Loading...
Admissions decision engineRecommendations in seconds

AI UniversityAdmissions.

Your admissions handbook, running as a live engine. The language model reads. Your rules decide. Your team validates - in seconds.

Recommendation Feed · LiveStreaming
4processed this minute60s window
#800
A. Okafor
MCom · Finance
gpa 3.6 ✓ielts 7.0 ✓prereqs 3/3 ✓
Unconditional
#823
L. Zhang
BEng · Mechanical
gpa 3.2 ✓ielts-w 5.5prereqs 2/3
Conditional
#846
R. Kapoor
MDSc · Data Science
gpa 3.8 ✓toefl 108 ✓prereqs 4/4 ✓
Unconditional
#869
S. Abdallah
MPH · Epidemiology
gpa 2.9ielts 6.5 ✓
Refer
#892
M. Tanaka
MBA
gmat 720 ✓work_exp 4y ✓ielts - missing
Missing
transcript.pdfielts_trf.pdfhsk_6_cert.pdfpassport.jpgstatement_of_purpose.pdfpte_score.pdfbachelor_transcript.pdfgmat_score.pdftranscript.pdfielts_trf.pdfhsk_6_cert.pdfpassport.jpgstatement_of_purpose.pdfpte_score.pdfbachelor_transcript.pdfgmat_score.pdf
In productionTop-5 Australian universityFive active pilotsIntegrates with existing SIS & CRM
Your rules, your team

Your handbook, as a decision engine.

AutoEnrol captures your admissions rules - including the ones that only live in evaluators' heads - and runs them as a live engine your team validates. Institution-specific, auditable, and consistent by design.

STEP 01
Discover

Your rules, finally in one place.

Most admissions rules live in individual evaluators' heads. We work with your team to surface them - thresholds, prerequisites, country equivalencies, exceptions - and put them somewhere the whole institution can see. Often the first time your rule set has existed as a single artifact.

admissions_policy_2025.pdf
course_requirements.xlsx
country_equivalencies.xlsx
english_language_thresholds.pdf
STEP 02
Process

The language model reads. Your rules decide.

Transcripts, certificates, and test scores read by a language model trained on global credentials. Every field extracted, every value evaluated against your rules. Same input, same output. Low-confidence cases route to your team for validation.

gpa 3.42 ≥ 3.0 ✓
english
ielts_overall 7.0 ≥ 6.5 ✓
ielts_writing 6.5 ≥ 6.0 ✓
prerequisites 3/3 ✓
visa_risk low ✓
recommendationACCEPT
STEP 03
Recommend

Recommendations in seconds. Validated by your team.

Accept, Conditional, Missing Information, Reject, or Refer - every recommendation ships with a human-readable rationale and decision lineage back to the source documents. Your team reviews, validates, and owns the final call.

A. Okafor · MCom Unconditional
L. Zhang · BEng Conditional
R. Kapoor · MDSc Unconditional
D. Silva · MPH Reject
Global complexity, operationalized

Messy documents in. Clean data out.

Transcripts, certificates, test reports, passports - scanned, photographed, low-resolution, multi-page. Native coverage of 100+ countries and credential systems, with bounding-box traceability back to the source page. This is where AI is - and only where AI is. Your rules still decide.

99.7%
extraction accuracy

Measured across transcripts, degree certificates, English test reports, and government-issued IDs - including photographed pages, skewed scans, and non-Latin scripts.

  • Transcripts & academic records
  • Degree & completion certificates
  • IELTS · TOEFL · PTE · Duolingo
  • Passports & national IDs
  • Statements of purpose & references
  • 100+ countries and credential systems
Extracting · transcript_undergraduate.pdfReading
ORIGINAL
Registrar
Date
L7.0
R7.5
W6.5
S7.0
Structured data
institutionUniv. of Melbourne
gpa3.42 / 4.0
credentialBSc (Hons)
english7.0 overall
conferred2024-11-15
confidence99.7%
Decisions in seconds

Faster, consistent, validated.

Compressed time-to-decision. Policy-aligned outcomes. Full decision lineage. Evaluators focused on the cases that actually need institutional judgement.

Per-application review
<1min

Compressed from the 45-minute manual baseline to machine speed. Every file, every time.

Time-to-recommendation
60sec

To process 100 applications end-to-end. Documents in, recommendations out.

Extraction accuracy
99.7%

Across transcripts, certificates, test scores, and supporting documents - including photographed pages and non-Latin scripts.

Recommendation consistency
100%

Rules-based evaluation produces identical outcomes on identical inputs. Same file, same answer, every time.

01

First credible offer wins

Students multi-apply and compare time-to-decision. Institutions that decide in days capture the applicants institutions that decide in weeks have already paid to generate.

02

Consistency as fairness

Your criteria applied identically to every application, every time. Fairness becomes repeatable execution, not aspiration.

03

Every recommendation explainable

Every recommendation traceable to a specific rule and a specific document. Human-readable rationale, generated alongside the recommendation - not reconstructed afterwards.

04

Your team, not a bystander

Evaluators validate every recommendation rather than rubber-stamp it. AutoEnrol sits under the team, not above it - the institution stays visibly in control of who gets in.

Build vs buy

The logic iceberg.

Every institution that has tried to build its own admissions rule engine has discovered the same thing: the rule surface is deeper than it looks. One large provider reached roughly 8,500 rules before the project was paused. AutoEnrol is what you switch to when your in-house team hits the asymptote.

Above the waterline~200 rules

Published entry criteria

GPA thresholds, English requirements, subject prerequisites. The rules you can hand to an intern.

Mid-depth~2,000 rules

Country equivalencies and overrides

Qualification mappings, program-by-program overrides, regional institution tiers. The rules your senior evaluators carry in their heads.

Below the waterline8,000+ rules

Edge cases and institutional memory

Conditional pathways, exception patterns, edge credentials, fraud signals. The rules nobody has written down - including the ones you don’t know you apply.

Redeploy your engineers to the rest of the roadmap.

Built for admissions leadership

One platform, four mandates.

AutoEnrol doesn't ask any stakeholder to compromise their core responsibility. Each persona gets what they actually need - without trading off the others.

VP Enrollment

Win the students you already attracted.

Faster credible offers protect yield. In a market where students multi-apply and compare time-to-decision, slow evaluation quietly leaks class quality you've already paid to generate.

Director of International Admissions

Throughput without burnout.

Absorb peak-cycle surges without adding headcount or compromising review quality. Evaluators move off repetitive checks and onto the decisions that actually need judgement.

Admissions Evaluators

The system proposes. You decide.

AutoEnrol handles the rule-bound baseline and flags the cases that actually need your expertise. You validate recommendations, not rubber-stamp them. The platform sits under the team, not above it.

CIO / IT

Fits your stack. Skips the internal build.

ISO 27001. Regional data residency. API-ready for SIS and CRM integration. Pilots run with zero production touch. And your engineers don't spend the next two years building a rule engine that never finishes.

A low-risk way to get started

The 150-app pilot.

See AutoEnrol's recommendations next to your team's manual outcomes - rule-by-rule, with zero integration and zero policy compromise. Your team validates; the pilot shows you where the engine agrees and where it doesn't.

Start a pilot 2–4 WEEKS · KICKOFF TO RESULTS
  1. Up to 150 anonymised applications

    Across three courses you choose. Real historical applications, fully de-identified.

  2. No technical integration required

    No API hookups, no SIS changes. Data flows via a secure transfer - nothing touches production.

  3. Side-by-side comparison with your team

    Every AutoEnrol recommendation benchmarked against your manual outcome, rule-by-rule.

Security & compliance

Built for institutions that can't take risks.

University-grade data handling, from day one of a pilot through full production deployment.

ISO 27001

Certified information security management - independently audited controls over data handling and confidentiality.

Data residency

Processing and storage in-region by default. AU, EU, UK, US options available.

De-identified pilots

Pilot data flows in fully anonymised - nothing identifying ever leaves your institution.

Rules-based & auditable

Every recommendation carries a complete rule-by-rule audit trail you can replay at any time.

Frequently asked

The questions procurement asks first.

Built for the concerns procurement, IT, and faculty raise before anyone signs anything.

Is this another black-box AI making admissions decisions?
No. AutoEnrol produces recommendations, not decisions - your team validates every one. Recommendations are generated by rules derived from your handbook, so the same inputs produce the same outputs every time. AI is used only where it is strongest and safest: reading documents, extracting fields, and drafting rationale. The rule logic itself is explicit, reproducible, and auditable.
Will we have to change our admissions policy to use AutoEnrol?
No. Your handbook is the source of truth. We encode your thresholds, your prerequisites, your country equivalencies, your exceptions - not a vendor scorecard. Your standards stay yours.
What happens if a regulator asks us to justify a specific recommendation?
Every recommendation carries a full rule-by-rule audit trail, a human-readable rationale, and bounding-box traceability back to the source documents. The explanation is generated alongside the recommendation - not reconstructed afterwards.
Does this replace our admissions evaluators?
No. The system proposes; your team validates. AutoEnrol handles the rule-bound baseline - low-confidence cases, policy exceptions, and edge credentials are flagged for closer review - so experienced evaluators spend their time on the cases that actually need institutional judgement. It sits under the team, not above it.
How do you handle credentials from countries we rarely see?
Native coverage of 100+ countries and credential systems, plus a validation path for anything the engine flags as low-confidence. The system is designed to admit what it does not know.
Many of our admissions rules aren’t written down anywhere. Is that a problem?
That’s the norm, not the exception. Most institutions have rules that live in individual evaluators’ heads. We work with your team to surface them and put them in one place - usually the first time your whole admissions rule set has ever been visible as a single artifact. Most teams describe the experience as relief.
Why not build this in-house? We have engineers.
Some institutions have tried. One large provider reached roughly 8,500 rules before the project was paused; another abandoned a two-year build. The rule surface is deeper than it looks - country equivalencies, program-specific overrides, and edge-credential logic multiply faster than most roadmaps anticipate. AutoEnrol is the off-ramp when your engineers’ time would be better spent on the rest of your stack.
How long does implementation take?
Published entry criteria to working rules in days, not months. Pilots run in 2–4 weeks with no production integration required.
Where does our data live and who can see it?
Regional data residency (AU, EU, UK, US). ISO 27001-certified controls. Pilots run on fully de-identified data. Our Trust Center hosts DPA and SOC-style documentation on request.
What if your rules engine disagrees with our evaluator?
Side-by-side benchmarking is a first-class feature, not an afterthought. The 150-app pilot is explicitly designed to surface every divergence so your team can decide whether to adjust the rule or override the case.
Get started

Decisions in seconds. Validated by your team.

By 2030, admissions decision infrastructure will be table stakes. The institutions that get there first win the next decade of international talent.

Book an intro call