Wearable device manufacturers and health tech startups hit the same wall: the hardware works, but the companion app, data sync, and analytics layer aren't ready to ship. Connecting a device over BLE, aggregating health data from Apple HealthKit and Google Health Connect, delivering OTA firmware updates without bricking devices in the field, and surfacing meaningful insights in a dashboard rather than raw sensor readings — each of those is a distinct engineering problem. We build that software layer, from the companion app to the backend, so your hardware can do what it was designed to do.
Companion iOS and Android apps with BLE device pairing, real-time sync, and HealthKit / Health Connect integration
OTA firmware update systems that push updates to thousands of devices without user intervention or bricked hardware
Health analytics dashboards that convert raw sensor data into scores, trends, and alerts clinicians or consumers can act on
Multi-device data aggregation pipelines that normalize readings across device models, firmware versions, and sensor types
Recognition
Your device tracks heart rate, sleep, and activity perfectly, but users can't see or act on their data because the companion app isn't ready?
Your firmware update process requires users to cable-connect to a laptop, so most of your installed base is running outdated software?
Your health analytics dashboard shows raw sensor values, not the risk scores, trend lines, or anomaly alerts your customers actually need?
The short answer
RaftLabs builds custom wearables app development software for device manufacturers, health monitoring startups, and enterprise wearables vendors. We ship companion mobile apps, BLE device sync, health analytics dashboards, OTA firmware update systems, and multi-device data aggregation platforms for smartwatch, fitness tracker, and medical wearable products. Most projects deliver in 12 to 20 weeks at a fixed cost.
Wearables app development covers the software layer that makes a body-worn device useful: the companion mobile app, the BLE synchronization layer, the backend data pipeline, the OTA firmware delivery system, and the health analytics or operations dashboard that surfaces device data in a form users can act on. Without this software layer, a well-engineered wearable is a sensor with no output.
01 Diagnosis
Your hardware ships on time but the companion app isn't ready
The most common wearables launch failure is not the hardware. Device teams hit their certification milestones, but the companion app, the BLE pairing flow, the data sync, the onboarding — lags by weeks or months. Users receive a device they cannot configure. Support volume spikes. Reviews go negative before the product has had a fair run.The companion app is not an afterthought. It is the primary interface between the device and the user. When it is scoped after hardware certification rather than in parallel, the launch date slips or the product ships with a software experience that undercuts the hardware quality.
Your installed base runs outdated firmware because updates require a cable
Firmware that can only be updated by plugging into a laptop has an adoption ceiling. The users who update are the technically confident early adopters. Everyone else keeps running the version the device shipped with, including the bugs, the security gaps, and the missing features you released six months later.According to SoftServe (2024), deploying OTA updates across hundreds of thousands of devices raises the stakes dramatically: a failed update can brick devices and trigger irreversible support costs. OTA firmware delivery, with staged rollouts, progress tracking, and rollback triggers, is the engineering work that keeps your installed base current without requiring anything from the user.
Your dashboard shows raw sensor readings, not answers
A dashboard that displays heart rate in beats per minute, SpO2 as a percentage, and step counts as a daily total is not an analytics product. It is a data relay. Users and clinicians can see the numbers but cannot tell whether the numbers mean anything. Alert systems that fire on every reading outside a population average create fatigue fast. Clinicians stop acting on them. Consumers ignore them.The analytics layer — individual baseline scoring, trend detection, anomaly classification, and risk stratification — converts raw sensor data into the signals that change behaviour or inform clinical decisions. Building that layer requires sensor data engineering, not just a charting library.
Multi-device data arrives in different formats with no unified view
Wearable platforms that support more than one device model, or aggregate data from Apple Watch, Garmin, Fitbit, and Oura alongside a custom device, face a normalization problem. Each device reports sleep stages differently. Heart rate sampling intervals vary. Activity classifications don't map cleanly across manufacturer SDKs. Without a normalization layer, the analytics pipeline runs on inconsistent inputs and the user sees different numbers depending on which device they wore.A multi-device data aggregation pipeline that normalizes readings to a consistent schema before they reach the analytics layer is the foundation every multi-device wearable platform needs. Apple HealthKit and Google Health Connect provide part of this for consumer devices, but custom hardware and enterprise deployments require the normalization work on your own infrastructure.
02 What we ship
We build native iOS (Swift) and Android (Kotlin) companion apps and cross-platform React Native apps for wearable devices. BLE pairing with custom GATT profiles, real-time data streaming, background sync, notification delivery, and connection state management are all handled. User onboarding, device configuration, settings management, and account linking are part of the same scope.
The companion app is where users spend their time. We design it around the user's mental model, not the device's data structure, so engagement stays high past the first week.
Built for wearable device manufacturers who need a companion app that matches the quality of their hardware, health tech startups launching their first connected product, and enterprise wearables vendors adding a software layer to existing hardware.
We design and build OTA firmware delivery pipelines before development starts, because the bootloader integration and update protocol define architectural constraints that cannot be retrofitted. The system covers secure update package signing, staged rollouts by device cohort or firmware version, progress tracking visible in the admin dashboard, rollback triggers that restore the previous firmware when an update fails, and adoption reporting across the fleet.
The update delivery path runs over BLE for short-range updates and over Wi-Fi or cellular for connected devices, with resume logic that handles interrupted transfers without starting over.
Built for device manufacturers who need to push firmware updates to thousands of deployed units without a support call, and health device companies whose FDA-registered software requires a controlled update path.
Raw sensor readings are inputs. The analytics dashboard converts them into outputs users and clinicians can act on. We build scoring algorithms against individual baselines, not just population averages. HRV trend analysis, sleep quality scoring, activity pattern recognition, and anomaly detection that fires when the individual reading deviates from their personal trend, not every reading outside a population norm.
For clinical deployments, we build clinician-facing dashboards with patient roster views, alert triage queues, and outcome tracking. For consumer products, we build personal dashboards with progress charts, goal tracking, and push notification logic tied to meaningful threshold events.
Built for remote patient monitoring platforms, fitness and wellness companies, and enterprise health programs that need to surface the signal in their sensor data.
The backend infrastructure that receives, normalizes, stores, and serves wearable data is where scale problems appear first. A synchronization backend that works fine for 500 devices creates bottlenecks at 50,000. We design the data ingestion pipeline, the time-series storage schema, the normalization layer for multi-device data, and the API surface the companion app and dashboard consume.
We use AWS IoT Core, Google Cloud IoT, or Azure IoT Hub depending on your existing infrastructure, and build the device registry, telemetry ingestion, and rule engine that routes data from device to analytics. The backend handles intermittent connectivity gracefully: offline buffering on the device, conflict resolution on sync, and deduplication at ingestion.
Built for health platforms handling continuous sensor streams, enterprise wearables vendors managing large device fleets, and fitness companies whose current sync architecture cannot keep up with user growth.
Platforms that aggregate data from Apple Watch via HealthKit, Android wearables via Health Connect, Garmin via Garmin Connect IQ, Fitbit via the Fitbit Web API, and custom hardware over BLE all face the same normalization problem: each source uses different units, sampling rates, and activity classifications. We build the aggregation layer that maps all inputs to a consistent internal schema before the data reaches your analytics or clinical systems.
For platforms with EHR interoperability requirements, we map aggregated wearable data to HL7 FHIR resources so readings flow into Epic, Cerner, or any FHIR-compliant system without a manual export step.
Built for corporate wellness platforms, clinical research tools, and consumer health apps that need to accept data from whatever device the user already owns.
Enterprise wearables, smart glasses for field technicians, connected safety vests, worker location systems, and fatigue monitoring devices, require software that connects the device to the operations workflow rather than a consumer health app. We build the operations dashboard that shows supervisor or safety teams a live view of worker status, the alert logic that fires when a worker enters a hazard zone or shows fatigue indicators, and the device management console for provisioning and updating a fleet of enterprise devices.
Integration with existing enterprise systems, SAP, Oracle, ServiceNow, or custom operations platforms, is part of the scope. Data flows from the device into the workflow where decisions get made.
Built for safety technology companies, industrial IoT vendors, and enterprise operations teams deploying wearable devices at scale across field or manufacturing environments.
03 How we work
Companies we've built for
04 Track record
06 Client voices
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
USARaftLabs nailed what other agencies couldn't — they started with our business problem and worked backwards to the right product. We were live in 14 weeks.
07 Why us
The industry changes. The broken process usually looks the same. Across 14+ industries and 100+ products, we recognise your problem fast, and we frame the fix around your margin and your operations.
We measure success the way you do: hours saved, revenue earned, margin recovered. We stay through launch and growth, so the result is ours to own.
Vodafone, T-Mobile, Cisco, Energia, Aldi, Nike. Six years, 100+ products in production, 4.9 on Clutch. Serious businesses keep coming back because we stay accountable long after launch.
08 Questions
Yes. We build iOS and Android companion apps that connect to custom BLE hardware using the device's GATT profile. We handle service and characteristic discovery, real-time data streaming, connection state management, background sync, and reconnection logic. We work from your hardware specification and BLE protocol documentation; you don't need to change the firmware to fit a SDK.
OTA firmware update architecture is designed before the first line of code is written, because retrofitting it later is expensive and often impractical. We build the update delivery pipeline, the device-side bootloader integration, staged rollouts that limit blast radius, progress tracking, rollback triggers for failed updates, and a dashboard showing update adoption across your fleet. A failed update that bricks a device in the field is an irreversible support cost — the architecture prevents it.
Yes. HealthKit integration on iOS and Health Connect on Android are standard integrations we build. Google Fit APIs were sunset in mid-2025, so new Android wearable integrations now go through Health Connect. For clinical or enterprise deployments that require EHR interoperability, we map wearable data to HL7 FHIR resources so readings flow into Epic, Cerner, or any FHIR-compliant system. The integration approach depends on what your device tracks and what the destination system receives.
A companion app with BLE sync, user onboarding, and a basic data dashboard takes 12 to 16 weeks. A full platform with OTA firmware updates, multi-device aggregation, health analytics, and a clinical or operations dashboard takes 16 to 28 weeks. Costs start around $30,000 for a focused companion app and run to $150,000 or more for a full-stack wearable platform with backend data pipelines and dashboards. Fixed cost is agreed before development starts.
Yes. Health data from wearables is sensitive whether or not it is formally classified as protected health information. We design data handling to match the regulatory context: HIPAA technical safeguards for any platform touching PHI in the US, GDPR data minimisation and consent flows for European markets, and Apple's and Google's health data guidelines for apps distributed through their stores. Encryption at rest and in transit, role-based access controls, and audit logging are built in from the start, not added later.
Yes. Converting raw accelerometer, heart rate, SpO2, HRV, or sleep stage data into risk scores, trend indicators, anomaly alerts, or population benchmarks is analytics engineering we do. Alert fatigue is the main risk: a system that fires on every reading outside a population norm gets ignored. We build scoring against individual baselines so the alerts that reach users or clinicians are the ones that matter.
Tell us what your device tracks, what platform it runs on, and what your users need to see. We will scope the companion app, sync layer, and analytics from there.