The problem Seravia solves
In India and across many parts of the world, mental health remains a largely underserved and stigmatized area. Despite the growing awareness of psychological well-being, a significant gap persists between those who require mental health support and those who actually receive it. Traditional therapy remains inaccessible for many due to geographic limitations, financial constraints, social stigma, or a lack of consistent engagement between therapy sessions. Moreover, individuals often struggle to articulate their emotions or recognize patterns in their mental state without structured tools, leading to underreporting or miscommunication during therapy.
It addresses this context gap by empowering individuals to express themselves through journaling in a safe, private, and guided digital environment. It leverages journaling as a therapeutic bridge between sessions and integrates AI-powered reflections and therapy-style feedback to encourage consistent self-awareness. The platform is designed to reduce stigma by allowing users to engage without fear of judgment or the need for immediate disclosure. Users can interact with therapy-aligned AI personalities, receive personalized reports based on their emotional patterns, and track their growth over time.
Additionally, licensed therapists on the platform can access AI-generated insights from user journals, enabling more meaningful therapy sessions. This creates a hybrid model of engagement—combining professional support with self-driven introspection. By offering both guided journaling and therapist collaboration,ensures mental health support is not just reactive but continuous and proactive.
The platform thus fills a critical void by promoting therapy through personalization, reducing stigma, and democratizing access to mental wellness tools using AI in a secure, ethical, and user-first manner.
Challenges we ran into
During the development of Seravia, we encountered several technical and architectural challenges, especially around data synchronization and third-party integrations.
A major issue was maintaining a synchronized state between journal data in MongoDB and the frontend cache. Since we preload a user’s journals upon login for seamless UX, conflicts emerged when multiple updates (create/edit/delete) occurred rapidly across components—leading to stale reads or overwritten data. We resolved this by adopting an event-driven state management pattern using MongoDB change streams on the backend and reactive hooks on the frontend to enable real-time updates.
Another significant hurdle came with OpenAI’s GPT API integration. Handling concurrent user requests often triggered 429: Too Many Requests errors or led to excessive retries, which affected responsiveness and delayed therapy report generation. As our system relies on the API to analyze journals and provide AI-driven therapy responses, this posed a serious concern. We addressed it by building a robust queueing system with exponential backoff, request deduplication, and a proxy-level rate limiter. We also implemented caching for frequently used prompt-response pairs to reduce dependency on real-time API calls.
Additionally, integrating therapist modules and supporting secure, multi-user interactions required strict role-based access control and well-structured MongoDB schemas. Ensuring data privacy and access isolation was critical, especially when therapists handled multiple clients. We tackled this using scoped queries, modular services, and a layered N-tier architecture.
These challenges ultimately helped us build a more scalable, resilient, and secure mental health platform.
Tracks Applied (1)
Main Track
Technologies used