EdTech & Knowledge-Sharing Platform
From early-stage idea to a production platform used by thousands — powering AI education and enterprise knowledge-sharing
Introduction
Superintelligent began with a bold idea: to make AI education more practical, more social, and more accessible. What started as a learning platform for individuals quickly turned into a system used by companies to collect and share AI use cases across teams.
We helped define the technical direction of the platform — architecture, infrastructure, backend, search, streaming, and many user-facing features.
This case study tells the story of how we built a scalable edtech application, navigated a major product pivot, and created an architecture that supported fast iteration, a social content feed, video streaming, and multi-organization permissions.
The Challenge
Superintelligent started as a consumer learning platform combining video lessons, social content, and AI assistance. But as it grew, companies began asking to use it internally — not just to teach AI skills, but to document and share real use cases across departments. That pivot meant the product now needed private workspaces, strict access control, team-specific feeds, and multi-tenant architecture — while still feeling as fast and fluid as a consumer app.
The constraint that shaped every decision: the platform needed to scale rapidly without ballooning costs or requiring a large DevOps team. The challenge was to build an architecture robust enough for enterprises and efficient enough for a startup.
Approach
Laying the groundwork for a scalable, cost-efficient platform
We set up infrastructure that could scale from dozens to thousands of users automatically — without needing a large ops team or ballooning costs.
Building a content engine that felt fast and alive
Once the foundations were in place, the next step was to build the core of Superintelligent's learning and social experience. This meant creating a content engine capable of supporting diverse media types—video lessons, polls, articles, challenges, and student projects—while making everything feel instantaneous.
We implemented a pipeline for lesson uploads and video streaming that maintained high quality without introducing unnecessary processing delays. Reliable view tracking ensured accurate analytics for creators and admins.
On top of that, we designed a chronological feed that surfaced the right mix of updates: new lessons, user posts, project showcases, and discussions. Social interactions such as comments, replies, mentions, and likes were engineered to be low-latency, giving the feed the "alive" feeling of a modern social app rather than a traditional LMS.
Admins received a suite of tools for managing courses, designing structured learning paths, organizing content, and tracking user progress—all built with attention to clarity and speed so teams could publish updates as quickly as they produced them.
The UI focused heavily on smooth interactions: hover-to-preview videos, near-instant page transitions, and a layout optimized for both browsing and deep learning. Every detail reinforced the idea that learning shouldn't feel like navigating a corporate portal—it should feel engaging and intuitive.
Search and discovery powered by relevance
As the content library grew, we needed a fast and intelligent way for users to find what mattered to them. We integrated Algolia to deliver sub-100ms searches across lessons, challenges, articles, and user posts. Instead of relying on generic text matching, we built custom ranking strategies that considered content type, user behavior, and learning progress.
This allowed us to provide meaningful recommendations, including contextually relevant "next lessons," without implementing a full recommendation engine. The result was a discovery experience that felt personal and adaptive, even as the platform evolved.
Evolving into a secure, multi-tenant enterprise platform
When companies started adopting the platform internally, we rebuilt the system so each organization got its own private workspace with isolated data, team-based access control, and structured content sharing.
Results
Within three months of starting development, the platform was live in production. It grew to thousands of active users, with tens of companies onboarded and thousands of seats across organizations. Teams used it as their internal hub to share AI use cases, document experiments, and roll out AI knowledge across departments.
The architecture proved itself under real-world load. ECS/Fargate, RDS, Redis, and Bunny.net handled traffic spikes — company-wide onboardings, heavy content launches — without downtime or manual intervention. Video lessons streamed smoothly, feeds stayed responsive, and new features shipped continuously while users were active.
The multi-tenant and permissions model validated the original design. Organizations could isolate their data, structure content by teams and folders, and surface the right knowledge to the right people. For admins, it felt powerful and controllable; for end users, it felt like a fast, consumer-grade app rather than a typical enterprise tool.
Tech Stack
Want to build something similar?
Let's discuss how we can help bring your edtech platform to life.