We don’t just give you a score.
We show you the math.
Admit Path recreates the admissions process, then uses evidence rather than anecdotes to coach you through it. Here is exactly how it works, because we believe you deserve to see how every conclusion is reached.
The two layers of evidence
Theory-based model
What schools say they want. We study each program’s stated admissions practices and encode what matters and how much.
- Published criteria and Common Data Sets
- Academic research and admissions office publications
- Fit frameworks such as Holland Codes (RIASEC)
Practice-based model
What schools actually do. We look at who schools actually admit to find which factors are associated with admission. Today that comes from published data; consented outcomes are added as applicants share them.
- Published admitted-student data: Common Data Sets, ABA 509 reports, AAMC and LSAC data
- Public datasets such as IPEDS
- Anonymized outcomes shared by applicants, with consent, as they come in
- Integrity checks: no duplicates, reliability thresholds
The Unified Model
Where theory and practice meet. A factor joins the model only when the evidence supports it. Where they diverge, you get a strategic insight.
- Tested against contributed outcomes as enough are shared
- Updated as committees and policies change
- Human scores feed back into the model
- Every output explained with a “Why?”
When what schools say and what they do diverge, that’s your opportunity.
Suppose a program’s published materials suggest that a certain factor, say demonstrated interest, matters only a little. If the practice-based evidence shows it is strongly associated with admission at that school, we flag it and advise you accordingly.
It works the other way too. A factor that schools mention often but that shows little effect in the admissions data gets less weight in your plan. You spend your limited time where it counts.
How a factor earns its place
Discovery, analysis, committee, report.
We collect information the way an admissions committee does, and we review it the way they do: with humans acting as an independent committee, supported by statistics.
Discovery
Three interactive questionnaires: a basic profile common to all applicants, a supplementary one for your program type (like LSAT or MCAT details), and an application questionnaire modeled on the official form.
Analysis
Your profile is compared with the practice-based profiles of admitted students and weighted using the Unified Model.
Committee review
For Application Review, independent trained readers score essays, recommendations and mock interviews using committee-style rubrics. Their scores feed the model.
Report
You get clear results, the reasoning behind them and a prioritized plan, plus a recommendation on whether and when to apply.
Results you can act on
Admit Preparation results
A high-level view early in the process, when there’s time to act.
Where you stand against admitted students, on a seven-level scale
Culture match
Recommendation
Application Review results
A specific, explained read once your application is drafted.
How your file compares with admitted students
We show whether your record sits below, within or above each school’s published ranges for admitted students, and readers score the rest of your file. It isn’t a prediction: no one knows who else will apply.
Recommendation
Committee feedback
Reader scores on criteria like intellectual vitality, personal character and narrative cohesion, with specific fixes before you submit.
Every insight comes with a “Why?”
Algorithms you can’t question aren’t worth trusting. Whenever the Unified Model produces a result, we show the factors behind it, so you can see what is driving it and act.
- Explained results: each score shows the evidence that shaped it.
- Human-reviewed badge: you always know when independent readers have verified a recommendation.
- Honest limits: we never present a single percentage as certainty.
Statistical modeling where the data is deep
Each of these programs admits hundreds of students every year, which gives us enough evidence to be confident. We don’t model outcomes where the data is too thin to be reliable. For high school admissions and lifelong learning, our guidance combines published criteria with expert review, and we always say which approach applies.
College
Selective undergraduate admissions, including Early Decision and Early Action strategy.
MBA
Work experience quality, leadership trajectory, GMAT/GRE and round strategy.
Law (JD)
LSAT and GPA positioning, plus scholarship versus ranking trade-offs.
Medicine (MD)
AMCAS, clinical and research experience, secondaries and interviews.
Consent first. Never scraped, never sold.
Our practice-based model starts from published admissions data and grows through a give-to-get approach: applicants who use Admit Path can choose to contribute their anonymized results, including acceptances, rejections and waitlists, so that the next applicant gets better guidance. Contributors unlock deeper insights in return.
Because data from the people we serve is what will make the model better, protecting it is non-negotiable.
Your consent, your choice
You decide whether to contribute outcome data. Nothing is added to the model without permission.
Anonymized before modeling
Personally identifying information is removed before any data enters the aggregate model.
You stay in control
Grant or revoke advisor access at any time, and ask us to delete your data whenever you like.
Never sold to advertisers
We use data only to improve the model and generate insights. We do not reveal any client relationship without your consent.
See where you stand.
Start with a free readiness assessment. No obligation, and no tax returns needed for sliding-scale pricing.