Product strategy
Defined the connected learning loop, the feature priorities, the access model, and the role each feature plays beyond producing a single answer.
Bytecraft product · Mobile · AI · Backend
LearnSnap AI: turning answers into a complete learning loop.
LearnSnap is an Android learning product that turns camera or typed questions into clear AI explanations — then helps the learner practise, revise and come back to the topic later.
The product problem
Getting an answer is only the first step.
Most homework and study tools stop the moment they produce a response. That is the easy part to build and the least useful part to own — a learner who gets an answer and closes the app has not learned anything, and has no reason to open it again tomorrow.
LearnSnap was shaped around a wider question: how does someone move from asking, to understanding, to practising, to remembering — and back to the same topic a week later?
The loop
- 1 Ask a question, typed or scanned
- 2 Read a step-by-step explanation
- 3 Practise it as a quiz
- 4 Revise with flashcards
- 5 Return through saved topics
Each step exists to make the next one likely. That is the product, not the answer on its own.
The product
Ask, understand, practise, revise, return.
The same question carries through every screen. A learner who scans a maths problem ends up with an explanation, a quiz, flashcards and a saved topic they can find again — not five disconnected features.
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Ask A scanned question becomes a step-by-step explanation, tagged by subject. -
Practice The same topic turns into a five-question quiz with an explanation after each answer. -
Revise Flashcards track what is known and what still needs another pass. -
Return Saved topics stay searchable across subjects, so a question is not a dead end. -
See progress Coverage and scores are broken down by subject rather than shown as one number.
Scope
A connected product across mobile, AI, backend, access and launch.
There was no client to hand the difficult parts to. Every decision below was ours, including the ones that turned out to be wrong first time.
Android product development
Built the Android experience across question input, explanations, quizzes, flashcards, saved topics, daily practice, progress, account, credits and subscription flows.
Backend and APIs
Developed the services behind account access, credits and entitlements, AI request orchestration, learning workflows and reliable product state.
AI workflows
Connected AI processing to structured product experiences rather than exposing a raw prompt, with retry, error, access and usage behaviour designed around the flow.
Monetization and access
Designed a credit-based access model with subscriptions, so AI-dependent actions stay viable to run as usage grows.
Analytics and product foundation
Instrumented the product events and operational signals needed to understand usage, funnels, failures and monetization after launch.
Launch support
Prepared the product and its supporting systems for Google Play release, product iteration and continued improvement.
Decisions
The questions that shaped the product.
A case study is more useful when it shows the forks rather than the finished thing. These are the ones that changed what got built.
- Should an answer be the end of the interaction?
- No. Every explanation leads directly into practice, because the answer is the moment a learner is most willing to keep going.
- How should AI usage be paid for?
- Credits, with subscriptions on top. AI-dependent actions cost money per request, so the access model had to be part of the product design rather than added after launch.
- Should reviewing cost the same as generating?
- No. Revisiting saved work does not spend credits the way new generation does — otherwise the product would punish the exact behaviour it wants to encourage.
- Where does product state live?
- One source of truth across the app, backend, subscriptions and analytics. Account, credits and entitlements disagreeing with each other is the failure mode that erodes trust fastest.
- What happens when the AI is wrong or unavailable?
- The product handles uncertainty and failure explicitly rather than presenting every response as correct. No AI output is shown as guaranteed accurate.
Technology
What LearnSnap is built with.
An AI product is mostly the services around the model. These are the parts that make the AI usable, affordable and measurable in a shipped Android app.
- Kotlin
- Java
- Swift
- OpenAI
- Anthropic Claude
- Google Gemini
- Node.js
- NestJS
- Express
- Laravel
- Firebase
- +3 more
- Firebase Analytics
- Google Analytics 4
- PostHog
- Microsoft Clarity
- Sentry
Shipped
The complete product foundation.
- Typed questions
- Camera-based question input
- AI-assisted explanations
- Quizzes
- Flashcards
- Saved topics
- Daily practice
- Progress tracking
- Credit-based usage
- Google account and access flows
- Subscription support
- Backend APIs
- Analytics and product measurement
- Google Play launch foundation
Why this project represents how we work.
LearnSnap required decisions across product strategy, Android engineering, backend architecture, AI usage, credit access, subscriptions, analytics, launch and continued improvement. Treating those as one system rather than nine separate workstreams is the whole of our delivery model — and building our own product is the only way to show it without a client's permission.
Building a mobile or AI product that needs more than a feature demo?
Tell us the user problem, the business model and the technical risks. We will talk through the whole path — first release to product learning — rather than quoting a feature list.
Rather talk it through? Book a discovery call.