Product management portfolio
I build products that ship.
Some of them, I also code.
I'm Anuraag Burman, a product leader based in Singapore with 10+ years of experience in product strategy, execution, and vision. Currently a Super IC at Delivery Hero / Foodpanda, where I've worked across growth, fintech, wallets, subscriptions, and incentives. I'm also deeply curious about AI. Not as a buzzword, but as a genuine shift in how we build and operate. So I've been learning, building, and experiencing that shift firsthand.
The short version
Most PMs talk about AI. I build with it. I've shipped a RAG-powered knowledge base on GCP that cut support costs by 12%. I've taken a stock intelligence app from "what if" to deployed MVP over a weekend using Claude Code. I know the difference between Sonnet and Haiku, and more importantly, I know when to use which one and why.
But here's the thing: the AI stuff sits on top of a decade of building products across wildly different worlds. I started my career practicing architecture in Hamburg and Barcelona. Then I co-founded an e-commerce platform for construction materials in India ($12M revenue). Then fintech comparison tools in Singapore. Then coliving. Then food delivery at scale. Somewhere in between, I became someone who cooks elaborate meals on weekends and thinks about subscription economics in the shower. AI is the latest chapter, and it's shaping how I think about every chapter before it.
Where I work
Delivery Hero / Foodpanda
Super IC on the Subscriptions team. I own product strategy, execution, and cross-functional delivery for subscriptions across 15+ markets, working across engineering, design, operations, data, and regional teams to align on outcomes that move the business.
Subscriptions, Fintech & Partnerships
Singapore · APAC & EU markets · Oct 2021 – present
SUPER IC
GMV impact
$150M+
Subscription P&L ownership
Churn reduction
36%
Pricing + benefit optimisation
Conversion uplift
8% → 38%
1-click enrolment + free trials
Support cost cut
-12%
AI knowledge base (Vertex AI)
- Subscription economics: Engineered 1-click enrollment and strategic free trial entry points that swung conversion from 8% to 38% and meaningfully lifted resubscription rates and LTV.
- Wallet strategy: Architected the shift to internal fintech wallet. Order share went from 3% to 27%, $30M GMV in 2 months, 6.99x ROI on cashback spend. Then connected Wallet to Subscriptions to build a loyalty flywheel.
- Partner integrations: Brought Spotify, TADA (SG), LineGo (TW), and Bolt (MY) into the Pro subscription. Turned it from "you save on delivery" to "this is your lifestyle membership." Built a reusable V1/V2 integration playbook.
- Incentives at scale: Owned the EUR 300M+ incentives domain. Built Offers Zone and Audience Targeting used across Quick Commerce, Shops, and Pandago. Drove EUR 60M in cost savings.
- AI knowledge base: Built a GenAI-powered semantic search tool using GCP Vertex AI for the Turkey subscriptions launch, giving internal teams and CX instant access to product details. Now extended to all markets. Reduced support costs by 12% through faster time-to-resolution.
- Payment reliability: Overhauled routing infrastructure to hit 99.8% payment success rate. In fintech, that number is everything.
AI proficiency timeline
How I got here
This wasn't a single decision. It was a series of small ones, each pulling me deeper into the AI ecosystem and expanding what I could build on my own.
Late 2024 / Early 2025
Discovery: AI as a thinking partner
Started with Claude and ChatGPT for everyday PM work: drafting PRDs, sharpening strategy docs, running competitive analysis. Then built custom Gemini Gems to help the team move faster. This phase was about developing a feel for what these models are good at, where they fall short, and how to prompt them well enough to trust the output.
Prompt engineering
Claude
ChatGPT
Gemini Gems
June 2025
First build: no-code AI tools
Picked up Lovable and Replit to build a quiz app. First time going from "using AI to write" to "using AI to build." Discovered the landscape of AI-native app builders, what they abstract away, and where they hit a ceiling. This is where I started forming opinions about which tools are suited for exploration versus production.
Lovable
Replit
No-code to low-code
September 2025
Levelling up: component-based frontends
Built thewaypoint.ai using 21st.dev's component library. Moved from drag-and-drop into composing real React components with Tailwind CSS. This was when the mental model shifted from "pages" to "systems of reusable parts," and I started understanding how modern frontend stacks actually work.
21st.dev
React
Tailwind CSS
Component architecture
January 2026
Enterprise AI: RAG, auth, cloud infrastructure
Built a production knowledge base for Foodpanda using Vertex AI Search, Auth0, and Google Cloud. This was the first time working with RAG pipelines, SSO authentication, serverless hosting, and multiple AI models (Claude Code, Gemini, ChatGPT) in the same project. It deepened my understanding of how AI fits into enterprise systems, not just standalone apps.
Vertex AI Search
Auth0
Google Cloud
Claude Code
Gemini
ChatGPT
RAG pipeline
March 2026 to present
Full-stack: databases, APIs, caching, deployment
Built PortfolioIQ end-to-end using Claude Code. Set up Supabase (Postgres + auth + storage), Upstash Redis for caching, Finnhub and FRED for market data, Vercel for deployment with CI/CD. Also went through the full design workflow: Google Stitch for initial generation, then Figma Pro with MCP for production design connected directly to Claude. This is where all the previous layers came together.
Next.js 14
Supabase
Claude API
Claude Code
Figma + MCP
Vercel
What I've been building
Things I built because I wanted to
Each project started with a question that mattered to me as a product leader. The builds were the way to find the answer.
PortfolioIQ
LIVE IN PRODUCTION
The question: Can a product manager with no engineering background own the full technical lifecycle of an AI product, from database design to deployment? PortfolioIQ is an AI-powered stock intelligence platform where users upload brokerage statements and receive budget-aware signals.
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 tradeoffs between models like Sonnet and Haiku. That clarity is the same skill that makes cross-functional programmes work at scale.
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 tradeoffs between models like Sonnet and Haiku. That clarity is the same skill that makes cross-functional programmes work at scale.
Build time
A weekend
Focused, end-to-end
Project-specific cost
$5
Claude API credit; rest is free tier
AI strategy
Single vendor
Anthropic: Sonnet + Haiku
Purpose
PM → full-stack proof
Non-coder building end-to-end
TR Knowledge Base
SHIPPED · PRODUCTION
The question: Could AI meaningfully reduce the operational cost of scaling a subscription product into a new market? We were launching subscriptions in Turkey and needed a way for internal teams and CX to get instant answers about the product without relying on tribal knowledge.
What I learned: The product decision that mattered most wasn't the AI model. It was designing the information architecture so the right answers surfaced for the right queries. That's a product problem, not an engineering one. The tool now serves all markets and drove a 12% reduction in support costs through faster resolution times.
What I learned: The product decision that mattered most wasn't the AI model. It was designing the information architecture so the right answers surfaced for the right queries. That's a product problem, not an engineering one. The tool now serves all markets and drove a 12% reduction in support costs through faster resolution times.
AI engine
Vertex AI Search
RAG pipeline over internal docs
Impact
-12% support costs
Faster resolution, all markets
Hosting
Google Cloud (Cloud Run)
Serverless, auto-scaling
Built with
Claude Code + Gemini + ChatGPT
Multi-model dev workflow
Vertex AI SearchAuth0Google CloudClaude CodeGeminiChatGPT
Waypoint.ai
SHIPPED
The question: What's the difference between assembling an app from AI-generated blocks and actually composing a product with real design intent? Built thewaypoint.ai for friends using 21st.dev's component library.
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 and what doesn't.
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 and what doesn't.
Component source
21st.dev
AI-native UI primitives
Key takeaway
Architecture > assembly
Composition is a skill
21st.devReactTailwind CSS
Quiz app
SHIPPED
The question: Can AI tools take a product idea from zero to functional without writing code? Tried Lovable first for speed, then Replit when I needed more control.
What I learned: AI app builders are excellent for validation and terrible for iteration. The v1 comes fast, but the moment you need to deviate from what the tool assumed, you're fighting it. That taught me to evaluate AI tools the same way I evaluate any product: by their constraints, not their demos.
What I learned: AI app builders are excellent for validation and terrible for iteration. The v1 comes fast, but the moment you need to deviate from what the tool assumed, you're fighting it. That taught me to evaluate AI tools the same way I evaluate any product: by their constraints, not their demos.
Build time
~5 hours
Across two platforms
Key takeaway
AI generates; PMs curate
Lovable = speed, Replit = control
LovableReplitJavaScript
Putting it together
Where strategy meets building
Four industries, three countries, two companies co-founded, and one consistent pattern: an entrepreneurial mindset applied wherever the context demands it. Whether it was building a business from zero in India, navigating fintech regulation in Singapore, or aligning twelve regional teams around a single subscription strategy at Delivery Hero, the core skill is the same. Read the market, identify the highest-leverage problem, and move decisively toward a solution that scales.
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A decade of product work across industries and stages.
$150M+ GMV in subscription strategy. 36% churn reduction. A 30-point conversion swing. EUR 60M in cost savings through incentive audience targeting. Wallet adoption from 3% to 27% order share. 99.8% payment reliability. Three ride-hailing partnerships across three countries. These came from knowing which problems to solve and in what order.
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Startup instincts inside a scaled organisation.
Two companies co-founded before Delivery Hero. In a startup, you build the thing yourself. In a large org, you align engineering, design, data, ops, and regional stakeholders around a shared outcome. Both require clarity about what matters. That duality shapes how product decisions get made.
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AI as a genuine capability, not a talking point.
Five projects built, four in production. An enterprise RAG pipeline that started in one market and now serves support teams globally. A full-stack SaaS deployed over a weekend. This builds real intuition about what AI can and can't do for a product, and that intuition matters in every roadmap and prioritisation conversation.
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Velocity that comes from clarity.
PortfolioIQ went from concept to deployed product. The TR Knowledge Base went from a Turkey launch need to a tool used across the organisation. 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 re-alignment cycles.
Say hello.
I'm always happy to connect with people thinking about product strategy, AI-native development, or building at the intersection of product and engineering. If something here resonated, I'd welcome a conversation.