EVIDENCE FIRSTNO PROMISESDATA YOU CAN AUDIT

Precise answersfor U.S. applications

We don't make up odds, we don't promise admits, and every conclusion can be traced back to its source.

Reach / Match / Safety in three tiers, each backed by 8 recent admission cycles + the current season's policy snapshots. Every data point is auditable — no black box.

All free tools work without an email signupEvery claim links back to its data sourceThe applicant + parents stay in the driver's seat
Pick your path by family stage

What stage is the applicant in now?

Not sure? Start with the free 10-min assessment — see where the applicant sits across 44 schools, then decide whether to go deeper.

vs. generic LLMs · COMPARISON

Same question,
different answers

Generic LLMs run on training-time data — they're prone to hallucinating numbers.

Kaiso runs on 8 recent admission cycles + current-season policy data, with every judgment leaving an audit trail.

Generic LLMsGPT / CLAUDE / GEMINI
VS
Kaiso enginePEIPAOLAB
Made-up numbers
"You have a 32–45% chance" — percentages out of thin air
Admit probability
Three-tier classification
Reach / match / safety — no fabricated admit rates
Training snapshot
Data from a year or two ago — policies have already shifted
Data freshness
Quarterly refresh
Public admit data + admissions policy refreshed each quarter
Generic POV
Defaults to US-domestic narrative — no specifics for Chinese-American families
Chinese-American context
First-hand Chinese context
7 Chinese-American hubs + ethnicity breakdown + Chinese-language scenarios
Black-box generation
Why this judgment? The model can't explain itself.
Traceable decisions
Auditable basis
13-axis scoring + comparison against similar admit cases
Data sources Public admit data · IPEDS · curated real admit cases
Evidence strength
EVIDENCE FIRST
Data driven · auditable decisions

Let the data
speak for theadmissions office

Start free assessmentBrowse all products →No signup · 2 minutes · always free