top of that we layer hand-curated data from Common Data Set filings for the most-applied-to schools, plus a regression-derived layer for schools where we don't yet have CDS-quality data. Schools without catalog coverage are excluded from the probability chart and listed in an explicit notice — we don't guess.
What we deliberately don't do
Much of the credibility of a college advisor comes from what they refuse to do. The AI doesn't invent outcomes data it doesn't have. It doesn't score race or ethnicity. It doesn't read essays. It doesn't claim single-point precision where admissions is genuinely stochastic. And it doesn't scare families with catastrophizing — concerns are ranked by their actual probability impact, not by how alarming they sound.
Your data, never trained on, never shared
Your student's documents and the profile we extract from them stay inside our infrastructure. They're never used to train models — ours or our LLM provider's — never sold, and never shared with universities, testing organizations, or admissions consultancies. From Account → Delete My Data you can wipe everything; backups roll off within 35 days.
Grounding and verification
Every claim in the report has to be traceable — either to something in the student's uploaded documents, or to the school brief for the institution in question. Before a report ships, an automated check walks the document and verifies that every numeric claim and every school-specific fact appears in one of those two sources. If something doesn't ground, the report is regenerated.
Questions about how we work?
Email methodology@collegesignal.ai. We answer every one personally.