Founder & CTO Tranquil Labs, Jan 2024 to Jan 2025
Back during the pandemic, watching close friends struggle with mental health revealed a frustrating pattern. Even when someone realized they needed help, two massive barriers blocked their path:
A close friend in college hit a breaking point but refused to see campus counselors. He trusted them, but he was terrified word would leak out. Seeing that gap firsthand—between needing support and having a private, affordable place to find it—is what drove me to build a solution.
Existing options fell short of bridging this gap:
While AI companions like Wysa or Replika provided easy access, they often lacked clinical depth and emotional nuance. We saw room for an experience that brought together low friction and genuine depth.
We tested a raw prototype with university students right away to gather honest reactions. Their feedback surprised us: even though the AI's technical engine was basic, users weren't evaluating its advice quality. Instead, they valued having a safe space to express thoughts they had kept hidden for years without fear of judgment.
That realization changed our entire roadmap. We abandoned our original, solution-focused bot and reworked the system to prioritize listening. The AI gave users space to process raw emotions first before applying gentle CBT-based guidance. Listening took priority; solutions came second.
That initial build expanded into a beta program with over 50 early testers and rapidly climbed to 4,000+ signups. Users engaged daily with journaling, chat sessions, and mood logging, driving average session durations past 9 minutes—far higher than industry averages.
Overcoming team bias: Since none of us co-founders kept personal journals, we almost sidelined the journaling feature. User testing forced us to rethink that assumption. People wanted to journal, but first-timers didn't know what to write. In response, we built a guided journaling tool: short prompts that helped users start, which the AI then reshaped into full entries. Catching our personal blind spot turned a dismissed feature into a core user favorite.
Predictive Mood Engine: Instead of waiting for users to reach out in crisis, our algorithm analyzed daily mood patterns. If data showed a user's mood dropping on weekday afternoons after back-to-back meetings, the app triggered a quick check-in or breathing exercise beforehand. Users also received trend reports mapping their main stressors alongside AI-generated coping strategies, shifting the experience from reactive to proactive.
Contextual Memory & Continuity: To avoid feeling like a generic utility, the AI retained history across past interactions and mood trends. This enabled personalized support, allowing the system to recognize recurring patterns and surface targeted resources without restarting the conversation every session.
Ethical Habit Engineering: Retention in mental health applications is notoriously tricky, especially when success means users feel better and rely on the app less. We applied Nir Eyal’s Hook Model intentionally to build consistency:
We spent time debating the ethics of gamifying wellness. Was this truly helping users, or were we creating unnecessary screen addiction?
Three key factors confirmed our approach:
This research confirmed that our engagement loop supported user health alongside business metrics.
Latency Optimization: I designed and scaled our backend architecture using Flask, Docker, and cloud infrastructure, cutting API response times by 30%. Beyond standard performance gains, low latency made daily habit loops feel seamless and was critical for supporting natural, real-time voice conversations.
Building long-term trust required strict boundary setting and privacy controls:
By the end of my tenure, user growth scaled from our initial 50 testers to over 10,000 active users. Retention rose 25% and daily active usage grew by 32%, driven by predictive interventions, tailored experiences, and intentional engagement loops. Expansion was driven by campus ambassador programs and organic word of mouth across target student communities.
We secured 5 Lakh INR in pre-seed backing, earned a spot among Cisco & Nasscom’s Thingqubator Top 10 Student Startups, and presented Tranquil at the Startup Mahakumbh and India AI Summit.
I led a cross-functional team of 10+ engineers, designers, and AI specialists, taking ownership of product direction and backend architecture.
The main challenge was operational rather than technical. Early on, the four co-founders operated on implicit understanding. As we brought on interns and new team members, that informal style led to misalignment due to a lack of shared documentation.
To fix this, we put structured workflows in place:
Looking back, three clear lessons stand out:
Building Tranquil proved that great products aren't built on loud feature ideas—they're shaped by listening closely to user behavior, analyzing feedback, and making data-informed adjustments. That user-first mindset continues to shape how I approach software engineering and product leadership.