B2B SaaS · PLG · AI Features · Platform & API Products · 0→1 · India
I build B2B SaaS products that grow. From founding and scaling a platform to ₹40 Cr ARR (acquired by Reliance Retail) to shipping AI-native features at a product-led company — I've lived every stage of the SaaS journey: pre-PMF scrappiness, growth-loop design, enterprise motion, and the hard conversation about what to kill.
Currently a full-time Senior PM. IIM Bangalore
I've owned multi-tenant SaaS platforms end-to-end — API-key authentication, role-based access control, usage metering, webhook delivery, developer documentation, and the billing infrastructure underneath all of it. I understand why the platform layer is where most SaaS companies win or lose long-term.
Subscription tiers, usage-based pricing, per-seat vs per-outcome — I've designed and iterated on pricing models with real revenue at stake. I know how to model the impact of a pricing change before shipping it, and I know that pricing is a product decision, not a finance one.
Time-to-value is the metric that predicts everything else. I design onboarding flows around it. I instrument activation milestones before the feature ships. I've run cohort analyses that changed the roadmap direction because the data disagreed with the intuition.
I've built self-serve funnels, in-product upgrade prompts, usage-triggered nudges, and the freemium limits that convert. I understand where PLG breaks down (enterprise deal sizes, compliance requirements, security reviews) and when to layer in a sales-assist motion.
Most "AI features" in SaaS are search wrappers or summarisation bolted onto existing flows. The ones that actually drive retention are deeply integrated into the core workflow, have clear fallbacks when the model is wrong, and are evaluated against real user outcomes — not demo impressions. I design the latter.
| Metric | How I've worked with it |
|---|---|
| ARR / MRR | Modelled expansion revenue from tier upgrades, usage overages, and seat growth |
| Activation rate | Defined activation events, instrumented them, and redesigned onboarding around improving them |
| Churn & NRR | Led win/loss analysis, identified leading churn indicators, built retention playbooks |
| Time-to-value | Reduced TTV by redesigning trial flows and removing friction from first meaningful action |
| API adoption | Tracked integration depth as a leading retention signal; built developer-facing features to deepen it |
| Feature adoption | Designed in-product discovery, contextual nudges, and gated releases to drive rollout |
| Area | What I've shipped |
|---|---|
| Multi-tenant SaaS platforms | Tenant isolation, API-key management, usage metering, billing integration |
| GenAI features | Agentic tool-use loops, RAG pipelines, LLM cost optimisation, eval frameworks |
| Developer-facing products | API design, webhook systems, SDK specs, developer docs, sandbox environments |
| Conversational AI | AI agent flows, human handoff logic, multi-channel messaging (WhatsApp, in-app) |
| Subscription billing | Tier design, upgrade/downgrade flows, usage-based overage billing, invoice management |
| Analytics & data | SQL fluency, funnel analysis, cohort retention, A/B test design and read-out |
On the platform vs features trade-off: Features win demos. Platform wins retention. I've seen both sides of this mistake and I default to asking "does this make the platform more valuable, or does it just ship something new?"
On pricing: Pricing is positioning. Every time you change a tier limit or add a new plan, you're making a statement about who the product is for. Most teams treat it as a spreadsheet exercise. I treat it as a product decision.
On growth loops: The best SaaS products have growth that is structural, not campaign-dependent. I look for the loop — the mechanism by which existing users generate new users or expand revenue — before I approve a roadmap.
On AI features in SaaS: The question isn't "can we add AI here?" It's "does adding AI reduce the time between a user's problem and their outcome?" If the answer isn't clearly yes, it shouldn't ship.
On churn: Churn is a product problem before it's a CS problem. By the time a customer calls to cancel, you've already lost them twice — once when they stopped finding value, and once when nobody noticed.
- IIM Bangalore — MBA
- Harvard Executive Education — AI & Business Strategy
- Ex-founder — Built a D2C SaaS platform from zero to ₹40 Cr ARR, acquired by Reliance Retail
- Currently: Senior PM at a product-led SaaS company, owning AI features and platform growth
- Senior PM / Lead PM at B2B SaaS companies where AI is core to the product, not a checkbox
- Head of Product at Series A–C startups building platform or infrastructure SaaS
- Platform PM roles owning API products, developer experience, or multi-tenant architecture
- Conversations — even if the timing isn't right
