DEV Community: PRADEEP HEBBALLI

Semantic Paradox

PRADEEP HEBBALLI — Tue, 12 May 2026 03:31:28 +0000

I Built Semantic Paradox with MeDo — A Fast Brain Game That Fights Your Instincts

Live app: https://app-b4mn8ezne7ep.appmedo.com/

What I built

Semantic Paradox is a high-speed Stroop-effect brain-training game built with MeDo.

The game looks simple at first: players see a color word such as RED, BLUE, GREEN, or YELLOW. But the word may appear in a different font color.

The player must follow the active rule:

HARMONY: tap the written word
PARADOX: tap the font color

The challenge is that the rule changes during the game, forcing the player to constantly switch between reading and visual recognition.

Why I built it

Most brain-training apps feel either too clinical or too repetitive. I wanted to create something that feels like a real game but still trains useful cognitive skills.

Semantic Paradox is inspired by the Stroop effect, where the brain experiences conflict between reading a word and recognizing its color. I turned that conflict into a fast 60-second challenge with scoring, feedback, leaderboard competition, and an AI-generated performance profile.

How the gameplay works

Each session lasts 60 seconds.

The player sees:

a timer
a score
the current rule: HARMONY or PARADOX
a large target word
four answer buttons: RED, BLUE, GREEN, YELLOW

Example:



If rule = HARMONY:
    correct answer = written word

If rule = PARADOX:
    correct answer = font color

Correct tap = +10 points
Incorrect tap = -5 points
Accuracy = correct taps / total taps

Decision Doppelgänger

PRADEEP HEBBALLI — Tue, 12 May 2026 03:19:13 +0000

Decision Doppelgänger – Meet Your Possible Futures

Elevator Pitch

Decision Doppelgänger simulates your future selves, compares possible choices, and helps you make smarter decisions with clearer trade-offs.

Project Link

https://app-b0nifp2dle69.appmedo.com/

About the Project

Decision Doppelgänger is an interactive decision-support app built with MeDo. It helps users explore important choices by simulating different versions of their future self.

The idea was inspired by a question everyone faces:

What would happen if I chose a different path?

Every major decision creates multiple possible futures. One version of you accepts the job. Another starts the business. Another waits. Another moves city, changes career, studies a new skill, rejects the offer, or takes the risk.

Decision Doppelgänger turns that idea into a structured reflection tool. Instead of giving a simple yes or no answer, the app helps users compare possible choices through future-self perspectives, trade-offs, benefits, risks, regrets, opportunities, and long-term consequences.

The goal is not to predict the future perfectly. The goal is to help users think more clearly before making important decisions.

What It Does

Decision Doppelgänger helps users compare possible decisions by generating different “future selves” for each option.

Users can:

Enter a decision they are facing
Add multiple possible choices
Compare future outcomes
View emotional, financial, career, lifestyle, and risk impacts
Generate future-self perspectives
Identify hidden trade-offs
Reflect on regret potential
See which option aligns best with their priorities

The app is useful for decisions such as:

Should I accept this job?
Should I quit and start a business?
Should I move to another city?
Should I learn a new skill?
Should I take a risky opportunity?
Should I choose stability or growth?

The core idea is:

Better decisions come from seeing the trade-offs clearly.

A simple way to think about the app’s decision model is:



Decision Fit = Benefits + Personal Alignment - Risks - Regret Potential

SafeReach AI

PRADEEP HEBBALLI — Tue, 12 May 2026 03:10:26 +0000

SafeReach AI – Global Scam & Crisis Safety Assistant

Elevator Pitch

SafeReach AI helps users detect scam-risk indicators, follow crisis guidance, use safety chat, and generate family alerts in Live Safety Mode.

Project Link

https://app-bg447rmtqhhd.appmedo.com/

About the Project

SafeReach AI is a global no-login safety assistant built with MeDo for seniors, families, caregivers, migrants, travelers, and vulnerable users.

The project was inspired by a real and serious problem: during emergencies, people often face two risks at the same time — the crisis itself and scams that exploit fear, urgency, and confusion.

A person may receive a message about bank verification, flood relief, hospital payment, government aid, emergency donation, or a family member in danger. In stressful situations, it can be difficult to know what is real, what is risky, and what action to take next.

SafeReach AI was built to help users answer one urgent question:

Is this message risky, and what should I do safely right now?

What It Does

SafeReach AI includes:

Live Safety Mode for real user-entered messages and crisis guidance
Demo Mode for sample scam and crisis scenarios
Scam-risk message analyzer
Guided Safety Chat Assistant
Crisis Navigator with step-by-step emergency guidance
Senior Mode with large, simple action buttons
Emergency Wallet Card generator
Family Alert Generator
Self-Help Safety Center
Country Safety Packs
Privacy-safe local analytics

The scam analyzer uses rule-based risk scoring to identify indicators such as:

OTP, PIN, password, or CVV requests
Bank/KYC verification pressure
Suspicious links
Urgent payment requests
Fake relief or government aid claims
Fake charity or donation requests
Family emergency pressure
Instructions not to call family, police, or banks

Instead of saying a message is definitely a scam, SafeReach AI uses safer language:

This message shows scam-risk indicators.

This keeps the app responsible while still giving practical safety guidance.

Inspiration

The inspiration came from real-world scam patterns that target vulnerable people, especially during emergencies.

Scammers often send messages like:

“Your bank KYC is expired. Enter OTP now.”
“Flood relief approved. Pay a processing fee.”
“Your son is in hospital. Send money immediately.”
“Government aid available. Upload your ID and bank details.”
“Donate now to an official rescue team.”

These messages combine urgency, emotion, money, and confusion. SafeReach AI slows the user down, highlights red flags, and guides them toward safer action.

How I Built It

I built SafeReach AI using MeDo through prompt-driven development and iterative testing.

MeDo helped turn a complex safety concept into a working web app by allowing me to describe the product structure, user flows, safety rules, UI behavior, and demo scenarios step by step.

The build process included:

Designing the Live Safety Mode and Demo Mode structure
Creating a global country and language selector
Building the suspicious message analyzer
Adding rule-based scam-risk scoring
Creating crisis guidance flows
Designing a senior-friendly interface
Adding a guided safety chat assistant
Creating emergency wallet card and family alert tools
Adding privacy-safe analytics
Testing the app for mobile usability, safe behavior, and clear disclaimers

MeDo was especially useful for rapid iteration. I could test the app, identify weak points, and improve the experience through follow-up prompts.

One important refinement was changing the app from a demo-only concept into a usable product with Live Safety Mode as the default and Demo Mode as an optional walkthrough mode for judges and testing.

What I Learned

I learned that safety apps must be designed differently from normal productivity apps. Users may be stressed, confused, elderly, or in danger, so the interface must be calm, simple, and direct.

I also learned the importance of responsible boundaries. SafeReach AI does not contact emergency services, does not diagnose medical problems, does not give legal advice, and does not guarantee scam detection. Instead, it provides structured safety guidance and encourages users to verify through trusted people and official channels.

I also learned how powerful prompt-driven development can be for building multi-feature apps quickly. MeDo helped me focus on product thinking: what the user sees, what they click, what they understand, and how safely the app behaves.

Challenges I Faced

The biggest challenge was balancing usefulness with safety.

A scam and crisis app must be helpful, but it must not overclaim. I had to carefully design the wording so the app says “risk indicators” instead of “confirmed scam.”

Another challenge was avoiding a demo-only experience. The app originally focused heavily on sample scenarios, but I refined it so users can actually paste their own messages and use Live Safety Mode.

A third challenge was making the app senior-friendly. That required large buttons, simple text, clear warnings, and direct actions like:

I Am Safe
Do Not Share OTP/PIN
I Received Money Request
Ask Safety Assistant
Create Emergency Card

The goal was to reduce confusion and make safer action easier.

Built With

MeDo
Prompt-driven app development
Web app interface
Rule-based risk scoring
Responsive UI design
Local app state
Privacy-first no-login structure
Guided safety chat logic
Global country safety packs
Live Mode and Demo Mode workflows

Key Features

Live Safety Mode

Users can paste their own suspicious message, select their country and language, and receive scam-risk indicators, red flags, safe next steps, and a family alert template.

Demo Mode

Judges and users can run preloaded scam and crisis scenarios to understand how the app works.

Guided Safety Chat

A rule-based assistant helps users navigate situations like suspicious messages, money requests, crisis steps, senior support, and family alerts.

Crisis Navigator

Users can select crisis types such as flood, heatwave, earthquake, fire, medical emergency, cyber scam, evacuation, or power outage and receive structured guidance for:

First 10 minutes
First 1 hour
Next 24 hours

Senior Mode

A simplified interface with large buttons helps seniors take quick actions without navigating complex screens.

Emergency Wallet Card

Users can create a local emergency card with key details, emergency contacts, medical notes, and safety reminders.

Privacy-Safe Analytics

The app tracks local interaction counts for product feedback without storing raw user messages.

What’s Next

Future improvements could include:

Offline crisis safety packs
More countries and languages
Voice/audio mode for low-literacy users
Verified local safety partners
NGO/admin safety content dashboard
Official emergency alert integrations
Encrypted emergency card storage
Caregiver sharing mode
More advanced scam-pattern detection
Accessibility improvements for seniors and disabled users

Final Summary

SafeReach AI is a global no-login safety assistant that helps users detect scam-risk indicators, follow crisis guidance, use guided safety chat, generate family alerts, and create emergency wallet cards.

Using MeDo, I was able to transform a complex public-safety idea into a working web app with Live Safety Mode, optional Demo Mode, senior-friendly design, and privacy-safe local analytics.

XenoLexis

PRADEEP HEBBALLI — Tue, 12 May 2026 02:55:19 +0000

XenoLexis – The Xenolinguistics Lab

Elevator Pitch

XenoLexis is a first-contact decoding lab where users analyze alien signals, uncover language patterns, and translate the unknown through frequency, grammar, and logic.

Project Link

https://app-bdk4u6bmkphd.appmedo.com/

About the Project

XenoLexis is an interactive xenolinguistics lab built with MeDo. The project is inspired by the idea of first contact: what if humanity received an unknown alien transmission and had to decode it from scratch?

Instead of presenting translation as a simple text conversion task, XenoLexis turns language decoding into a structured reasoning experience. Users explore alien-style signals, analyze symbol patterns, inspect frequency clues, interpret grammar hints, and gradually move from mystery to meaning.

The app is designed to feel like a sci-fi research console while teaching real analytical ideas such as pattern recognition, symbolic reasoning, frequency analysis, and language structure.

The core idea is simple:

Language is not only vocabulary. It is repetition, probability, grammar, context, and structure.

The decoding logic is inspired by frequency analysis. If one symbol appears again and again, it may represent a common word, repeated concept, or grammatical marker. XenoLexis turns that idea into an interactive first-contact puzzle.

What It Does

XenoLexis lets users enter a futuristic decoding lab where they can:

Start a new alien signal
Try a practice signal
Analyze alien-style language patterns
Explore symbol repetition and frequency clues
Interpret grammar and structure hints
Use pattern recognition to move toward translation
Experience a cinematic sci-fi interface
Learn analytical thinking through interactive play

Inspiration

The project was inspired by science fiction, cryptography, linguistics, and the mystery of unknown communication systems.

I wanted to create an app that makes users feel like they are part of a first-contact research team decoding an alien language for the first time. The goal was to make learning feel like a mission, not a lesson.

How I Built It

I built XenoLexis using MeDo, which helped me turn the idea into a working web app through prompt-driven development.

MeDo made the build process faster and more visual. I described the product concept, user flow, visual direction, and required interactions, then refined the app step by step. Instead of starting from a blank development setup, I used MeDo to rapidly move from idea to prototype to a polished interactive experience.

The development process included:

Defining the xenolinguistics concept
Creating the sci-fi landing page
Building the signal-starting flow
Adding a practice signal mode
Designing the frequency-analysis-inspired experience
Refining the hero section and action buttons
Improving the layout for desktop and mobile
Removing unnecessary elements that distracted from the main experience
Testing the app flow for clarity and usability

MeDo was especially useful for rapid iteration. I could test the app visually, identify what felt confusing, and improve the design through follow-up prompts. For example, I refined the landing page, simplified the main actions, and removed unnecessary UI so users could focus on the decoding experience.

What I Learned

I learned that a strong educational app does not need to look like a classroom. It can feel like a game, a mission, or a research simulation.

I also learned how useful prompt-driven development can be when building creative products quickly. MeDo helped me focus on product thinking: what the user sees, what they click, what they understand, and how the experience feels.

I learned the importance of balancing:

Visual atmosphere
Usability
Educational value
Clear interaction flow
Minimal but meaningful UI
Fast iteration through MeDo prompts

A sci-fi interface can easily become too decorative, so I focused on keeping the main actions clear: start a new signal or try a practice signal.

Challenges I Faced

One challenge was making the concept easy to understand quickly. “Alien language decoding” is exciting, but the user still needs to immediately understand what the app does and where to click.

Another challenge was keeping the interface visually rich without overcrowding it. I refined the layout, removed unnecessary elements, and focused the hero section around the core decoding experience.

A third challenge was translating a fictional concept into a usable interface. The app needed to feel mysterious, but not confusing. It needed to look cinematic, but still work clearly as an interactive learning tool.

MeDo helped by allowing fast design and functionality refinement. I could adjust the interface, test the flow, and improve the experience without rebuilding everything from scratch.

Built With

MeDo
Prompt-driven app development
Web app interface
Responsive UI design
Procedural language concept design
Frequency-analysis-inspired logic
Sci-fi visual design
Iterative UI refinement

What’s Next

Future improvements could include:

More alien species and language families
Deeper grammar puzzles
Translation scoring
Progressive difficulty levels
Audio signal decoding
Achievement badges
Mission-based campaign mode
Leaderboard or decoding accuracy score
More guided educational explanations inside the decoding lab

Final Summary

XenoLexis is a first-contact language decoding experience that combines sci-fi storytelling with educational pattern recognition. It helps users think like linguists, cryptographers, and explorers while decoding the unknown.

Using MeDo, I was able to rapidly transform a creative concept into a polished interactive web app with a strong visual identity and a clear learning experience.

FP&A Command Center

PRADEEP HEBBALLI — Tue, 12 May 2026 02:04:31 +0000

FP&A Command Center AI Ultra

Elevator Pitch

FP&A Command Center AI Ultra is a local-first AI-enabled Financial Planning & Analysis command center for planning, forecasting, variance analysis, financial statements, CSV uploads, templates, exports, and live data workflows — no login or backend required.

App Link

https://app-bjcv8bavb401.appmedo.com/

Inspiration

The project was inspired by the gap between spreadsheets and enterprise FP&A platforms.

Spreadsheets are flexible, but they become fragile as planning models grow. It becomes difficult to manage data quality, assumptions, versions, templates, calculations, and outputs reliably.

Enterprise FP&A tools such as Anaplan, Oracle EPM, OneStream, and Workday Adaptive Planning are powerful, but they are expensive, implementation-heavy, and usually require enterprise infrastructure.

I wanted to build something between these two worlds: a lightweight FP&A command center that feels structured like an enterprise planning tool but runs locally in the browser.

The goal was not to replace enterprise EPM systems. The goal was to make core FP&A workflows easier to learn, test, and use.

Built With MeDo

This project was built using MeDo, which helped turn a complex FP&A product idea into a working browser-based application.

MeDo was used to rapidly generate, test, refine, and improve the app across multiple development cycles. The project went through many iterations, including building the dashboard, planning modules, editable grids, upload system, template library, Demo Mode, Live Mode, financial statement logic, exports, and AI Finance Copilot support.

One of the biggest advantages of using MeDo was the ability to move quickly from idea to working prototype, then continue improving the app through testing and issue fixing. Instead of only describing the product, I could build and refine actual workflows.

MeDo helped with:

Generating the initial app structure
Building the FP&A module layout
Creating dashboards and planning pages
Adding Demo Mode and Live Mode
Creating editable planning grids
Implementing CSV templates and upload flows
Adding local browser persistence
Building financial statement pages
Improving exports and validation
Iterating through bugs and fixes quickly

The project also showed that even with AI-assisted building, serious testing is still necessary. Page loading is not enough. A finance app must prove that inputs produce correct outputs.

What It Does

FP&A Command Center AI Ultra helps users move from financial inputs to planning outputs.

The app includes:

Executive Dashboard
Demo Mode with sample FP&A data
Live Mode for user-entered or uploaded data
CSV upload and mapping
Template Library
Revenue Planning
COGS Planning
OPEX Planning
Headcount Planning
CAPEX Planning
Working Capital Planning
Budgeting
P&L Statement
Balance Sheet
3-Statement Model
Forecasting
Variance Analysis
Data Quality and Anomaly Center
AI Finance Copilot support
Export functionality
Browser-based local persistence

Demo Mode and Live Mode

One important design decision was separating Demo Mode and Live Mode.

Demo Mode allows users to explore the platform with sample financial data. It helps users understand dashboards, statements, forecasts, and analysis before using their own data.

Live Mode allows users to manually enter planning data or upload CSV files. Live Mode is designed to stay separate from Demo Mode so users know whether they are viewing sample data or their own data.

This matters because finance tools must be honest about data source.

Upload and Template System

The app includes templates for structured FP&A inputs.

Templates include:

Company Setup
Planning Calendar
Chart of Accounts
Dimensions
Assumptions
Driver Library
Revenue Planning
COGS Planning
OPEX Planning
Headcount Planning
CAPEX Planning
Working Capital
Cash Flow
Balance Sheet
Budget vs Actual
Forecast
Trial Balance
General Ledger
KPI
Product Profitability
Customer Profitability
Scenario Assumptions
Workflow / Approval
Audit Trail Import
Report Input
AI Finance Copilot Prompt Template

The intended workflow is:

Choose the relevant template
Download the CSV
Fill or modify the sample data
Upload the file
Map and validate fields
Import into Live Mode
Review dashboard and statement outputs

Financial Logic

The app is built around core FP&A calculations.

Revenue Planning

Revenue = Units × Price × (1 - Discount %)

Example:

Units = 100
Price = 1,000
Discount = 0%

Revenue = 100 × 1,000 × (1 - 0%)
Revenue = 100,000

Gross Profit and Gross Margin

Gross Profit = Revenue - COGS

Gross Margin % = Gross Profit / Revenue × 100

Example:

Revenue = 100,000
COGS = 60,000

Gross Profit = 100,000 - 60,000
Gross Profit = 40,000

Gross Margin % = 40,000 / 100,000 × 100
Gross Margin % = 40%

EBITDA

EBITDA = Gross Profit - OPEX

Example:

Gross Profit = 40,000
OPEX = 25,000

EBITDA = 40,000 - 25,000
EBITDA = 15,000

EBIT and Net Profit

EBIT = EBITDA - Depreciation

Profit Before Tax = EBIT - Interest Expense

Tax = max(0, Profit Before Tax × Tax Rate)

Net Profit = Profit Before Tax - Tax

Working Capital

Working Capital = Receivables + Inventory - Payables

Example:

Receivables = 50,000
Inventory = 30,000
Payables = 20,000

Working Capital = 50,000 + 30,000 - 20,000
Working Capital = 60,000

Free Cash Flow

Free Cash Flow = Operating Cash Flow - CAPEX

Example:

Operating Cash Flow = 40,000
CAPEX = 10,000

Free Cash Flow = 40,000 - 10,000
Free Cash Flow = 30,000

Closing Cash

Closing Cash = Opening Cash + Operating Cash Flow - CAPEX + Financing Cash Flow

Example:

Opening Cash = 100,000
Operating Cash Flow = 40,000
CAPEX = 10,000
Financing Cash Flow = 0

Closing Cash = 100,000 + 40,000 - 10,000 + 0
Closing Cash = 130,000

Variance Analysis

Variance = Actual - Budget

Variance % = (Actual - Budget) / Budget × 100

Example:

Actual Revenue = 100,000
Budget Revenue = 120,000

Variance = 100,000 - 120,000
Variance = -20,000

Variance % = -20,000 / 120,000 × 100
Variance % = -16.67%

For revenue, a negative variance is unfavorable. For costs such as OPEX or COGS, a lower actual value can be favorable.

Forecasting

Naive Forecast = Last Actual Value

Moving Average = Average of recent periods

Weighted Forecast = Recent Month × 50% + Previous Month × 30% + Earlier Month × 20%

Example:

March Revenue = 140,000
February Revenue = 120,000
January Revenue = 100,000

Weighted Forecast = 140,000 × 50% + 120,000 × 30% + 100,000 × 20%
Weighted Forecast = 126,000

Anomaly Detection

The app flags unusual financial movements using threshold rules.

Examples:

Revenue drop greater than 20% = anomaly
OPEX increase greater than 15% = anomaly
CAPEX increase greater than 25% = anomaly
Gross margin drop greater than 5 percentage points = anomaly

AI Finance Copilot Support

The AI Finance Copilot section is designed to help users structure finance analysis and management commentary.

It supports use cases such as:

Variance explanation
CFO summary
Forecast commentary
Cash risk review
OPEX challenge
Revenue driver analysis
Board-pack narrative
Anomaly explanation
Scenario recommendation
Management action planning

The app also includes an AI Finance Copilot Prompt Template so users can structure finance questions clearly.

How I Built It

The project was built as a local-first browser app using MeDo.

The main design principles were:

Keep data local to the browser
Avoid login and backend complexity
Separate Demo Mode and Live Mode
Use templates for structured data input
Convert planning inputs into financial facts
Preserve dimensions such as product, customer, channel, entity, department, and account
Show outputs through dashboards, statements, forecasts, and exports
Make financial logic traceable

The core workflow is:

Input Data → Mapping → Validation → Financial Facts → Calculations → Dashboard / Reports / Exports

Challenges I Faced

The hardest part was not creating screens. The hardest part was making the workflows connected and honest.

Some of the key challenges were:

Preventing Demo Mode and Live Mode from mixing data
Making sure Live Mode does not silently show demo data
Ensuring uploaded dimensions such as product, customer, channel, department, and entity are preserved
Avoiding duplicate facts when users save or upload the same data repeatedly
Making validation specific to the selected upload type
Ensuring financial calculations match expected results
Making exports clearly show Demo or Live mode
Adding AI Finance Copilot support without overclaiming fake AI capability

This forced the project to move beyond page-load testing. A finance app should not only load pages. It must prove that inputs produce correct outputs.

Input → Processing → Output → Expected Result

What I Learned

This project made one thing very clear: FP&A software is mostly about data integrity, not dashboards.

A dashboard is useful only if the input data is mapped correctly, the calculations are traceable, and the output can be trusted.

I also learned that local-first apps can be powerful for finance learning and prototyping. Users can explore planning workflows without needing cloud infrastructure, login systems, or enterprise setup.

The most important lesson was that testing must be based on expected versus actual outputs, not only screenshots.

I also learned that MeDo can accelerate app creation significantly, but the builder still needs strong product thinking, detailed prompts, ruthless testing, and clear acceptance criteria.

Current Status

FP&A Command Center AI Ultra is being submitted as a Hackathon Release Candidate.

It is suitable for:

Hackathon demonstration
FP&A education
Lightweight financial analysis
Small-business planning
Consulting demos
Workflow prototyping

It is not positioned as a full enterprise EPM replacement.

Known Limitations

Some areas still need future improvement:

Some configuration templates may be download/manual-reference only
Some upload workflows may need additional parser hardening
Users should manually validate outputs before using them for high-stakes financial decisions
Enterprise features such as multi-user roles, approval workflows, ERP integration, and cloud database persistence are outside the current scope

Final Thought

FP&A Command Center AI Ultra is an attempt to make financial planning and analysis more accessible.

It combines templates, uploads, manual planning, financial statements, forecasting, variance analysis, exports, and AI-assisted finance thinking into one browser-based app.

The project is not trying to replace enterprise finance systems. It is trying to make the FP&A workflow easier to learn, test, and use.

Try the app here: FP&A Command Center AI Ultra

SupplyShock Sentinel

PRADEEP HEBBALLI — Tue, 12 May 2026 01:43:59 +0000

Building SupplyShock Sentinel with MeDo: An AI Control Tower for Supply Chain Risk

Supply chains usually fail quietly before they fail visibly.

A supplier delay starts as a small update. Inventory drops below a safe level. A Tier-2 supplier gets disrupted. A purchase order moves from “confirmed” to “delayed.” By the time the business notices, the problem may already have turned into a customer delivery failure.

That is the problem I wanted to solve with SupplyShock Sentinel.

Live app: https://app-bfiq7qjaez9d.appmedo.com/

What is SupplyShock Sentinel?

SupplyShock Sentinel is an AI-powered supply-chain risk analysis and decision-support platform for small and mid-sized businesses.

It helps users answer one simple but important question:

What will fail first, why will it fail, and what should we do now?

The app allows users to upload supply-chain data, validate it, analyze it, simulate disruptions, and generate recovery actions.

Users can either:

try built-in demo data
upload their own CSV files

Supported data types include:

Purchase Orders
Suppliers
Inventory
Supplier Relationships
Alerts

Why I built it

Many small and mid-sized businesses still manage supply-chain operations using Excel, email, WhatsApp, and manual follow-ups.

That creates major blind spots:

supplier delays are discovered too late
inventory stockouts are not predicted early
supplier reliability is not quantified
Tier-2 and Tier-3 dependencies are invisible
teams do not know what to fix first
financial exposure is not estimated before disruption

Most enterprise supply-chain control towers are expensive and complex. I wanted to build a lighter, accessible version that a smaller business could understand and use quickly.

How MeDo helped

I built the app using MeDo, an AI-powered app-building platform that helps generate full-stack web applications through natural language prompts.

MeDo made the process much faster because I did not need to manually build every page, route, form, table, and dashboard from scratch.

Instead, I could describe the app as a product:

what pages it needed
what data structures it should use
what the upload flow should look like
how risk scoring should work
how dashboards should react
how users should edit data
how what-if scenarios should work

Then I could test, find gaps, and ask MeDo to improve specific parts.

The biggest benefit was iteration speed.

The app started as a dashboard idea. After testing, I realized that a dashboard alone was not enough. Users needed a clear upload workflow. So I used MeDo to add:

CSV template downloads
required header explanations
upload preview
row-level validation
import controls
reactive dashboard updates
data editing
what-if scenario testing
Help and About pages

MeDo helped turn the project from a demo into a usable product experience.

Core workflow

The app follows this journey:

Open the app.
Choose Try Demo Data or Upload Your Own Data.
Review required CSV formats.
Download sample templates.
Upload supply-chain data.
Preview and validate uploaded rows.
Confirm import.
Run Sentinel Analysis.
View dashboards and alerts.
Edit data or run what-if scenarios.
Ask AI Copilot what to fix first.
Download risk-scored CSV files and reports.

This was important because a first-time visitor should not need external explanation to understand the app.

Risk scoring logic

The app uses an explainable risk scoring model.


text
Risk Score = Inventory Risk + Supplier Risk + Delay Risk + Priority Risk + Status Risk + Deep-Tier Risk

DEV Community: PRADEEP HEBBALLI

Semantic Paradox

I Built Semantic Paradox with MeDo — A Fast Brain Game That Fights Your Instincts

What I built

Why I built it

How the gameplay works

Decision Doppelgänger

Decision Doppelgänger – Meet Your Possible Futures

Elevator Pitch

Project Link

Category

About the Project

What It Does

SafeReach AI

SafeReach AI – Global Scam & Crisis Safety Assistant

Elevator Pitch

Project Link

Category

About the Project

What It Does

Inspiration

How I Built It

What I Learned

Challenges I Faced

Built With

Key Features

Live Safety Mode

Demo Mode

Guided Safety Chat

Crisis Navigator

Senior Mode

Emergency Wallet Card

Privacy-Safe Analytics

What’s Next

Final Summary

XenoLexis

XenoLexis – The Xenolinguistics Lab

Elevator Pitch

Project Link

Category

About the Project

What It Does

Inspiration

How I Built It

What I Learned

Challenges I Faced

Built With

What’s Next

Final Summary

FP&A Command Center

FP&A Command Center AI Ultra

Elevator Pitch

App Link

Inspiration

Built With MeDo

What It Does

Demo Mode and Live Mode

Upload and Template System

Financial Logic

Revenue Planning

Gross Profit and Gross Margin

EBITDA

EBIT and Net Profit

Working Capital

Free Cash Flow

Closing Cash

Variance Analysis

Forecasting

Anomaly Detection

AI Finance Copilot Support

How I Built It

Challenges I Faced

What I Learned

Current Status

Known Limitations

Final Thought

SupplyShock Sentinel

Building SupplyShock Sentinel with MeDo: An AI Control Tower for Supply Chain Risk

What is SupplyShock Sentinel?

Why I built it