Everything you need, one page.
Approved bios, hi-res photos, talk abstracts, and booking details. I reply to media and booking requests within 24 hours.
50 words
Protik Roychowdhury is VP Product at Intellect, Asia's largest enterprise health platform (4M+ users). He builds trusted AI, marketplace, and high-stakes digital products across Asia. A three-time founder (one exit), published researcher, and ICF-certified coach with 15+ years of experience at Grab, Ola, and Exotel.
100 words
Protik Roychowdhury is VP Product at Intellect, where he leads product, design, and AI for Asia's largest enterprise health platform — 4M+ users across APAC, ANZ, China, and Latam at $20M ARR. He builds trusted AI, marketplace, and high-stakes digital products. Before Intellect, he led Growth, Engagement & Loyalty at Grab (Southeast Asia's leading superapp) and marketplace products at Ola (India's largest ride-hailing platform). A three-time founder and IIT Kharagpur alumnus, Protik has authored 3 academic papers on AI safety and LLM evaluation and is an ICF-certified career coach. Based in Singapore.
250 words
Protik Roychowdhury is VP Product at Intellect, where he leads product, design, content, localisation, and AI for a regulated, multi-tenant enterprise SaaS platform serving more than four million users across APAC, ANZ, China, and Latin America. He led Intellect's pivot from consumer app to B2B2C care ecosystem, growing enterprise revenue to $20M ARR, and has shipped GenAI features — triage, session preparation, treatment planning — inside one of the most safety-critical categories in software.
Before Intellect, Protik was Principal Product Manager for Growth, Engagement & Loyalty at Grab, Southeast Asia's leading superapp, and led marketplace products at Ola, India's largest ride-hailing platform — work spanning ML personalisation, marketplace fraud, and driver economics. He is a three-time founder; his first venture, voice-based social platform Croak.it!, was acquired by Exotel in 2015.
Protik has authored three academic papers on AI safety and LLM evaluation, including a vignette-based study of bias across nine large language models. He is an IIT Kharagpur alumnus, an ICF-certified coach, and speaks at conferences across Asia on AI product strategy, trust in high-stakes products, and product leadership. He is based in Singapore and works globally.
Headshot. Credit: courtesy of Protik Roychowdhury. Download ↓
On stage. Credit: courtesy of Protik Roychowdhury. Download ↓
From roadmaps to judgement: what product leaders need in the AI era
AI is absorbing the mechanical parts of product work. What remains — and what organisations now hire for — is judgement. What that looks like in practice, and how to build it in a team.
- Which PM tasks AI is unbundling first, with examples
- The "impact architect" profile and how to hire for it
- How to restructure product orgs around judgement, not process
Building AI products people can trust
Trust in AI is an engineered property with a three-layer architecture: transparency, control, and recourse. Drawn from shipping GenAI features to millions of users in a safety-critical category.
- A reusable trust architecture for any AI feature
- What users actually need to see before they rely on AI
- How trust failures compound — and how to design recovery
What high-stakes products can teach us about trust
Products where failure harms people — health, safety, money — force disciplines that every product team eventually needs. Lessons from mental health platforms and marketplace fraud.
- Compliance as a product surface, not a checkbox
- Harm taxonomies and tiered escalation design
- Why the regulatory moat is a competitive advantage
Scaling product teams without killing product judgement
Scaling a product org from 3 to 20+ without the thinking getting worse. Pods, rituals, and the difference between encoding taste and enforcing process.
- What to standardise and what to deliberately leave loose
- Hiring for judgement at different levels
- The founder-to-operator transition, from someone who made it
AI, personalisation, and the future of care
Three layers of AI personalisation — matching, content, timing — and the ethical constraints that make or break them, from Asia's largest mental health platform.
- Where personalisation creates value vs where it erodes trust
- The hybrid human-AI care model and why it wins
- Designing AI that challenges users instead of echoing them
Why UX is care: lessons from mental health product design
Design lessons from the highest-stakes UX context there is — onboarding someone in crisis — and what they teach about designing for any vulnerable or stressed user.
- Five UX principles that earn trust under stress
- Designing for cultural context across 20+ languages
- What "user empathy" means when it actually matters