PeiPaoLab

Kaiso

PeiPaoLab's in-house admissions matching engine · anchored in official admissions data · upgraded each cycle

About Kaiso

Kaiso is PeiPaoLab's fully in-house admissions matching engine. Anchored in official admissions data and re-calibrated each application cycle, it gives every applicant an auditable Reach / Match / Safety breakdown.

  • Fully in-house

    Profile pipeline, matching logic, policy calibration, and explanation layer all built end-to-end.

  • Anchored in admissions

    We rely on real historical offer distributions, not weighted averages of public rankings.

  • Cycle-aware tuning

    Hard policy constraints for the current cycle enter inference directly, recalibrated every quarter.

Four-stage pipeline

From applicant inputs to Reach / Match / Safety output, every Kaiso step is broken into auditable nodes.

  1. 1

    Profile decomposition

    Break the applicant's academics, activities, recommendations, and intent into fine-grained items, accounting for school- and identity-specific context.

  2. 2

    Distribution fit

    Position you within the past-8-cycle official admissions data distribution, bucketed by identity, school type, region, and intended major.

  3. 3

    Policy calibration

    Hard policy constraints for the current cycle feed directly into tier adjustments, re-calibrated quarterly.

  4. 4

    P-rank scoring

    13 scoring components are parsed independently — every tier landing can be traced back to which item pushed it up or pulled it down.

Specific weights, thresholds, and bucketing strategies are PeiPaoLab core IP and are not disclosed.

Data sources and cadence

  • 44core US schoolsTop 30 + high-density Chinese-American family targets
  • 8cycles of admissions samplesofficial admissions data from the past 8 cycles
  • 13+Chinese-American hubs coveredcross-region high-school baselines · including domestic tier-1/2 international tracks
  • 7policy variable categoriesrolling calibration to the admissions environment
  • 40+fine-grained profile itemsacademics / activities / recs / intent / holistic
  • 13P-rank dimensionsscoring core v2 · in-house algorithm

Data pool

  • 44 core US schools × 8 cycles of official admissions data
  • High-density Chinese-American high-school baseline pool, across regions and curricula
  • Admissions data stratified by application intent — avoids global-average distortion

Update cadence

  • Fully retrained each year as a new admissions cycle closes
  • Policy variable layer recalibrated quarterly
  • Each report is version-stamped — conclusions are traceable

Iteration timeline

Kaiso is not a one-shot product — admissions policies shift every quarter, data grows every year, and inference logic is recalibrated alongside. Each upgrade is version-stamped, so any conclusion in a report is traceable to a specific version.

v9.02026-10-17

Applicant-pool tiering

  • Applicant-pool tiering v1 launched
View earlier versions (8)
  1. v8.02026-09-23

    Time-aware reasoning & customer-facing consistency calibration

    • Time-aware reasoning & customer-facing consistency calibration v1 launched
  2. v7.02026-05-25

    Full-pipeline data alignment & validation layer

    • Full-pipeline data alignment & validation layer v1 launched
  3. v6.02026-05-22

    Report release review layer

    • Report release review layer v1 launched
  4. v5.02026-05-21

    Report consistency validation engine

    • Report consistency validation engine v1 launched
  5. v4.02026-05-18

    Institutional data verification engine

    • Institutional data verification engine v1 launched
  6. v3.02026-05-18

    Self-healing coordination layer

    • Self-healing coordination layer v1 launched
  7. v2.02026-05-11

    Admissions signal source v2 · multi-channel precision upgrade

    • Admissions signal source layer v1 → v2
    • Standardized testing fine-grained signal channel v1 launched
    • Application strategy context layer v1 launched
    • Recommender network recognition sub-module v1 launched
    • Rigor signal channel v2 → v2.1
    • EC type component v1.2 → v1.3
    • High school dual-track recognition v1 launched
    • One-shot import privacy channel v1 launched
  8. v1.02026-04-15

    Kaiso engine initial release

    • Kaiso engine v1.0 initial release
    • P-rank scoring dim component v1 launched
    • 4-stage pipeline baseline established
    • Sprint Pack judgment chain v1 launched
    • Positioning Diagnosis judgment chain v1 launched
    • Growth Profile judgment chain v1 launched

Specific feature lists, inference weights, and data pool composition are PeiPaoLab core IP; release notes only disclose capability dimensions.

Run Kaiso for free

Spend two minutes entering the applicant's basics, and Kaiso returns a Reach / Match / Safety tier breakdown with policy assumption notes. No email required.

Start the quick assessment