I'm Manaswin Pandey, a Senior Product Manager based in Gurugram with 12+ years across energy, strategy consulting, and enterprise life sciences SaaS. Currently at Indegene, leading a 28-person cross-functional team building AI-powered commercial products for global pharma clients.
I'm also serious about AI — not as a talking point, but as something I build with. I've shipped Gen AI into production inside one of the most regulated industries on the planet. And I've taken products from idea to deployed MVP over a weekend using Claude Code.
But here's the thing: the AI work sits on top of a decade of building across genuinely different contexts. I started as a process engineer at Shell — optimising gas plant uptime with sensor data. Then strategy consulting to an MD at ONGC Videsh, building $50M acquisition business cases at board level. Then product leadership at Indegene, where I've secured investment for three 0→1 products, led teams of 28, and earned two promotions in four years.
AI is the latest chapter. And it's changing how I think about every chapter before it.
Senior Product Manager, leading cross-functional teams of 28 — 3 PM direct reports, 25 engineering indirect reports — across the commercial content suite for global life sciences clients.
Built the business case and renewables integration strategy that secured Executive Committee and Board approval for a $50M Singaporean fuel cell start-up acquisition.
Led portfolio optimisation across $1.5B South American assets — gap analysis with Shell and Qatar Petroleum stakeholders. Reduced cash call processing time by 33%.
Achieved record 98% plant availability through sensor data analysis and control instrumentation optimisation. Key contributor to BG Group Annual Award 2015.
Managed water dumping project resulting in $1M annual increase in production revenue. Mentored 5 operators during crunch periods, maintaining 95%+ KPI performance.
This wasn't a single decision. It was a series of deliberate ones — each pulling me deeper into the AI ecosystem and expanding what I could build independently.
Completed the Advanced AI Product Leadership Certification on Maven — a practitioner-taught cohort covering AI strategy, model evaluation, and product decision-making in AI-native contexts. Taught by Dr. Marily Nika, Constantinos Neophytou, George Zoto, and Mark Cramer. This was the structured foundation that preceded everything built after it.
Started with Claude and ChatGPT for everyday PM work: drafting PRDs, sharpening strategy documents, running competitive analysis. This phase was about developing a feel for what these models are genuinely good at, where they fall short, and how to prompt them with enough precision to trust the output.
Built a quiz generation app — Lovable first for speed, Replit when I needed more control. First time going from "using AI to write" to "using AI to build." Discovered what AI-native builders abstract away, and where they hit a ceiling.
Built thewaypoint.ai using 21st.dev's component library. Moved from drag-and-drop into composing real React components with Tailwind CSS. The mental model shifted from "pages" to "systems of reusable parts."
Shipped the Gen AI content generation pipeline at Indegene — LLMs and vision models for compliant pharma marketing, with semantic retrieval over pre-approved content libraries. First time owning a production AI system in a regulated industry. Human-in-the-loop review was a deliberate architecture decision, not a limitation to overcome.
Built Portfolio IQ end-to-end using Claude Code — Supabase, Upstash Redis, Finnhub, FRED, Vercel CI/CD. Went through the full design workflow: Google Stitch for exploration, Figma Pro for production design, Figma + MCP to connect designs directly to Claude-generated React code.
Built the Executive Update Copilot — a multi-agent application on Vertex AI giving junior PMs a scaffold for leadership communication. Core principle: AI-assisted judgment, not AI-asserted judgment. Deliberately incomplete outputs with bracket placeholders force the PM to own the judgment calls. Actively used by my direct reports.
Each project started with a question that mattered to me as a PM. The builds were the way to find the answer.
The question: Enterprise AI adoption in regulated industries moves slowly — for good reason. Could you build something that genuinely works inside those constraints, not around them?
What I learned: The hardest product decisions weren't about the AI model. They were about information architecture — structuring the content library so retrieval was precise, not approximate. And about scope: knowing which parts of the workflow AI could own versus which needed a human in the seat. Human-in-the-loop isn't a limitation to overcome — it's the right call for where enterprise AI adoption actually is today.
The question: Junior PMs often know what happened — but struggle to frame it for leadership. Could AI give them a scaffold to think through before anything goes upward?
What I learned: The hardest design decision wasn't technical — it was deciding what the AI shouldn't do. An AI that writes the update for you creates passive PMs. An AI that asks "what's the business impact?" and leaves space for the answer creates sharper ones. That line between assistance and abdication is a product judgment call. I made it deliberately.
The question: Can a product manager with no engineering background own the full technical lifecycle of an AI product — from database design to deployment?
What I learned: Writing a thorough PRD before touching any code is what makes AI-assisted development fast. The skill isn't coding — it's specification, architecture decisions, and understanding cost trade-offs between models. That clarity is the same skill that makes cross-functional programmes work at scale.
Frontend in Next.js 14 with Tailwind CSS and shadcn/ui. Backend via serverless API routes — no dedicated server. Redis caching reduces Claude API calls by ~70% for repeated market queries. Supabase Row Level Security means the database enforces permissions, not application code.
| Service | What it does | Cost |
|---|---|---|
| Supabase | Database, auth, file storage | $0 |
| Upstash Redis | Caching layer | $0 |
| Finnhub | Stock data + news | $0 |
| FRED | Macro indicators | $0 |
| Vercel | Hosting + domain | $0 |
| Cloudflare | CDN + security | $0 |
| GitHub | Code + CI/CD | $0 |
| Google Stitch | Initial design | $0 |
| Claude API | Signal generation + parsing | $5 credit |
| Claude Pro (incl. Claude Code)* | AI IDE + dev workflow | $20/mo |
| Figma Pro* | Design system + MCP bridge | $15/mo |
| Project-specific spend | $5 |
* Claude Pro and Figma Pro used daily for work — not costs specific to this build.
The question: What's the difference between assembling an app from AI-generated blocks and composing a product with real design intent?
What I learned: Composition is a design skill, not just a technical one. Choosing which components to use, how they relate to each other, and where to break from the library's defaults requires the same product judgment as deciding what goes on a roadmap.
The question: Can AI tools take a product idea from zero to functional without writing code?
What I learned: AI app builders are excellent for validation and poor for iteration. The v1 comes fast, but the moment you need to deviate from what the tool assumed, you're fighting it. I now evaluate AI tools the same way I evaluate any product: by their constraints, not their demos.
Three industries. Three roles. One consistent thread: the ability to read a complex system, identify the highest-leverage problem, and move toward a solution that scales.
$4M+ SaaS revenue through 0→1 product launches. 50% improvement in client content turnaround. 60% operational efficiency gain. Two promotions in four years. PMO Excellence Award 2025. These came from knowing which problems to solve and in what order.
I started as an engineer — optimising real systems under real constraints. Before product management, I built a business case that moved $50M at board level. That background shapes how I think about product decisions: with a strong bias toward what's actually true about the system, not what's convenient to assume.
Five projects built, four in production. A Gen AI pipeline serving enterprise pharma clients globally. A full-stack SaaS deployed over a weekend. An agentic tool actively used by my direct reports. The progression from using AI to building systems that run on AI creates a different kind of product intuition — one that matters in roadmap conversations, architecture reviews, and decisions about where AI creates real value versus where it's just a feature checkbox.
Portfolio IQ went from concept to deployed product in a weekend. The Gen AI pipeline went from a scoped need to production across 7 markets. The Executive Copilot went from a team friction I noticed to a tool my junior PMs use every day. That comes from clear specs, right tools, and knowing which decisions to make now versus defer. In teams, it shows up as crisp PRDs, unambiguous success criteria, and fewer realignment cycles.
I'm always open to conversations about product strategy, Gen AI in regulated industries, or building at the intersection of product and engineering. If something here resonated, I'd welcome a conversation.