JoinBeet: A Personalized Medical Nutrition Platform

It depends on scope. The cost of a build like this is driven by scope, features, integrations, compliance requirements, team composition, and infrastructure complexity. We provide a fixed estimate after a short discovery call once those are defined.

Each medical condition maps to a set of nutritional rules, and a nightly job uses a matching algorithm to assign recipes that satisfy all of a user's active constraints while keeping variety. It respects calorie targets, allergens and the meal plan tier, so hundreds of personalized, medically appropriate plans are produced automatically each night rather than by hand.

Terra is an abstraction layer that connects many wearable brands, such as Apple Watch, Fitbit and Garmin, through one integration. In JoinBeet, raw data flows into time-series storage and is transformed for display, so activity, sleep, heart rate and body metrics appear in clear charts, and adding a new supported device needs no per-brand work.

By designing for it from the start. JoinBeet handles content such as Arabic recipe names alongside English, with layouts built to work correctly in both left-to-right and right-to-left directions. Building bilingual and RTL support in early, rather than retrofitting it, is what makes the experience feel native in each language.

It means each staff member sees only what their job needs. In JoinBeet, six roles — admin, chef, doctor, kitchen manager, delivery worker and editor — each get section-level permissions, and the panel renders navigation and controls based on who is logged in. Kitchen staff see prep views, nutritionists see clinical controls, managers see analytics and audit logs.

A full platform like JoinBeet took around seven months, given the mobile apps, the admin panel, the matching engine and the integrations. A smaller first version is faster. We work in two-week sprints with working software throughout, so a large build like this still shows steady, visible progress.

Yes. JoinBeet's assignment engine handles multiple meal plan tiers in a single pass, including snack-based plans, seven-meal plans, Ramadan plans and bariatric plans, each with its own rules. The engine is built around flexible constraints, so new plan types can be added without rebuilding the core matching logic.

By encoding the medical rules into the engine and applying them consistently. Each condition maps to strict nutritional constraints, the matching algorithm only assigns recipes that satisfy all of a user's active constraints, and nutritionists retain fine control over limits such as sodium, potassium and phosphate. Safety comes from the rules being enforced automatically rather than relied on by memory.

Yes — that was the core goal. Automated nightly matching replaces the per-user manual assignment that used to bottleneck the service, so hundreds of subscribers can be served without a dietitian configuring every user by hand. The platform removes the operational ceiling while keeping clinical control in the hands of nutritionists.