Fitscope/Solutions/Apps & Platforms/AI Coaching Apps
Apps and platform

Your Model Writes the Plan.
We Supply the Sessions.

Coaching models are good at deciding what a member should do next. They cannot film it. License a structured library your engine can select from, with the metadata it needs to make that selection well.

Fitscope Studio
The Gap

A Coaching Engine
With Nothing to prescribe

Most AI fitness teams arrive at the same wall. The model works, the personalization logic is sound, and the app has nothing to hand the member at the end of it. Filming a library is a production business, and it is rarely the business the founders set out to build.

Licensing solves it in weeks rather than quarters. What matters is not only the number of classes but whether the library is described well enough for software to choose between them without a human curating every plan. That is what Content Licensing delivers here, and it is why the metadata schema matters as much as the footage.

Stock Footage Context
What Your Engine Gets

The Engine
And the Content

metadata
01
metadata

Described for Machines to Read

Every class carries modality, duration, intensity band, equipment required, instructor, and movement focus as structured fields. Your engine filters on those directly instead of parsing a title string.

structured FieldsMachine Readable
Pairing
02
coverage

Enough Variety To personalize

Personalization fails when the catalog is thin. More than thirty modalities across a wide range of durations and intensities means the model has somewhere to go when a member progresses or plateaus.

30+ modalitiesProgression range
mind-body
03
consistency

Predictable Structure Every Class

Warmup, work, and cooldown sit in consistent positions with consistent cueing. That predictability is what lets an engine stitch classes into a coherent week rather than a random sequence.

Consistent Structureprogrammable
launch
04
launch

A Library on Day One

Launch with a catalog already deep enough for your model to demonstrate what it does. Adding your own content later does not require replacing what you licensed.

Ready at launchaditive
Two Routes

Into Your App,
or onto ours.

01

License Into Your Build

Take the library and the metadata, and keep the app you have already built. Most teams with a working product choose this.

02

Launch on Our Platform

If the app is not built yet, license the platform as well and skip the infrastructure work entirely.

03

Either Way

The content, the metadata schema, and the delivery specs are the same. The choice is about how much you want to build, not about what you receive.

Related Routes

If Your Product Sits Somewhere Else.

Platform Licensing
Platform

Skip the build

A branded iOS and Android app with equipment pairing already in place, if you would rather not build the delivery layer yourself.

Platform Licensing →
Production Services
Clinical

Lower-Intensity Programming

Teams working alongside clinicians need a different intensity profile. The rehab and telehealth page covers that content set.

Telehealth, PT and Rehab →
Common Questions

What Product Teams
Ask First.

Modality, duration, intensity band, equipment required, instructor, movement focus, and structural markers for warmup, work, and cooldown. The schema is shared before signing so your team can confirm it maps onto what your engine expects.
It depends on how aggressively your model varies a plan. A narrow, tightly personalized product can launch on a smaller set than a broad one. We would rather size it against your logic than sell you volume you will not use.
Yes. Licensed classes and your own content sit in one catalog with the same metadata structure, so your engine treats them identically. Many teams license for breadth and add signature content over time.
No. White-label delivery removes on-screen marks and platform mentions, so the classes sit inside your product without pointing anywhere else.
Delivery timing depends on the size of the selection and your ingest process. Scoping that first gives you a real date, which is more useful than a general answer here.
Next Step

Show Us How
Your Model Chooses.

Tell us how your engine builds a plan and what it needs to know about a session. We will map the library against it and show you where the coverage is strong and where it is thin.