From weeks to days: How AI can transform university admissions

By Ben Bowers‍, Technical Lead at Rmkble

Abstract

Across my client work at Rmkble, manual verification bottlenecks are a frustration I’ve seen play out in almost every industry. Tackling this problem recently in higher education inspired us to build a serverless AI workflow engine capable of automating complex, multi-step checks. It confirmed a pattern I've long suspected: with the right multi-model strategy, you can turn one of the slowest operational hurdles into a major competitive advantage.

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The silent countdown

Every peak admissions cycle, a silent countdown begins. For Australian universities triaging thousands of international applications, a slow response means losing top-tier students to faster global competitors.

When applications flood in, admissions officers can spend hours manually extracting data from multi-lingual, poorly scanned documents. They must verify complex identity data, academic histories, and English proficiency against country-specific frameworks.

Incomplete or non-viable submissions waste significant time, with the result that highly qualified candidates face unnecessary delays. In a hyper-competitive international market, extended processing timeframes ruin the student experience at the exact moment first impressions matter most, leading directly to lost enrolments.

Where university leaders risk going wrong

There’s a misconception that AI will provide the solution by fully automating the admissions decision from end-to-end. This all-or-nothing approach introduces apprehension around compliance and non-deterministic results (where the system may reach different conclusions on identical inputs), leading to decision paralysis for university leaders.

Protecting the role of human judgement

AI’s immediate role isn’t to replace human judgement, but to build intelligent assistant systems that reduce administrative burden. By leveraging deterministic workflow orchestration instead of unpredictable autonomous agents, institutions can create high-precision triage systems.

These frameworks don't make the final call; they validate data and categorise applications into Recommend Accept, Recommend Reject, or Human Review Required, keeping staff firmly in control.

AI architectures need to be grounded in technical pragmatism, and this is how my team and I approach these challenges with our clients at Rmkble. Our insights come from designing frameworks that bridge the gap between complex AI capabilities and enterprise-grade predictability. By using "Augmented AI" building blocks, we can deliver massive operational leaps without requiring multi-million-dollar overhauls of core systems.

Caveats for higher education

Higher education's complexity lies in the sheer volume and variability of submission quality. Poorly scanned documents and irregular language structure can heighten the risk of AI models hallucinating.

True accuracy requires layering specialised tools to flag poor document quality early. In addition, business logic must remain transparent, letting university staff easily update rules via tools they already understand, like centralised spreadsheets.

Case study: AI in higher education admissions

I recently worked with a higher education client facing this exact challenge. To help them, we built an AI solution designed to optimise complex checks on international student applications.

Challenge

Like many institutions across Australia, our client was facing a high volume of multi-lingual applications. Evaluating scanned transcripts and identifying missing criteria was creating major manual bottlenecks. The admissions team was stuck in a loop of document extraction rather than engaging with qualified students.

Solution

Working closely with the admissions team, we engineered an AI solution that would reduce administrative burden without replacing human judgement.

This intelligent, serverless workflow engine was designed to automate complex, multi-step validation processes. The solution orchestrates a diverse mix of advanced generative AI models to dynamically route tasks through specialised validation paths, specifically targeting academic credentials, language proficiency, and identity matching.

To ensure enterprise-grade reliability, we built an automated testing framework that continuously calibrates model outputs against a rigorous, verified golden dataset.

Outcomes

Within five days, we had developed a proof of concept that demonstrated:

• 95–100% accuracy for validating English competency admission criteria.
• 90–100% accuracy – within minutes – for assessing complex applications against academic requirements.
• Predictable expenditure and clear return on investment through active management of AI operating costs.
• Application processing time reduced from weeks to days.

This early testing quickly demonstrated that AI could address the core challenge at relatively low cost, building institutional confidence to expand the initiative.

Can AI solutions work for all institutions?

From successful use cases like the above, there are three critical lessons for leaders in higher education:

1. AI must be built with the admissions team to map real lived workflows.
2. A strict "golden dataset" is vital to continuously benchmark accuracy.
3. Delivering early value through incremental check integrations builds rapid organisational confidence.

Imagine an institution where the application-to-enrolment lifecycle drops from weeks to minutes. By delegating multi-lingual document triage to secure, pay-per-use AI models, institutions can free up admissions staff for higher value tasks and to focus on edge cases, to ensure an even better level of service to prospective students.

Where to start

This strategic shift directly impacts university CIOs driving practical innovation, admissions directors aiming to hit aggressive enrolment targets, and higher ed executives focused on maximising student experience.

If you are in one of these roles, ask yourself four questions:

1. Are your admissions experts wasting valuable peak-season hours manually checking basic scanned transcripts instead of engaging with qualified students?
2. Could your institution avoid losing students to competitors by simply reducing response time?
3. Are you missing a clear strategy to establish "golden datasets” for validating AI precision before deployment?
4. Can you deploy a low-risk, serverless pilot to optimise a single, high-friction check rather than attempting an all-or-nothing system replacement?

If you answered “yes” to any of these questions, it’s time to take steps towards building the solution.

To find out how my team can leverage your existing Microsoft ecosystem to deliver similar results, feel free to reach out to me directly on LinkedIn or chat with the Rmkble team.