What MyGymAI does
Most training apps hand you a fixed template and expect you to follow it for twelve weeks. MyGymAI works the other way around: it generates a personalized workout program from your goals, experience level and available equipment, then rewrites that program as your actual logged performance comes in.
You tell the app what you are training for — strength, hypertrophy, general conditioning — and how many days a week you can realistically commit. It builds the split, picks the exercises, and sets the starting loads. From there, every session you log becomes input: hit your targets and the progression accelerates, miss them and the app backs off before you stall.
Key features
- Personalized workout generation — programs built around your goal, experience and equipment, not a generic template.
- Adaptive progression — loads and volume adjust automatically based on your logged sets.
- Progress tracking — every set, rep and weight stored, with trends over time.
- Exercise substitution — swap a movement when the rack is busy and the program adapts around it.
- Native iOS — built in Swift and SwiftUI for a fast, offline-friendly gym experience.
How we built it
MyGymAI is a native iOS application written in Swift and SwiftUI, with an LLM-backed program generator behind it. The interesting engineering problem was not the model call — it was constraining it. A language model asked for a training plan will happily invent exercises, prescribe unsafe jumps in load, or forget what it prescribed last week.
We solved that with a structured program schema the model has to fill, a validation layer that rejects unsafe progressions before they reach the user, and a persistent training history that gets fed back on every generation. The result is an assistant that behaves like a coach with a notebook rather than a chatbot with amnesia.
This is the same pattern we apply to client work: an LLM inside a hard schema, with domain rules enforced in code rather than trusted to the prompt.