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Inseed

AI for fitness and wellness apps

Fitness and wellness apps, made smarter without a rewrite.

We add the AI features that keep people showing up, adaptive plan generation, on-device coaching, natural-language logging, and wellbeing reflection, to the app you already ship.

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You have users. Keeping them coming back is the whole game.

Your app works. People download it, start a plan, log a few workouts. The problem every fitness and wellness product fights is the drop-off: the plan that does not adapt when life gets in the way, the logging that feels like data entry, the coaching that is the same for everyone. AI is genuinely good at the parts that drive retention here, a plan that adjusts to the person, feedback that feels personal, and logging that takes a sentence instead of ten taps.

The constraint in fitness and wellness is not the model, it is the daily habit your app is trying to build. A rebuild resets the habit and the history that make the product stick. So we do not rebuild. We add AI as a layer beside your plan, tracking, and reflection flows, and we keep as much on-device as the feature allows because health data is personal and latency kills a coaching moment. Your existing plans, history, and users stay exactly where they are.

What we build

Four AI features that earn their place in a fitness or wellness app.

These are the patterns we see move retention in health products. Each one sits beside your existing plan and tracking flow, and leans on-device where it can to keep personal data private and responses fast.

Adaptive plan generation

Plans that adjust to the person instead of handing everyone the same template: harder when they are progressing, lighter when they miss a week, reshaped around the equipment and time they actually have.

What it changes
The plan stops feeling generic and starts feeling like it is paying attention, which is most of why people stay past week three.
How it is built
A plan-building engine, like the customizable plan builder we shipped for Kinobody, with an LLM layer that adapts the plan from a person’s logged progress and constraints.

On-device coaching and feedback

Personalized cues and feedback that respond in the moment, kept on the device so a person’s health data does not have to leave the phone for the feature to work.

What it changes
Coaching feels immediate and private, and the product can offer real-time feedback without shipping sensitive data to a server on every rep.
How it is built
Smaller on-device models for the low-latency, privacy-sensitive path, with a server call reserved for the heavier reasoning that genuinely needs it.

Natural-language logging

Logging a workout, a meal, or a mood by saying or typing it plainly, "45 minutes of tennis and a chicken bowl", instead of hunting through menus and number fields.

What it changes
The friction that kills daily logging drops to a sentence, so people actually keep a streak, and a streak is what keeps them in the app.
How it is built
An LLM pass that parses the free-text entry into your structured fields (exercise, sets, calories, mood), with a quick review before it is saved.

Wellbeing reflection

A reflection and mood layer that turns how someone is feeling into a trend they can see, and a companion they can talk through a hard day with, alongside the physical tracking.

What it changes
The app covers the mental side of wellness, not just reps and macros, which widens why someone opens it and how long they stay.
How it is built
The same reflection and voice-companion patterns we built in Afterlight and Epiphra, with raw entries kept private and only the derived signal surfaced.

Proof

What we have actually shipped in fitness and wellness.

We name only work we can stand behind, and we are precise about who delivered it.

Kinobody

Built by our founder and team

An influencer-backed fitness platform for Greg O’Gallagher to sell courses and deliver customized plans across iOS, Android, and web, with an admin portal and a per-user workout tracker. Our founder built the mobile apps, the auth and security, and the admin portal with its customizable plan builder.

  • 100k+ downloads and around 4.8 stars on the App Store
  • Serves 10k+ monthly users behind a creator with a 1M+ following
  • Per-user workout tracker tied to each person’s plan, with reps, sets, and progress
How we build cross-platform

Epiphra

Delivered by Inseed

A live AI wellness companion where people reflect on what is going on by text or real-time voice and get brief, non-directive support, with the app remembering past conversations. Our founder led the architecture and technical direction, with the team building the production app.

  • Real-time native-audio voice via the Gemini Live API
  • Long-term memory using text embeddings and a pgvector index
  • Live on the App Store and Play Store
How we keep AI on-device

Afterlight

Built by our founder

An AI self-reflection app that turns each journal entry into a visual reflection, emotional vectors, and a summary, so a person can track how they feel over time and share it without exposing the raw incident. Our founder built the entire agentic AI pipeline.

  • Multi-agent pipeline on Supabase functions
  • Layered graph of emotions over time
  • Raw entries kept end-to-end encrypted, never stored
Read an AI feature case study

Straight answer on the gap: our strongest fitness credential is Kinobody, a live, well-rated, high-download platform with a real plan builder and per-user tracker, and our AI depth (adaptive reasoning, on-device and voice, reflection) is proven in adjacent wellness products like Epiphra and Afterlight rather than inside a single fitness app. We have not yet shipped on-device form-analysis in production. The audit is where we prove the specific feature on your app, in a throwaway prototype, before you commit to a build.

Borrowed proof, labeled honestly

React Native holds up in health and fitness.

React Native runs in production in health tracking at Gyroscope, and at consumer scale at Discord and Coinbase. These are not our clients. They are proof that the framework we build on carries a daily-use, data-heavy product, so the question for your app is the feature, not the foundation.

How it starts

It starts with an audit, not a rebuild.

We do not open with a six-figure proposal. We open with one week of work that tells you exactly where AI fits in your app, what it costs to run, and whether it is worth building at all.

Step 1

Mobile App Audit

$5K to $8K

1 week

A written assessment of where AI fits in your app, a working proof-of-concept of the top feature, and a cost model you can take to your board. The fee credits toward a Sprint signed within 30 days, or is refunded if the report is not useful.

Step 2

AI Integration Sprint

$15K to $30K

2 to 4 weeks

One AI feature shipped into your existing app, as a thin integration layer with fallbacks, caching, and guardrails. Your architecture stays yours.

Step 3

AI-Native Mobile MVP

$25K to $75K

6 to 12 weeks

A production-ready AI-native mobile MVP when the answer is a new product rather than a feature. Fixed price, fixed milestones, both stores.

See full pricing

FAQ

Fitness and wellness questions, answered straight.

Can a plan actually adapt, or is it a fancy template?

It can genuinely adapt. We start from a real plan-building engine, the kind of customizable plan builder we shipped for Kinobody, and add an LLM layer that reshapes the plan from a person’s logged progress and constraints. The adaptation is grounded in a structured plan model, not a model inventing a workout from nothing, which keeps it safe and coherent.

Why on-device? Is that not slower?

It is usually the opposite. Health data is personal, and shipping every rep or heart-rate reading to a server adds latency and a privacy surface you do not want. Smaller on-device models handle the real-time, sensitive path fast and privately, and we reserve a server call for the heavier reasoning that truly needs it. The user gets an immediate response and their data mostly stays on the phone.

Will natural-language logging get the numbers right?

It parses into your structured fields and shows the result for a quick confirm before saving, so a person can fix a misread in one tap. The goal is to cut logging from ten taps to a sentence while keeping your data clean. You keep the schema; the model just fills it from plain language.

Can you add a mental-wellness side to a physical fitness app?

Yes, and it is a natural extension. We have built the reflection and voice-companion patterns in Afterlight and Epiphra, where a person reflects by text or voice and the app tracks how they feel over time while keeping the raw entries private. Bolted onto a fitness app, that covers the mental side of wellness alongside the physical.

Do creators or coaches keep control of the plans?

Yes. We build the admin and plan-builder tooling so your coaches or operators create and manage plans themselves, which is exactly what the Kinobody admin portal does. AI adapts within the structure they set; it does not take the plan out of their hands.

Is React Native robust enough for a daily-use fitness app?

Yes, and we can point at live evidence. Kinobody runs across iOS, Android, and web for 10k+ monthly users with a per-user workout tracker. Epiphra runs real-time voice and semantic memory on it. Beyond our work, Gyroscope runs health tracking on React Native, and Discord and Coinbase run it at consumer scale.

Ready to add AI to your fitness or wellness app?

30 minutes, free. We will look at your app and tell you honestly where AI fits, what it costs, and what we would build first. Shuhel will be on the call.

Book a 30-minute call