Automated Admissions Lead Management for an EdTech Platform

AI predictive maintenance in manufacturing
Industry

Industry

Education Technology

Head Quarter

Head Quarter

UK

Service Provider

Service Provider

Divyal Technologies Pvt Ltd.

Platform

Platform

Admissions Lead Management & Engagement Platform

ClientOverview

The client is an AI-powered SaaS platform designed to assist universities and higher education institutions in managing student recruitment and admissions processes. The platform focuses on improving how inquiries are captured, qualified, and nurtured from initial interest through to enrollment.

Project Challenges

The Challenge

As an education technology platform supporting universities and recruitment partners, the client needed to reliably manage and orchestrate high volumes of admissions inquiries across multiple institutions.However, the platform’s existing capabilities were not designed to support enterprise-scale lead operations.

This made it difficult for rail operators to respond quickly during disruption events, maintain consistent decision-making, and scale operations without significantly increasing cost and operational risk.

Key challenge:

  • Inability to consolidate inquiries from multiple sources into a single, standardized lead pipeline.
  • Heavy reliance on manual processes for lead qualification and follow-ups, limiting the platform’s ability to scale.
  • Limited support for configurable, institution-specific engagement workflows
  • Insufficient analytics to provide institutions with clear visibility into admissions funnel performance.
  • Operational complexity in enabling consistent collaboration between institutions and recruitment agents.As a result, the platform faced constraints in supporting institutional growth, demonstrating measurable outcomes, and scaling recruitment operations in a competitive education market.

Impact

On a day-to-day basis, the platform struggled to keep up with the volume of incoming inquiries without significant manual effort. Response times slipped, teams had to intervene more often than planned, and it became harder to maintain a consistent engagement experience across institutions.When recruitment activity peaked, these pressures increased. Lead queues built up, follow-ups became inconsistent, and the platform lacked a clear, real-time view of how leads were moving through the pipeline. This made it difficult for the client to scale operations confidently and clearly demonstrate value to its institutional customers during critical enrollment periods

TheSolution

To support the next phase of its growth, the client needed a platform that could handle admissions lead management at scale without adding operational complexity. Divyal Technologies was engaged to take ownership of this challenge and design a solution that would strengthen the client’s core product offering.Divyal led the platform architecture, automation strategy, and delivery, enabling the client to move away from manual, fragmented processes and towards a more reliable, scalable admissions workflow. The engagement also includes ongoing operational support, allowing the platform to evolve alongside the client’s business and customer needs.

Our Software Development Approach

We designed and built a modular, cloud-based admissions lead management platform that sits at the core of the client’s product. The focus was on making the platform easier to scale, simpler to operate, and flexible enough to support different institutional requirements.

Key Components:

  • Lead Capture & Ingestion:

    Divyal implemented a centralized intake layer that brings together inquiries from websites, recruitment agents, and third-party sources into a single, consistent lead pipeline for the client’s platform.

  • Lead Scoring & Classification:

    Automated AI- driven scoring and classification logic, designed by Divyal, helps the client’s platform prioritise high-intent leads while reducing manual intervention

  • Engagement Automation:

    Divyal enabled configurable communication workflows, allowing the client to offer timely, personalized follow-ups through email and SMS as part of its core product.

  • Admissions & Agent Portal:

    Role-based dashboards were built to support both institutional users and recruitment partners, giving the client a scalable way to manage collaboration within its platform.

  • Analytics & Reporting:

    Real-time reporting and dashboards provide the client and its customers with clear visibility into lead volumes, response times, and conversion performance.The platform is deployed on AWS using a secure, highly available architecture designed and operated by Divyal, ensuring reliability, performance, and ongoing monitoring as the client scales

AI predictive maintenance in manufacturing

Implementation

The platform was rolled out incrementally to avoid disruption to live events. Divyal worked closely with planning, finance, and operations teams to align workflows, validate rules, and refine the system based on real operational usage.

This phased approach allowed the client to validate changes in real usage, adjust automation rules over time, and scale adoption across multiple customers with confidence.

The Result

The platform moved from being a supporting tool to becoming a core operational system for the client’s admissions offering.

Key Components:

  • Automated Lead Qualification & Follow-Ups:

    Lead qualification and follow-ups shifted from manual handling to automated, rule-driven workflows.

  • Predictable Response Times:

    Response times became more predictable and consistent, even during peak recruitment periods.

  • Reduced Operational Overhead:

    Operational teams spent less time managing exceptions and more time improving workflows and outcomes.

  • Improved Accuracy:

    Eliminated human error in compensation calculations, ensuring passengers received the exact amount owed according to industry regulations.

  • Scalable Multi-Tenant Admissions Platform:

    The client gained a scalable, multi-tenant admissions platform capable of supporting multiple institutions and recruitment partners from a single system.

AI predictive maintenance in manufacturing

Business Outcome & Next Steps

This case study demonstrates how Divyal Technologies helped rail operators transform Delay Repay from a fragmented, manual process into a scalable, resilient operational platform. By combining automation, clear workflows, and ongoing operational support, operators were able to reduce cost, improve reliability, and maintain regulatory confidence, even during peak disruption periods.

If your organisation is managing high-volume, regulation-driven operational processes and struggling to scale during peak demand, Divyal Technologies can help.

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