Behavioral Analytics Platform
Design and Development for Pretaa

Pretaa is a behavioral analytics company. Its solutions aim to revolutionize addiction recovery and behavioral health. By partnering with biometric makers like Google Fitbit and developing their own proprietary Smart Rings, Pretaa provides caregivers and treatment centers with real-time biometric data.

Pretaa helps create a holistic view of a patient’s body and mind with these solutions. This data-based, easily graspable, holistic view of patient health drives better healthcare outcomes.

Pretaa approached our studio with a powerful vision: to build, from the ground up, their ultra-secure, clinically intelligent care delivery system that had to be capable of translating raw data from wearables (like the Google Fitbit, Apple Watch, and Pretaa’s own Smart Ring) into actionable interventions.

They needed a technology partner to construct the complex infrastructure needed to transform raw real-time biometric data into presentable info for both patients and caregivers.

Our core objectives for developing the Pretaa platform were to:

• Build a Secure, Scalable, and HIPAA-Compliant, Zero-Knowledge Data Backend: The first goal was to engineer a backend architecture capable of ingesting and processing millions of real-time biometric data points, while guaranteeing patient privacy through a zero-knowledge framework and auditable HIPAA compliance checks
• Ensure Seamless Wearable Integration: We needed to build ultra-secure API integrations + data pipelines with third-party wearables like Fitbit (as well as Pretaa’s own tools) to ensure that all biometric info sourced from wearable devices was encrypted, transmitted in real-time, and with perfect integrity at every stage
• Create an Actionable Caregiver Dashboard: The plan was to develop a user-facing dashboard and mobile-friendly interface (UI) for clinicians and caregivers. The UI had to present complex data in intuitive, actionable formats in all types of user scenarios – from overloaded clinicians needing caseload views to  anxious family caregivers needing individualized alerts
• Implement Proactive Intervention Tools: Another key goal was to give the Pretaa platform a suite of ‘smart’ machine learning features, including – customizable notifications, geofencing capabilities, behavioral tracking algorithms that empower caregivers to deliver timely, data-driven support; only with these tools could our target users identify subtle indicators of patient distress/relapse that a regular alert system would miss

As you can see, this was not just a regular app design/development project. We had to build a new care delivery ecosystem that could access meaningful data and truly understand a patient’s well-being in real-time.

Achieving these goals meant solving many technical and strategic challenges:

Data Integration Hurdles:

• A core challenge was architecting a centralized system that was capable of aggregating disparate, real-time data streams from multiple wearable sources. The platform had to reliably capture/process continuous biometric data flows without any data loss/corruption. We also had to ensure that the patient, who may be struggling with shame or anxiety, didn’t feel surveilled. So, we always prioritized patient agency by creating a zero-knowledge architecture where even Pretaa’s admins couldn’t access raw patient data without patient approval

Security, Compliance:

• The platform had to be built to scale to thousands of simultaneous users while maintaining perfect, auditable compliance with healthcare regulations like HIPAA §164.312. This required meticulous infrastructure planning and secure logging for every data access event. These steps would ensure that the system was enterprise-ready for treatment centers from day one.

Translating Data into Human Insight:

• The platform could not just be a repository of data. The UI needed to tell a story about the patient’s well-being, not just display numbers. Plus, wearables generate a constant stream of data. A simple heart rate spike could be a workout or a panic attack. We needed to build algorithms sophisticated enough to differentiate between normal life and a true moment of need, thus avoiding “alert fatigue” for caregivers. Ensuring all system alerts were graspable and meaningful was a major challenge.

Overall, the project required a blend of high-level data science, secure infrastructure, and user-centric design. Thankfully, our team was up to it.