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My Work ( Overview )

Module 1: Construction Budgeting Module

Role: Lead Product Manager | Timeline: 6 Months | Status: Live (v1.1)


1. Executive Summary
Managing a $100M construction budget on Excel is financial suicide. One formula error can hide millions in cost overruns. I led the 0-to-1 development of the Budgeting Module, a centralized financial command center that replaced disconnected spreadsheets with a gated, version-controlled approval engine.

2. The Problem Space

  1. Excel Hell: Budgets were living documents managed on local desktops. There was no Single Source of Truth.
  2. Manual Drudgery: Estimators spent 2-3 days every month manually splitting costs across a 24-month schedule.

User Personas

  • The Estimator (Rahul): Frustrated by manual data entry and constant version emails.
  • The PMO Head (Suresh): Anxious about hidden costs and unauthorized budget changes.

3. Strategic Vision
To build a financial operating system where every Rupee is tracked, versioned, and approved before it is spent.

4. The Solution (Deep Dive)

  1. The Monthly Distribution Logic
    I designed an algorithm to automate the cash flow forecast.
  2. Input: Total Cost (e.g., ₹50L) & Schedule Duration (e.g., 100 Days).
  3. Innovation: For Materials (like Steel), I added a Payment Milestone logic (e.g., 40% Advance, 60% Delivery) to reflect real-world cash outflows.
  4. User Flow & UX
  5. Auto-Fetch: User selects a project. The system pulls all Work Packages and Quantities from the Engineering DB.
  6. Rate Entry: User only inputs the Unit Rate. The system auto-calculates the Total Amount.
  7. Key Artifacts
  8. Gated Workflow: Draft -> Pending Approval -> Locked.
  9. Revision History: A GitHub-style diff view showing exactly which line item changed between V1 and V2.

5. The Impact

  1. Efficiency: Reduced budget creation time from 5 days to 4 hours.
  2. Control: 100% elimination of Shadow Budgets (offline spreadsheets).

Module 2: Schedule Planning & Real-Time Engine

Role: Product Manager | Timeline: 4 Months | Status: Live (v2.41 & v2.42)


1. Executive Summary
A construction schedule is usually obsolete the moment it is printed. I built a Real-Time Predictive Engine that uses live site data to auto-calculate the project's completion date every single day. It's not just a tracker; it's a GPS that reroutes the project when it hits traffic.

2. The Problem Space

  1. The Reality Gap: Project Managers (PMs) compared progress against "Lifetime Average," which masked recent slowdowns (e.g., due to rain).
  2. Reactive Management: Delays were only discovered after the deadline was missed.
  3. No Sandbox: PMs couldn't ask "What if we double the workforce?" without messing up the live data.

3. The Solution (Deep Dive)

  1. The Rolling Window Algorithm
    This is the core innovation. Instead of using Planned Duration, the system calculates two dynamic metrics:
  2. 15-Day Average PR (Production Rate): How fast is the team working right now?
  3. Required PR (The Catch Up Metric): How fast must we work to hit the deadline?
  4. User Flow: The Parallel Universe Sandbox
    To solve the No Sandbox problem, I designed a Multi-Plan Architecture.
  5. Create Plan: User creates "Recovery Plan A".
  6. Simulate: User tweaks variables (e.g., increase Production Rate by 20%).
  7. Commit: User clicks "Set as Active" to overwrite the live schedule.
  8. The Ghost Bar Visualization
    We needed to show Plan vs. Reality instantly.
  9. Ghost Bar (Outline): Represents the Planned Schedule.
  10. Solid Bar (Filled): Represents the Actual Progress.

4. The Impact

  1. Forecast Accuracy: Improved completion date prediction accuracy from 60% to 90%.
  2. Proactive Action: PMs identified critical delays 3 weeks earlier than before.

Module 3: My Startup – Lovedit App

Role: Founder & Product Manager | Timeline: 12 Months | Status: Acquired (Data Exit)


1. Executive Summary
I bootstrapped Lovedit, a relationship wellness app, with just ₹10,000. By focusing on hyper-specific user problems (breakups & intimacy), I scaled it to 1,000+ users and secured a 10X exit in just 12 months by selling our unique user behavior data to a larger incumbent.

Note: Read the full story of this journey in my dedicated blog post: My Bootstrapped Startup.

2. The Problem Space

  1. The Gap: The market had dating apps and therapy apps, but nothing for the "messy middle"—breakups, relationship maintenance, and financial stress for couples.
  2. The Rejection: VCs dismissed it as "too niche" or "just a student project".

3. The Solution (Deep Dive)

Product Strategy: Instead of fighting the giants on matching, I built a Relationship Healthcare ecosystem.

  1. Content Engine: We created high-value guides that drove massive organic traffic.
  2. Financial Feature: We introduced a Marriage Loan feature, connecting couples to banks.

4. The Impact

  1. Data Valuation: Our user behavior data became so valuable that an incubated company acquired the data report for 1 Lakh—a 10X return on my initial capital.

Module 4: Matrimony App Onboarding Enhancement

Role: Product Manager | Focus: UX & Growth | Status: Live


1. Executive Summary
In matrimony apps, trust is the currency. A generic Sign Up form causes high drop-offs because it feels transactional. I redesigned the onboarding flow to be Context-Aware, changing the entire app's language based on who is creating the profile (Parent vs. Self).

Note: Read How to Enhance User Onboarding & Profile Creation for Matrimony App in my dedicated blog post: Read More.

2. The Problem Space

  1. The One-Size Fail: Most apps ask "What is your name?" even if a father is creating a profile for his daughter. This breaks immersion and trust.
  2. Data Fatigue: Matrimony profiles require 100+ data points.

3. The Solution (Deep Dive)

  1. The Digital Doorstep Flow
  2. The Magic Question: The very first screen asks, "Who are you creating this profile for?"
  3. If Daughter: The UI changes to "What is her education?"
  4. If Myself: The UI says "What is your education?"
  5. Privacy-First Income: Replaced exact salary fields with Income Ranges (e.g., 5-10 LPA), reducing user friction.

4. The Impact

  1. Trust: The Context-Aware language immediately established the app as a serious, family-oriented platform.
  2. Completion: Reduced drop-offs by ~40%.

Module 5: Fintech Loan App Conversion (The EMI Screen)

Role: Product Manager | Focus: Conversion Rate Optimization (CRO)


1. Executive Summary
The Select EMI Plan screen is the graveyard of loan apps. I redesigned this critical screen to focus on Transparency and Psychological Safety, transforming it from a Debt Calculator into a Plan Selector.

Note: Read How to Stop Losing Customers on Your Loan App’s Most Important Screen in my dedicated blog post: Read More.

2. The Problem Space

  1. Hidden Fees: Users would select a plan, see a higher number on the next page, and feel cheated.
  2. Choice Paralysis: Presenting 10 different EMI tenures overwhelmed users.

3. The Solution (Deep Dive)

  1. The Transparency UI
  2. The Breakdown Card: I added a dynamic breakdown right on the selection card (Principal, Interest, Processing Fee).
  3. The Recommended Tag: We used data to highlight the tenure with the highest success rate (e.g., "6 Months - Best Value") to guide indecisive users.

4. The Impact

  1. Trust: By showing the exact final amount upfront, we reduced drop-offs at the final Sign Agreement stage.

Module 6: E-Commerce Seller Onboarding

Role: Product Manager | Focus: Supply Side Growth


1. Executive Summary
I digitized the onboarding process into a Self-Serve Seller Portal that automates GST verification and catalog upload, reducing activation time from days to minutes.

Note: Read How to Enhance Your E-commerce Seller Onboarding in my dedicated blog post: Read More.

2. The Problem Space

  1. The Document Black Hole: Sellers emailed PDF documents. Verification took 48 hours.
  2. Catalog Friction: Uploading 100 products via a CSV file was too technical for small vendors.

3. The Solution (Deep Dive)

  1. The Instant Workflow
  2. API Verification: Integrated with GSTIN APIs. Sellers enter their GST number, and the system auto-fetches their business name and address.
  3. Smart Catalog: Built a "Clone Product" feature. Sellers can search for an existing generic product (e.g., iPhone 13) and just add their price/stock.

4. The Impact

  1. Speed: Reduced "Sign-up to First Product Live" time by 90%.

Module 7: HubSpot CRM (Churn Reduction)

Role: Growth PM (Case Study) | Focus: Retention Strategy


1. Executive Summary
I analyzed the Activation Gap and proposed a Template-First Onboarding strategy to get users to their Aha! Moment within the first session.

Note: Read How to Tackle the High Churn Rate ~ A HubSpot CRM Case Study in my dedicated blog post: Read More.

2. The Problem Space

  1. The Blank Slate: A new CRM is empty. It requires contacts, pipelines, and templates to be useful.

3. The Solution (Deep Dive)

  1. The Pre-Filled Strategy
  2. Industry Templates: During signup, ask "Industry?" (e.g., Real Estate).
  3. Auto-Population: Immediately load the CRM with a Real Estate Pipeline (Lead -> Site Visit -> Negotiation) instead of a generic Sales Pipeline.
  4. Dummy Data: Include 5 Sample Leads so the user can practice moving cards immediately.

4. The Impact

  1. Psychology: Users immediately saw how the tool would work for their specific business.

Module 8: Dating App Revenue Strategy

Role: Product Strategist | Focus: Market Positioning & Revenue


1. Executive Summary
To build a $10M business, you can't just copy Tinder. I developed a blueprint for a High-Intent Premium App that focuses on scarcity and curation rather than volume.

Note: Read the blueprint for a $10M premium dating app in the next 6 months in my dedicated blog post: Read More

2. The Problem Space

  1. Burnout: Users are tired of swiping endlessly with low match rates.
  2. The Freemium Trap: Ad-based models require millions of users to make money.

3. The Solution (Deep Dive)

  1. The Club Model
  2. The Waitlist Gating: Create artificial scarcity. You can't just sign up; you must be approved or invited.
  3. The 3 Matches Rule: Instead of unlimited swipes, give users only 3 highly curated matches per day.
  4. High ARPU: Charge a high subscription fee ($30/month).

4. The Impact

  1. Brand: Establishes the app as a Status Symbol rather than a utility.

Module 9: Vudio.ai (Shoppable Video AI)

Role: Product Manager | Focus: E-Commerce Growth | Status: Live (Shopify App)


1. Executive Summary
I led the development of Vudio.ai, an AI-powered Shoppable Video widget for Shopify. This feature allows influencers to explain products in 30-60 second reels directly on the product page, with an integrated Add to Cart button.

2. The Problem Space

  1. The Static Trap: Traditional product pages are boring. Users bounce because they can't visualize how the product looks or feels in real life.
  2. Missing Sales Assistant: There is no one to answer "Does this lipstick match my skin tone?"

3. The Solution (Deep Dive)

  1. The Contextual Suggestion Logic
    This isn't just a video player; it's a recommendation engine.
  2. Context Check: The system identifies the product attributes (e.g., "Dusky Skin Tone" variant selected).
  3. AI Suggestion: The video widget automatically updates to show an influencer with a similar skin tone applying that exact shade.
  4. Key Features
  5. AI Scripting: The system uses LLMs to auto-generate high-converting scripts for the influencers.
  6. Multi-Format Widget: The video can live as a Reel, a Story, or a Popup.

4. The Impact

  1. Sales: Generated 12,000+ Orders directly attributed to the video widget.
  2. Conversion: 10.65% Plays-to-ATC (Add to Cart) Rate.

Module 10: Digitizing Concrete Quality Control

Role: Product Manager | Duration: 3 Months | Status: Live


1. Executive Summary
I built a digital Concrete Testing Module that automates the scheduling, recording, and verification of all quality tests.

2. The Problem Space

  1. The Missed Interval Crisis: Concrete must be tested at specific ages. QEs were manually tracking dates on whiteboards, often missing the 7-day window.
  2. User Personas: The Site QE says, "I have 50 cubes curing right now. I don't know which one needs testing today."

3. The Solution: A Smart Testing Workflow

  1. The Auto-Generation Logic
  2. The Timeline: The system generates a visual timeline for that specific sample, marking the 7th, 14th, and 28th days as "Due Dates".
  3. Alerts: On the due date, the QE gets a mobile notification: "Test Cube #402 today".
  4. The Conditional Logic
  5. Rule: If the 28-Day test passes, the workflow closes.
  6. Exception: If the 28-Day test fails, the system automatically unlocks the "36-Day Test" (conditional) and flags the sample for Review.

4. The Impact

  1. Compliance: 100% adherence to testing intervals (zero missed tests).

Module 11: Material Inspection Suite

Role: Product Manager | Duration: 2 Months | Status: Live


1. Executive Summary
I unified three fragmented processes—Material Testing, Sample Testing, and Methodology Testing—into a single Material Inspection Suite, ensuring that no vendor material is used without a digital Pass certificate.

2. The Problem Space

  1. The Fragmented Workflow: The Material Inspector checks the quantity, but the Quality Engineer checks the quality. They used different systems.

3. The Solution: The Gated Procurement Flow

I built a dependency between Inward and Payment.

  1. Trigger: Security creates a Gate Entry.
  2. Hold: The system puts the material in a "Quarantine" state.
  3. Release: Only after the test passes does the system move the material to "Inventory" and enable the "Payment" button.

4. The Impact

  1. Cost Savings: Prevented payment for 20 Lakhs worth of defective steel in the first month.
  2. Efficiency: Unified 3 disparate logs into one Quality Dashboard.

Module 12: Invoice Rejection Workflow

Role: Product Manager | Duration: 6 Weeks | Status: Live


1. Executive Summary
I built a Conditional Rejection Workflow that decouples the physical exit of the vehicle from the financial rejection of the invoice.

2. The Problem: The Gate Deadlock
When a material failed quality checks, the Site Store Manager had to physically stop the truck from leaving until the paperwork was done. Trucks blocked the gate for hours.

3. The Solution: Decoupling Logistics & Finance

  1. Physical Stream: If material fails, generate a "Gate Pass (Returnable)" immediately. Let the truck leave.
  2. Financial Stream: Trigger a "Debit Note" workflow in the ERP.
  3. The Trigger Rule: The "Reject Invoice" button is hidden by default. It only appears if Material Test Status == Failed OR Quantity Check == Shortage.

4. The Impact

  1. Logistics: Reduced gate turnaround time for rejected vehicles from 4 hours to 15 minutes.
  2. Audit: Created a 100% traceable digital trail for every rejected item, ending he-said-she-said disputes with vendors.

Module 13: User Onboarding & Activation (SaaS)

Role: Product Manager | Duration: 2 Months | Status: Live (v1.1)


1. Executive Summary
I designed a Gamified Onboarding Sequence that guides users through a strict, dependency-based setup path.

2. The Problem: Analysis Paralysis

  1. The Empty State: A new user lands on the dashboard. It's blank. There are 50 buttons.
  2. The Error: Users would try to create a "Schedule" before defining the "Calendar", causing the system to crash.

3. The Solution: The Golden Path

  1. The Guided Journey
    I forced a linear setup process.
  2. Step 1: Build Project Tree (Highlighted). All other buttons disabled.
  3. Step 2: Set Working Days.
  4. The Progress Tracker
    I added a permanent widget in the top-right corner:
  5. Red: Setup Incomplete.
  6. Green: Ready for Launch.
  7. Psychology: Users hate seeing a Red status. This drove them to complete the boring setup tasks quickly.

4. The Impact

  1. Activation: Reduced time-to-value for new projects from 1 week to 1 day.
  2. Support: Reduced "How do I start?" support tickets by 70%.

Module 14: Global Configuration System

Role: Product Manager | Duration: 3 Months | Status: Live

1. Executive Summary
I built a Global Configuration Engine that acts as the Single Source of Truth, enforcing corporate standards across all sites.

2. The Problem: Data Fragmentation

  1. Site A calls it M25 Grade. Site B calls it M-25 Concrete.
  2. Result: Analytics was impossible.

3. The Solution: Parent-Child Inheritance

  1. The Cascading Update
  2. Global Layer (Admin): The HQ defines the master list (e.g., M25, M30).
  3. Local Layer (Project): When a new project starts, it inherits this master list.
  4. Update Logic: If HQ changes the standard test for M25 from Slump to Flow, this change automatically propagates to all new projects.

4. The Impact

  1. Analytics: Enabled true cross-project benchmarking for the first time.