Scaling and Modernizing an E-Learning Platform

About the Client

A UK-based education technology company developing a large-scale digital learning platform used by higher education institutions.

The platform’s reach extends beyond the UK through partnerships, including North America and multiple European markets. 

The company operates in the university sector, where digital access to academic materials, engagement tracking, and institutional reporting are becoming central to teaching and operational strategy. The platform supports universities in delivering structured digital content to students while enabling administrative and analytical oversight at scale.

Objective

As the platform expanded across institutions and product areas, including growth beyond the UK through partnerships in North America and multiple European markets, the client faced increasing architectural complexity and delivery pressure. They required a scalable, cross-functional engineering extension capable of accelerating development, modernising legacy components, and supporting new data-driven and AI-powered initiatives without disrupting ongoing operations.

Solution

Altabel’s team worked across several core areas of the platform. The scope of work covered operational workflows, analytics, AI-related functionality, data processing, and overall platform reliability, across both user-facing features and backend services.

Operational portals for publishers and library staff

The team developed frontend and backend functionality for managing large volumes of entities. This included bulk actions (edit, delete, copy, move, and licensing-related operations), as well as improved search and filtering.

As a result, repetitive manual work was reduced, day-to-day operations became faster and UX and performance improved for users handling large catalogues of academic materials.

Institutional analytics and dashboards

The team built a dedicated analytics module for administrators and faculty leads. It included cohort-based filtering, drill-down analytics, multiple dashboard views, and sharing/export tools for reporting and collaboration.

On the data side, the work supported consistent reporting and responsive dashboard performance.

AI-powered study capabilities

Developers prepared existing user content and learning materials for AI usage. This included transforming and backfilling student notes into formats suitable for AI processing, as well as implementing mechanisms to prepare books for retrieval and assistance scenarios.

Data synchronisation and platform reliability

The team synchronised data across databases and microservices, extended content-related workflows, and implemented client-driven changes, improvements, and bug fixes in a live environment. This also included incorporating distributor-related information into workflows where it affected pricing logic.

Python-based transformation and migration engines

Python specialists worked on data transformation and migration engines, refactored content extraction logic, and supported AI-related endpoints. This work improved reliability, scalability, and cost efficiency.

QA and product design

Across all streams, manual and automated QA supported release stability, while product design ensured usability, consistency, and accessibility as new features were added and existing workflows evolved.

Result

Since February 2025, the engagement has helped the client increase development velocity across multiple product areas while improving the stability and maintainability of core platform components. By enabling several workstreams to run in parallel, covering analytics, AI-related initiatives and legacy modernisation-the team reduced internal delivery bottlenecks and supported consistent progress without disrupting ongoing operations. The collaboration remains ongoing, with continued contribution to the platform’s development and long-term scalability.