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CASE STUDIES
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2024
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Unacademy
Unacademy: EdTech Internal Automation & Analytics
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CLIENT
Unacademy
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TIMELINE
18 months
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SERVICES
Learner Retention Automation
Educator Support Automation
Content Intelligence & Discoverability
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OVERVIEW
When Scale Becomes the Problem
Unacademy is one of India's largest education platforms, thousands of educators, millions of learners, and a content library expanding faster than any manual system could structure or surface. At that scale, the constraints are not growth constraints. They are operational ones. The cost of a learner dropping off exceeds the cost of acquiring them. Support teams absorb increasing volumes of repetitive queries that have nothing to do with learning outcomes. Content becomes harder to find as the library grows, which is the opposite of what should happen. WeLaunch was brought in not to help Unacademy grow faster, but to make sure its existing scale was not eroding from the inside.
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CHALLENGES
Growth that outpaces its own infrastructure doesn't compound. It corrodes.
The platform was not constrained by demand. It was constrained by its ability to operate at the scale demand had already created. Every inefficiency that existed at one million learners was more expensive at ten million. Manual processes that worked in the early years were now structural bottlenecks. The longer they remained, the more they cost.
How do you build operational infrastructure for a platform already at scale, where the systems need to improve as usage increases, not just keep pace with it?
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SOLUTIONS
We built systems that get smarter as the platform grows.
The design principle across every system WeLaunch deployed was the same: build for compounding, not for coverage. A system that handles today's volume is a band-aid. A system that improves as usage increases is infrastructure.
Deployed a learner retention system that analysed engagement patterns in real time and triggered personalised interventions when users showed early signals of drop-off, shifting retention from reactive to proactive
Automated educator support workflows covering scheduling, content management, and operational queries that were previously consuming human support bandwidth at scale
Built a content intelligence system that structured, tagged, and surfaced content based on live learner behaviour, so discoverability improved as the library grew, instead of degrading
Instrumented an analytics layer giving the internal team clear visibility into engagement health, drop-off risk, and content performance across millions of active learners
Designed every system to feed back into itself, more learner interactions sharpened the retention model, more educator usage made support automation more efficient
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RESULTS
Operational leverage: scale increased efficiency instead of eroding it.
The systems performed as designed. As the learner base grew, the retention system became more precise, identifying drop-off risk earlier and intervening more effectively. As educator usage increased, support automation absorbed more of the load without additional headcount. As the content library expanded, discovery improved rather than degrading. The platform did not just maintain its operational standard at scale, it raised it. That is what infrastructure built for compounding looks like in practice.
Proactive
Retention system deployed
18 months
Systems compounding
Zero
Headcount added
I've launched companies before. I've never had a co-builder who moved at this pace. WeLaunch had our product in front of students faster than I thought was possible. That timeline doesn't exist anywhere else I've worked.
Doug
CosmicPrep
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CASE STUDIES
