Smoove is a fitness and nutrition platform that classifies every user through a smart questionnaire and a health-risk engine, then assigns the training and food plan that fits — for individuals, couples, families and teams. We built the mobile app and the admin panel as a market-ready MVP.
Member numbers and engagement are the client's to share, and they are not on this page. What is on this page is what was built and why it was built that way.
Smoove starts with a question: who is this for — you, a couple, a family or a team? From there a smart questionnaire works out the user's goals and health, a rule-based engine places them at low, moderate or high risk, and the platform assigns a training and nutrition plan to match. Users log their activity daily and weekly, track steps, sleep and water, follow food and sport programs, and chat with their coaches.
The brief was a market-ready MVP: the mobile app, an admin panel to manage users, plans and datasets, subscriptions tracked in QuickBooks, English and Arabic throughout — and a backend designed so an AI coach, Smov, can be added later without rebuilding it.
Most fitness apps hand every user the same program and hope the difficulty setting sorts it out. That works for the already-fit and fails the people who most need care: someone with a heart condition or a recent injury gets the same plan as everyone else, with nothing that stops them.
And people rarely train alone. Couples, families and corporate teams want to sign up together and see each other's progress — which most apps cannot model at all. A platform that gets this right has to classify safely before it prescribes, escalate high-risk users to a medical check, keep plans moving as people improve, and hold group accounts together under one subscription.
Every Smoove plan is the output of rules the client controls — a questionnaire, a risk engine and a progression model — rather than a template every user shares.
Onboarding runs as a decision tree: each answer decides the next question, so the platform builds a picture of the user's goals, habits and health in a few steps instead of one long form.
A rule-based engine scores health-condition answers as low, moderate or high risk. High-risk users upload a medical report, and their plan waits in the admin panel's pending approvals until it is reviewed.
Users sign up as an individual, a couple, a family or a team. Group entities share one package, and members join with a code — so a family or a company trains together under one subscription.
Daily and weekly activity logs and physical evaluation tests feed reassessment triggers; when a user is ready, automated progression logic upgrades their level and their plan with it.
The questionnaire's result routes users to the right package, they subscribe and pay in the app, and complete their profile after payment — with every subscription tracked in QuickBooks.
A React panel covers user management, plan assignment, pending approvals, full CRUD on the exercise and nutrition datasets, and bulk CSV import and export — so the client changes the content, not the code.
Every user gets a training and nutrition plan matched to their goals and their health, not a shared template.
High-risk users are caught by the risk engine and reviewed with a medical report before their plan goes live.
Couples, families and teams sign up together with join codes and train under one package.
Plans progress automatically as reassessments show users are ready for the next level.
The client manages users, plans and datasets — including bulk CSV imports — from the admin panel.
The backend is ready for an AI coach to be added without rebuilding the platform.
Subscription tracking synced to the client's accounts.
Package subscriptions paid in the app.
Social sign-in alongside email and phone.
Building a health or fitness app that has to personalise safely?
The Flutter app and the React admin panel share one Node.js and MongoDB backend, so a questionnaire answer, a plan and an approval are the same record everywhere.
Every tool below is in the build — no résumé-ware.
By classifying before prescribing. Smoove runs every user through a branching questionnaire and a rule-based risk engine that places them at low, moderate or high risk. High-risk users upload a medical report, and their plan waits for review in the admin panel before it goes live.
Yes. Smoove has four account types — individual, couples, family and team or corporate. Group entities share one package, and members join with a code, so people who train together sign up together.
Through reassessment. Activity logs and physical evaluation tests trigger reassessments, and automated progression logic upgrades the user's level — and their training and nutrition plan — when they are ready.
Yes. Smoove's admin panel has full CRUD on the exercise and nutrition datasets plus bulk CSV import and export, so the client changes the content without a developer.
That is how the backend was designed. Smoove's architecture leaves room for an AI chatbot coach, Smov, to plug into the same user, plan and activity data — so it can be added in a later phase without rebuilding the platform.
It depends on how much of the logic is rules versus content, and how many account types you need. A mobile app and admin panel MVP like Smoove is scoped in phases and quoted fixed-price after a free scoping call.
Smart onboarding, health-risk rules, group accounts and plans that progress on their own.
See how we'd scope yours.
