Kaiso
PeiPaoLab's in-house admissions matching engine · anchored in real admissions outcomes · upgraded each cycle
About Kaiso
Kaiso is PeiPaoLab's fully in-house admissions matching engine. Anchored in real admissions cases 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.
Profile decomposition
Break the applicant's academics, activities, recommendations, and intent into fine-grained items, accounting for school- and identity-specific context.
Peer sample matching
Find peer samples with similar conditions in the 8-cycle real admits pool, bucketed by identity, school type, region, and intended major.
Policy calibration
Hard policy constraints for the current cycle feed directly into tier adjustments, re-calibrated quarterly.
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
Data pool
- 44 core US schools × 8 cycles of real admissions samples
- High-density Chinese-American high-school baseline pool, across regions and curricula
- Peer samples 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.
- v7.02026-05-25Full-pipeline data alignment & validation layer
- Full-pipeline data alignment & validation layer v1 launched
View earlier versions (6)
- v6.02026-05-22Report release review layer
- Report release review layer v1 launched
- v5.02026-05-21Report consistency validation engine
- Report consistency validation engine v1 launched
- v4.02026-05-18Institutional data verification engine
- Institutional data verification engine v1 launched
- v3.02026-05-18Self-healing coordination layer
- Self-healing coordination layer v1 launched
- v2.02026-05-11Admissions 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
- v1.02026-04-15Kaiso 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→