Open to: APM  ·  Associate PM  ·  Product Manager  ·  AI PM  ·  SaaS PM

AI products + user empathy + data + shipping discipline

Turning messy user problems into focused AI-powered product bets.

Tabassum Khanum — Product Lead

I'm a Product Lead with 3+ years of building digital products people actually want to use. My work sits at the intersection of AI-powered features and deep user research — finding the real problem before writing a single spec. I care about shipping things that make a measurable difference, not just things that look good in a deck.

2+
years shipping products
10+
products delivered
4
live AI products built solo
Illustration of product research notes, analytics, and roadmap planning

APM portfolio

Research notes, clean metrics, soft launch energy.
Activation +18%

Ask better questions before building prettier answers.

Launch list

"Tiny friction, big drop-off."

Interview insight
AI signals

Summarize, predict, personalize, automate.

I like product work where the answer is not obvious yet: listening to users, finding the sharpest problem, sizing the opportunity, and helping teams choose what to build next.

Selected work

Case studies built for APM interviews.

ShopFloor AI — AI-powered manufacturing execution system
AI Product Live Build · Manufacturing

ShopFloor AI — AI-powered production tracking for manufacturers

Identified the gap in manufacturing visibility tools — shop floor operators lack real-time production tracking, inventory awareness, and a natural-language way to query floor status. Designed and shipped an AI-powered MES with voice, Kanban, and live inventory tracking.

  • Built a Kanban production board with drag-and-drop work orders across Queued, In-Progress, QC, and Done stages — giving plant operators a single real-time view of the shop floor.
  • Designed a bilingual AI assistant (Hindi + English) for natural-language floor queries — 'Aaj kitne orders pending hain?' — making the tool accessible to operators regardless of language preference.
  • Shipped real-time inventory tracking with auto-deduction on order completion and live reorder alerts, reducing the risk of production stoppages from stockouts.
  • Included a live demo mode (no sign-up required) with real manufacturing data, lowering the barrier to evaluation for potential users.

Live · AI-Powered MES · Voice in Hindi & English · Zero sign-up demo

Open live app ↗
ChurnGuard AI — AI-powered churn prediction and proactive retention
AI Product Case Study · PRD

ChurnGuard AI — AI-powered churn prediction and proactive retention

Identified why SaaS companies lose revenue to churn they could see coming — lagging indicators, reactive support, and no automated save workflows. Designed an AI system that scores customer health in real time and triggers proactive retention plays before cancellation intent becomes a cancellation event.

  • Defined a customer health scoring model combining behavioral analytics (product usage, login frequency, feature adoption) with CRM signals to surface at-risk accounts before they churn.
  • Designed agentic retention workflows using n8n — triggered automatically when a customer's health score drops below threshold, routing to the right save play (outreach, discount, success call) without manual intervention.
  • Specified proactive retention metrics: save rate, churn rate reduction, time-to-intervention, and health score accuracy — tied directly to ARR impact.
  • Scoped the product from signal detection through workflow orchestration to outcome tracking, covering the full retention loop in one system.

AI-driven · Churn Prediction · Proactive Retention · Agentic Workflows · n8n

Open case study Open PRD
Person writing product documents
AI Product PRD · V1.0

PM DocForge — AI-powered PM document generator

Designed and shipped an AI web app that transforms a brief product description into a full suite of PM documents — PRD, Design Doc, Technical Spec, GTM Brief, User Stories, and Release Notes — in under 60 seconds, no signup required.

  • Identified the core problem: writing a single PRD takes 2–6 hours; existing tools require manual editing or enterprise subscriptions.
  • Defined a frictionless two-step flow (Describe → Choose Docs) powered by Claude AI with instant DOCX/PDF exports.
  • Set 90-day success metrics: >65% activation rate, 500+ docs/week, <90s time-to-download, NPS > 45.
  • Scoped V1 in/out boundaries — no accounts, no collaboration, no integrations — to ship fast and validate core value.

Target: 500+ docs generated per week with NPS > 45 by end of Phase 3

Open PRD case study
Health insurance card with stethoscope representing PolicyPilot
AI Product PRD · Strategy

PolicyPilot — AI-first health insurance marketplace

Diagnosed why AI insurance assistants like PolicyBazaar's PB Buddy stall before purchase, then specified a smoother product where every recommendation is actionable and the journey from first question to active policy completes in one flow.

  • Identified three root causes of drop-off: dead-end plan recommendations (plain text, no buttons), fragmented intake, and no chat-to-checkout handoff.
  • Designed a 5-stage continuous flow — Discover → Advise → Decide → Buy → Serve — where context carries forward and nothing is re-entered.
  • Specified an actionable-recommendation contract: every plan rendered as a card with View / Buy buttons, making dead-end recommendations structurally impossible.
  • Defined success metrics: >40% recommendation-to-action rate, >60% flow completion, 2–3× chat-to-purchase conversion in under 10 minutes.

Target: 2–3× baseline chat-to-purchase conversion · >55% assistant containment

Open PRD case study
askDr.ai — RAG-powered health assistant interface
AI Product Case Study · Live Build

askDr.ai — RAG-powered health information assistant

Identified why generic AI chatbots fail at health queries — hallucinations, no citations, no safety guardrails — then designed and shipped a grounded health assistant using Retrieval-Augmented Generation, verified medical sources, and a safety-first architecture. Built solo in 4 days at $0 on free tiers.

  • Chose RAG over a plain LLM to make every answer source-traceable: responses cite the specific openFDA drug label or MedlinePlus guideline they came from, making hallucination structurally constrained.
  • Designed safety-first architecture: emergency queries (chest pain, self-harm, overdose) bypass RAG entirely and return crisis guidance before any AI processing begins.
  • Instrumented a full Mixpanel funnel (Page View → Category → Message → Response); 28.57% drop-off at landing drove the decision to add suggested starter questions and a clearer CTA.
  • Built and ran a 12-test automated eval suite — initial 7/12 score exposed missing safety patterns; fixed before launch and shipped at 12/12 across retrieval quality, grounding, safety, and hallucination resistance.

12/12 eval suite · Safety-first RAG · Shipped in 4 days · $0 infra cost

Open case study Live app ↗
askDr.ai PRD — Product Requirements Document
AI Product PRD · v1.0

askDr.ai — Product Requirements Document

Full PRD for a RAG-powered health information assistant — from problem statement and user personas through P0/P1 requirements, safety & compliance rules, success metrics, data architecture, 4-phase release plan, and risk register.

  • Defined three user pain points (hallucinating AI, information overload, no safety net) and two primary personas — the Informed Patient and the Caregiver — with explicit anti-personas to keep scope tight.
  • Specified P0 requirements enforcing RAG grounding, a pre-RAG emergency screen, no-diagnosis policy, and mandatory citations on every response as non-negotiable trust constraints.
  • Set five success metrics with targets: grounding rate >70%, safety pass rate 100%, engagement >3 messages/session, funnel conversion >30%, eval score 12/12.
  • Documented a 4-phase release plan (Medicine RAG → All Categories → Vision + Streaming → Analytics + Eval) with clear ship criteria per phase and a risk register covering critical, high, and medium risks.

Shipped · All P0 requirements delivered · 12/12 eval · Live at ask-dr-ai.vercel.app

Open PRD
Phone showing the WhatsApp app listing
Strategy SWOT deck

WhatsApp SWOT analysis and Smart Priority Inbox strategy

Analyzed WhatsApp's strengths, weaknesses, opportunities, and threats, then proposed AI-first usability improvements to reduce message overload.

  • Identified message overload, limited search, and chaotic groups as core usability gaps.
  • Recommended AI chat summarization, smart priority inbox, and context-based notifications.
  • Defined metrics like meaningful conversations, time to find, response rate, and notification fatigue.

Expected outcome: 20% drop in notification fatigue score over one quarter

Open PDF case study
Cvent website screenshot showing CventIQ and Cvent Sidekick
Teardown Product analysis

Cvent product teardown and enterprise event SaaS analysis

Deep-dived into Cvent's event management platform, pricing model, user personas, activation friction, AI strategy, and enterprise event workflows.

  • Mapped the end-to-end event manager journey across onboarding, registration, go-live, onsite, and analytics.
  • Identified friction in 3-step publishing, module complexity, email tooling, pricing opacity, and India-market gaps.
  • Prioritized fixes like Smart Publish, event-type onboarding, transparent pricing, and CventIQ opportunities.

Target: Cut time-to-first-event under 30 min for new enterprise users

Open PDF teardown
NUA period care products arranged on a coral background
Femtech Onboarding

NUA onboarding case study and subscription conversion strategy

Studied NUA's app onboarding, period tracker activation, subscription discovery, loyalty loop, and marketplace-to-app retention gap.

  • Found friction in the 10+ question plan quiz, empty tracker state, spend-only loyalty, and buried subscription CTA.
  • Mapped impact to MRR, subscription conversion, tracker activation, Comfort Club progression, and pause/cancel rates.
  • Recommended surfacing subscription earlier, progressive onboarding, engagement-based rewards, and symptom-led discovery.

Expected outcome: +12% subscription conversion within 2 sprints

Open PDF case study
Live Build Job Tracker · Web App

Job Application Tracker — stay on top of every opportunity

A focused web app to track job applications end-to-end — add roles, log status updates, set follow-up reminders, and see your pipeline at a glance.

  • Built to solve a real personal pain point: losing track of applications across dozens of companies.
  • Clean dashboard showing pipeline stage (Applied → Interview → Offer → Rejected) per application.
  • Live and free to use — no setup, just sign in and start tracking.

Live product · Try it at job-tracker-nu-five.vercel.app

Open live app ↗

Live AI builds

Products I've shipped and put online.

How I work

A simple product loop.

01

Frame the problem

Clarify the user, context, pain, business goal, and the decision to be made.

02

Find the signal

Combine interviews, funnel data, support themes, and competitor patterns.

03

Choose the bet

Prioritize with impact, confidence, effort, risk, and learning value.

04

Measure and learn

Define success metrics, guardrails, rollout plans, and follow-up questions.

Career timeline

Where I've worked and what I've built.

Product Lead

Web Spiders Group
Full-time Jul 2024 – Present Kolkata, India · On-site
  • Owned end-to-end product delivery for event services apps, CMS platforms, and websites across enterprise clients — CII EXCON, North Star Group, Internet Retailing, and PCOA — managing requirements, stakeholder alignment, timelines, and cross-functional coordination from kickoff to launch.
  • Led product lifecycle for Android and iOS applications across multiple client accounts — scoping features, aligning design and engineering, and managing releases end to end.
  • Defined and shipped Voice First AI agents for Hindalco and Gajraj Hyundai — translated business needs into agent workflows, prioritised capabilities, and drove client-readiness and handoff.
  • Shaped product requirements and delivery for manufacturing industry solutions at Gainwell and Hidromas — spanning discovery, feature prioritisation, design reviews, QA coordination, and production release.
Product Ownership Stakeholder Management Voice AI Agents Android · iOS CMS Roadmapping Client Delivery Figma

Frontend Developer

The Mainstage Productions
Full-time Apr 2023 – Jul 2024 Remote · 1 yr 4 mos
  • Owned end-to-end product delivery of Pocketxtra — an event management Android app supporting multiple roles — from Figma design through development, QA, and Play Store release.
  • Led stakeholder meetings to gather requirements, align on priorities, and communicate delivery progress across the project lifecycle.
React Native Figma Android Play Store Stakeholder Management End-to-End Delivery

Full Stack Engineer

Crio.Do
Apprenticeship Jan 2022 – May 2023 1 yr 5 mos
  • Built full-stack features using Node.js, HTML5, and web technologies on an ed-tech platform.
  • Developed and shipped module-level functionality as part of a structured engineering programme.
Node.js HTML5 JavaScript REST APIs Full Stack

Full Stack Developer

Ekosight
Internship Oct 2022 – Mar 2023 6 mos
  • Contributed to frontend and backend development for an early-stage product startup.
  • Shipped web application features across a 6-month engagement.
JavaScript HTML · CSS React Node.js Full Stack

Product toolkit

Skills I bring to the team.

Discovery

User interviews, problem statements, journey maps, opportunity sizing.

interviews JTBD journeys

Analytics

Funnels, cohorts, event taxonomy, SQL basics, AI feature measurement.

funnels cohorts AI evals

Execution

PRDs, acceptance criteria, AI workflow specs, roadmap tradeoffs, stakeholder updates.

PRDs roadmaps tradeoffs

Design sense

Wireframes, usability review, information architecture, copy clarity.

wireframes IA UX copy

Credentials

Certifications.

IBM

Product Management: Foundations & Stakeholder Collaboration

IBM May 2026
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IBM

Generative AI: Introduction and Applications

IBM Apr 2026
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IBM

Product Management: An Introduction

IBM Apr 2026
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freeCodeCamp

JavaScript Algorithms and Data Structures

freeCodeCamp Aug 2022
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Tabassum Khanum Open to: APM  ·  Associate PM  ·  Product Manager  ·  AI PM  ·  SaaS PM

Let us talk

Product Manager, open to meaningful product conversations.

I work across product discovery, execution, and AI-enabled experiences. Always up for a good product conversation — reach out via email or connect on LinkedIn.

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