---
title: "Dipankar Yadav - Founder and Product Builder in Kanpur Nagar | Tal"
description: "Meet Dipankar Yadav, Building AI products that ship | IIT Kharagpur ’26 | Junior PM @ YuvaIntern | Portfolio in featured section | Open to APM / Product roles based in Kanpur Nagar. AI stack includes antigravity, claude code, and claude. Explore public work links including github."
image: "https://tal.club/meet/dipankar-yadav/share-image"
---

# Dipankar Yadav on Tal

## Headline

Building AI products that ship \| IIT Kharagpur ’26 \| Junior PM @ YuvaIntern \| Portfolio in featured section \| Open to APM / Product roles

## About

I started out building ML systems\. Somewhere along the way, I realized the part I loved most wasn't the model, it was figuring out what to build, who it was for, and whether it actually solved a real problem\. That's the thread that's pulled me toward Product\. A bit about my journey so far: → Dual Degree \(B\.Tech \+ M\.Tech\) at IIT Kharagpur, graduating July 2026\. Top 2\.8% in JEE Advanced 2021\. → Shipped AI Buddy \- a cross\-platform SaaS app with 3 LLM\-powered modes, built with Gemini API and deployed on a live public domain\. I owned the whole thing end to end, from deciding what to build to pushing it live\. → Led a team of 6 in my first year at IIT to build a mobile app for the National Disaster Response Force \(NDRF\) during Smart India Hackathon 2022\. We made it to the national finals\. That's where I first realized I liked the "deciding what to build" part of the work more than just the building part\. → Spent 3 months as a Research ML Intern at IIT KGP doing something most engineers skip — actually sitting with 56 farming households in UP, understanding their lives, and then building the data pipeline around what I learned\. That experience taught me more about user empathy than any course ever could\. → Built a bunch of ML stuff along the way: a spam classifier that hit 97\.68% accuracy, a recommendation engine using collaborative filtering, a cancer image classifier with DenseNet201, and an AI trading bot using Deep Q\-Networks\. → National Finalist at SIH 2022\. 3rd at Pitch Premier, Techkriti IIT Kanpur 2023\. → Built 2 AI workflows on n8n that automate my own lead generation and job hunt pipelines\. Small projects, real time savings, and honestly some of the most useful product thinking I've done outside of formal work\. What I'm doing now: I'm deep into Product Management \- frameworks, case studies, PRDs, product teardowns, the whole thing\. I'm writing about what I'm learning here on LinkedIn, breaking down AI products, and sharing case studies I'm building\. My background gives me a slightly unusual lens\. Most PMs come from MBA or business backgrounds\. I come from building things and from doing fieldwork with users most product teams never meet\. I think that combination matters, especially for AI products that need to work for more than just the urban power user\. If you're hiring for APM, Associate PM, or AI PM roles or just want to chat about AI products, building things, or the messy work of figuring out what users actually need, I'd love to connect\. Portfolio with case studies, PRDs, and product teardowns: https://dipankar\-portfolio\-eight\.vercel\.app

## Location

Kanpur Nagar

## AI stack

antigravity

claude code

claude

gemini

n8n

notebooklm

## Work experience

Founder and Product Builder at AI Solutions for SMBs \(Independent\), Mar 2026 \- Present · 6 mos, Remote, Independent business where I sell and ship AI\-built websites and ad creatives to small businesses across 6 countries \(USA, Canada, Germany, UAE, Singapore, and India\)\. What I actually do: Every client engagement starts with a discovery call, usually 30 to 45 minutes, where I figure out what the business actually needs\. Sometimes it's a landing page that converts, sometimes it's a set of ads for a product launch, sometimes it's both\. The pattern I've noticed: small business owners know what they want the outcome to be, but rarely know what the deliverable should look like\. That translation work is where most of the value is\. I use AI tools across the build \(design, copy, image generation, video editing\) and treat the AI as leverage, not as the product\. Every output still gets a human pass before delivery\. That has protected me from the usual AI\-slop problem that kills client trust\. Most recent project: A US\-based e\-commerce brand needed 7 ads in 10 days: 4 educational ads explaining their product and 3 UGC\-style ads for social media\. I scoped the full requirement from one 45\-minute discovery call, delivered a first cut in 4 days, iterated through 3 rounds of client feedback, and shipped final versions on day 9\. What this taught me about product: Selling to small business owners across 6 different markets forced me to actually understand user context in a way no class ever did\. A US e\-commerce founder cares about different things than a UAE service business or an Indian D2C brand, and the AI tools that work brilliantly for one can fall flat for another\. Every project has been a mini lesson in market fit, pricing psychology, and how buyers evaluate AI products when the hype wears off\. I bring all of that into how I think about product work\. Skills used: Client discovery, requirements scoping, creative direction, AI tool orchestration, project management, cross\-cultural communication

Product Manager Intern at Forage, Aug 2026 \- Aug 2026 · 1 mo, Remote

Junior Product Manager at Yuva Intern by Henry Harvin, Jul 2026 \- Aug 2026 · 1 mo, Remote, Junior Product Manager \(Intern\) \| E\-Commerce YuvaIntern \(NSDC\-affiliated\) • Own full product lifecycle for an e\-commerce niche: market research, gap analysis, personas, roadmap, feature prioritization, and success metrics across 4\-week cycle\. • Analyze user feedback \+ market signals weekly to surface 3\+ friction points and propose data\-backed enhancements to cross\-functional teams\.

Data & Product Analytics Simulation at Deloitte, May 2026 \- May 2026 · 1 mo, Worked through Deloitte Australia's simulation focused on solving a real client problem for an industrial factory, the kind of work where you can't just dump raw numbers on stakeholders and call it a day\. What I actually did: • Took messy production data and figured out which patterns mattered to the business and which were just noise • Built a Tableau dashboard that stakeholders could actually use \- meaning I had to think about what questions they'd ask, not just what visualizations looked impressive • Used Excel for forensic classification and turned the findings into three clear business recommendations What I took from it: Analytics is only useful when it becomes a decision\. That mindset is what pulled me toward product thinking in the first place\.

Research Intern \| User Research \+ ML Pipeline at Indian Institute of Technology, Kharagpur, May 2025 \- Jul 2025 · 3 mos, Kharagpur, West Bengal, India, Supervisor: Prof\. Ankit Shekhar, Dept\. of Agricultural & Food Engineering, IIT Kharagpur Project: Correlating Socio\-Demographic Factors with Climate Change Awareness Among Indian Farmers Most ML projects start with a clean Kaggle dataset\. Mine started with talking to 56 farming households across Kanpur Nagar and Dehat in UP \- 30 different variables, real people, real complexity\. Here's what the work looked like: • Designed the survey instrument and worked on the ground to collect primary data from farmers\. This was as much user research as it was data collection\. • Cleaned the dataset across 3 structured sections — missing values, duplicates, schema mismatches\. Did feature engineering on 6 categorical variables including income normalization and government\-scheme grouping\. • Ran statistical modeling using Scikit\-learn and Statsmodels and produced 8 EDA visualizations \- correlation matrices, 3D scatter plots, heatmaps, pairplots\. • The interesting finding: landholding size predicted income better than family size did\. Education level was the strongest predictor of climate\-change awareness\. I turned this into actionable policy recommendations for the department\. What this taught me about Product: You can't build for users you've never met\. Sitting across from a farmer who's trying to explain his decision\-making in his own words is something no dataset can replicate\. This is the experience I bring to product thinking \- building for the user you actually understand, not the one you imagine\. Tools: Python, Pandas, NumPy, Scikit\-learn, Statsmodels, Seaborn, Jupyter Notebook

## Education

Dual Degree \(B\.Tech \+ M\.Tech\), Agricultural & Food Engineering \| Agricultural Systems Management, Indian Institute of Technology, Kharagpur

Senior Secondary \(Class XII & X\), CBSE, Science \(Physics, Chemistry, Mathematics\), Army Public School, Kanpur

## Public links

- github: https://github\.com/CodeAlchemist09

[View the original profile](https://tal.club/meet/dipankar-yadav)

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