Their model is lock-in
Leading wearable brands earn from subscriptions tied to their own apps and clouds. Opening raw data to any AI undercuts that model, which makes it costly for them to follow.
Otolo / 04 Why Otolo
Otolo connects people to their devices, not apps to vendor clouds. Here is where that sits in the market and why it is hard to copy.
Two questions sort the market: does a product depend on vendor clouds, and who controls the engine that analyzes the data? Otolo is the only approach that reads devices directly and lets you choose the AI.
| Capability | Cloud aggregators | Single ecosystems | Open phone apps | Otolo |
|---|---|---|---|---|
| Reads devices directly, without the vendor cloud | No | Own devices | Yes | Supported devices |
| Data on your computer, in open files | No | Export only | On the phone | SQLite, Parquet |
| Works with the AI you choose | Through apps | Limited | No | Any MCP client |
| Multi-device studies for R&D teams | Yes | No | No | Yes, device-direct |
Leading wearable brands earn from subscriptions tied to their own apps and clouds. Opening raw data to any AI undercuts that model, which makes it costly for them to follow.
R&D teams, AI training programs, developers and researchers value openness and device choice over polish. They pay for data across many devices, which no single brand offers.
A driver library and community make more devices usable with any AI. Consumer experiences and our own open, low-cost wearable follow.
AI labs already read Apple Health and Health Connect, and anyone can wrap an API in MCP. What compounds is harder to copy.
Tested, documented drivers for each supported device, growing with every release and every contributor.
How well each device agrees with a reference, with methods and firmware versions. Proof that travels with every sale.
Local by design, employer-blind, open source. A privacy brief security and HR can sign.
Builders who add drivers and recipes, credited, with a validation path into the trusted tier.