moonbatant

4+ years building AI-native products

Mamoon Mondal

AI Product Manager

I build AI-native products end to end — running a PLG self-serve product and an enterprise B2B agentic product in parallel, owning 0-to-1 discovery, agent design, and eval-driven reliability.

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📍 Bengaluru, India · Open to PM roles in AI

About

I'm an AI product manager who builds AI-native products from zero to one. As founding PM at Supanote.ai, I run a PLG self-serve clinical-scribe product and an enterprise B2B benefits-verification agent in parallel — owning discovery, agent design, and the eval harnesses that keep them reliable in production.

Before that, at Innovaccer, I shipped AI agents that automated healthcare operations at scale, lifting per-user efficiency 4x and unifying post-acquisition product integrations.

I use AI heavily inside my own workflow too — building internal agents on Claude Code + MCP that automate program and design ops and multiply my throughput as a PM. I write about those experiments below.

Experience

Where I've shipped

Founding Product Manager · Supanote.ai

Bengaluru, India

Jan 2026 — Present
  • Lifted PLG paid conversion 5% in 2 months on the AI clinical scribe through funnel analysis, onboarding iteration, and reliability improvements — running PLG and enterprise B2B in parallel.
  • Scaled the B2B benefits-verification agent to 2.2x ARR in 3 months post 0-to-1 launch by re-architecting rule-based workflows into an agentic, harness-based orchestration with parallel agents, skill hierarchies, tool calling, and context/memory loops.
  • Drove production incident rate from 3.5% to <1% in 2 weeks via architectural audits and a custom eval harness.
  • Built the founding product operating layer — analytics, agile cadence, and internal AI agents automating program and design ops — driving a 5x lift in product velocity.

Product Manager · Innovaccer

Noida, India

Jul 2022 — Dec 2025
  • Automated fax-based referral intake for specialty clinics with an AI agent, improving per-user operational efficiency 4x (40% faster TAT).
  • Scaled adoption of an automation platform 75% in 3 quarters by segmenting users, uncovering key pain points, and shipping bi-weekly experiments guided by KPIs.
  • Improved complex UI flows via user-journey analytics and A/B testing, lifting active engagement 35%.
  • Scaled automation execution capacity 2x through demand forecasting and rapid cross-functional delivery, maintaining <1% error rate under higher load.
  • Unified post-acquisition product integrations into a single UX, achieving 100% SLA adherence and <2% incident rate post-launch.
  • Built a contract-driven integration layer replacing one-off API/Kafka hooks, cutting rollout time by 67%.

B.Tech, Mechanical Engineering · CGPA 8.28

Indian Institute of Technology (IIT) Dhanbad

2018 — 2022

Selected work

Case studies

All case studies →
Supanote.ai 2026

Building a 5x PM agent on Claude Code

An always-on autonomous PM agent: one Slack message in, a PRD, a mockup, and a Linear ticket out — built on Claude Code + MCP with parallel research sub-agents and full observability.

5x
Product velocity lift
1 Slack msg
→ PRD, mockup & Linear ticket
Minutes
Insight → reviewable artifact (was hours)
Read case study →
Innovaccer 2022–2025

Automating fax-based referral intake with an AI agent

Specialty clinics still run referrals over fax. An AI intake agent cut turnaround 40% and 4x'd per-user operational efficiency.

4x
Per-user operational efficiency
40%
Faster turnaround time
Read case study →
Supanote.ai 2026 · ~2 months

Rebuilding Supanote's PLG activation path

Signup→subscription sat flat at ~12% for months. I reframed the quarter around conversion, found the real leak — setup, not the demo note — and lifted trial-to-paid +5% in two months.

+5%
Trial-to-paid conversion (2 mo)
5 in 7
Activation metric (notes / days)
3.5% → <1%
Incident rate, cut in parallel
Read case study →
Supanote.ai 2026 · 3 months post-launch

Re-architecting benefits verification into an agentic system

Took a brittle rule-based enterprise workflow to a harness-based agent orchestration — 2.2x ARR in 3 months, with eval-gated reliability for an enterprise bar.

2.2x
ARR in 3 months
0 → 1
Launched the product line
Eval-gated
Regressions caught pre-production
Read case study →

Contact

Let's build something.

I'm open to PM roles in AI, and always happy to swap notes on agent design, evals, and building with Claude.

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