Zeno
zenoapp.ioZeno Fitness and Habit Platform
Production mobile-app backend and demo frontend for an AI fitness, nutrition, and habit platform, with generated programmes gated on a confidence threshold.
Challenge pressure
AI-generated training and nutrition programmes reach a real body, so a low-confidence generation cannot be allowed to ship.
The situation
Progressive-overload programming and calorie targets are advice a person acts on physically. A general-purpose generator is acceptable when the output is a draft; it is not when the output is next week of training.
What we built
A NestJS backend on MikroORM over PostgreSQL on AWS RDS, with domain-driven modules per vertical covering strength, nutrition, meditation, breathwork, and fasting. Multi-provider routing spans Claude 3 Haiku, OpenAI, and Gemini. Firebase JWT handles authentication, Pro-tier gating controls access, AWS SQS carries event-driven processing, and Jest with supertest covers the suites. The demo frontend is Next.js 16 and React 19 on Tailwind 4, deployed to Vercel.
What changed
Generated programmes are held behind an 85% confidence threshold before they reach a user.
Constraints and decisions
The confidence gate is the product decision that shaped the architecture: generation had to be separable from delivery so a low-confidence result could be withheld rather than shown with a caveat. Routing across three providers exists to keep that threshold reachable at an acceptable cost per generation.
Measured outcomes
85% confidence threshold gating AI-generated programmes
Domain modules per vertical across strength, nutrition, meditation, breathwork, and fasting
Multi-provider routing across Claude 3 Haiku, OpenAI, and Gemini
Technology stack
Delivery fit
Designed for Zeno where AI Agents needed to feel like a dependable operating system, not a fragile prototype.
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