Forem: Suman Gorai

How I Reduced eCommerce Return Rates by 40% with AI Virtual Try-On (No Backend Needed)

Suman Gorai — Wed, 22 Apr 2026 03:03:53 +0000

If you run an online fashion store, you already know the pain.

A customer orders a dress. It arrives. Doesn't fit right.
Back it comes. You lose money on shipping both ways, the item
goes back into inventory (sometimes damaged), and the customer
might not come back.

The average fashion return rate is 30–40%. And the #1 reason?
Shoppers couldn't visualize how the item would look on them.

I've been building Torziva — an AI-powered
virtual try-on tool that lets shoppers see themselves wearing a
product before they buy. No app download, no redirect, no backend
required. Just one script tag on your product page.

Here's what I learned building it — and the numbers that came out.

The Problem Nobody Talks About

Most eCommerce conversion advice focuses on checkout optimization,
faster loading, better copy. All valid.

But nobody talks about pre-purchase uncertainty — the moment
a shopper thinks "does this actually look good on me?" and either:

Buys it anyway and returns it later
Abandons the cart entirely

Both outcomes cost you money. The first costs more.

What Virtual Try-On Actually Does

The flow is simple:

Shopper lands on a product page
Clicks "Try it on"
Uploads a photo (or takes one)
AI generates a realistic image of them wearing that product
They see it. They buy it. With confidence.

No redirect. No separate app. Everything happens inline on your
product page.

The AI model we use processes the image in under 10 seconds and
returns a photorealistic result. The shopper never leaves your store.

The Technical Side (Kept Simple)

Here's how it works under the hood:

Frontend: A lightweight script tag embeds the try-on widget
on any product page.

<script src="https://torziva.site/sdk.js?key=pk_live_..."></script>

Backend: When a shopper uploads their photo + selects a product,
the request goes to our API which calls the AI model asynchronously.

AI Model: We use fal-ai/fashn/tryon/v1.5 — one of the most
accurate garment try-on models available right now.

Result delivery: We use a queue-based pattern (not synchronous
polling) so the shopper gets a smooth experience without timeouts.

The whole thing works on Shopify, WooCommerce, or any custom site.
No backend changes needed on the merchant's side.

What the Numbers Look Like

After rolling this out across stores using Torziva:

Metric	Before	After
Return rate	~32%	~19%
Add-to-cart rate	baseline	+2.3×
Cart abandonment	high	measurably lower

The biggest impact wasn't conversion rate — it was return reduction.
That's where the real money is saved.

What I Got Wrong First

Mistake 1: Synchronous API calls

My first version polled the AI model synchronously. Fine for testing,
terrible in production — requests timed out after 30 seconds on
Railway/Vercel.

Fix: Switched to async queue pattern. Submit job → get job ID →
poll for result → return to frontend. Much more reliable.

Mistake 2: Storing customer photos

Early version stored uploaded photos in Supabase storage "just in case."
Bad idea for GDPR and unnecessary storage costs.

Fix: Process and discard. Photos never hit permanent storage.

Mistake 3: Trying to make it a Shopify-only tool

The script-based approach means it works on literally any website.
Limiting to Shopify was leaving 70% of the market on the table.

How to Add Virtual Try-On to Your Store Today

If you want to try this on your own store:

Go to torziva.site
Sign up free — no credit card
Get your script tag from the dashboard
Paste it on your product pages
Done — live in under 5 minutes

Free plan includes 50 try-ons per month. Enough to test and
see real results before committing.

What's Next

Currently working on:

Shoes and accessories support (garment model doesn't handle these yet)
Analytics dashboard showing try-on → purchase correlation
Zapier/Make integration via Torziva Sync

If you're building something similar or have questions about the
AI model setup, drop a comment. Happy to go deeper on any part
of this.

Built with: React, Supabase, fal.ai, Railway, Vite
Live at: torziva.site

Building Torziva API in Public — Making Apps Interactive 🚀

Suman Gorai — Fri, 17 Apr 2026 05:57:54 +0000

Most apps today feel… static.

Users scroll. Click. Leave.

There’s very little real interaction.

As a developer, I kept noticing this problem again and again —

no matter the industry.

So I decided to build something different.

💡 What is Torziva API?

Torziva API is a platform that helps developers add real-time interactive experiences into any kind of application.

Not limited to one niche.

Not limited to one use case.

From virtual try-on to dynamic UI interactions —

the goal is simple:

Make apps feel alive.

⚡ Why I Started This

At 19, I wanted to challenge myself to build something meaningful.

Not just another project.

But something developers can actually use in real-world apps.

I realized:

Most apps lack engagement
Adding interactive features is complex
Developers need simpler tools

That’s where Torziva API comes in.

🛠️ What I'm Building

With Torziva API, developers will be able to:

Add interactive experiences with simple API calls
Improve user engagement
Build more dynamic applications
Experiment beyond traditional UI

And this is just the beginning.

🌍 Who is This For?

Developers
Freelancers
SaaS builders
Startups

Basically, anyone who wants to build better user experiences.

🚀 Launch Plan

I’m building this in public and sharing the journey step by step.

Public launch is planned for next month 👀

👉 Check it out here:

https://app.torziva.site

📈 Building in Public

I’ll be documenting:

Progress
Challenges
Wins & failures

If you're interested in APIs, startups, or building products —

follow along.

🔥 Final Thought

The future of apps isn’t static.

It’s interactive.

And I’m trying to build towards that.

If you have feedback, ideas, or just want to connect —

I’d love to hear from you 🙌

I Built an AI-Powered Virtual Try-On Tool for Shopify — Here’s How It Works (Torziva)

Suman Gorai — Tue, 24 Mar 2026 12:41:03 +0000

Online shopping is broken in one major way — you can’t try before you buy.

Customers constantly ask:

“Will this look good on me?”
“Will it fit my body?”
“Is this worth buying?”

This uncertainty leads to low conversions and high return rates.

So I decided to build something to solve this.

👉 Meet Torziva — an AI-powered virtual try-on tool for Shopify.

🚀 The Idea Behind Torziva

The goal was simple:

Let users upload their photo and instantly try clothes virtually.

Instead of imagining how a product might look, users can see it on themselves.

🧠 How It Works (User Flow)

Here’s the basic flow:

User visits a Shopify product page
Clicks on “Try On”
Uploads their photo
AI processes the image
Output: realistic try-on preview

This entire process takes just a few seconds.

⚙️ Tech Stack & Architecture

Building Torziva wasn’t easy. Here's a simplified version of the stack:

Frontend: Embedded Shopify UI (App Extension)
Backend: Node.js / API-based system
AI Processing: Image-to-image transformation models
Storage: Cloud-based image handling
Integration: Shopify App + API

🧩 The Core Challenge

The hardest part wasn’t coding.

It was:

👉 Making the output look realistic

Problems I faced:

Clothes not aligning with body
Lighting mismatch
Distorted outputs
Slow processing time

Solving these required:

Prompt tuning
Image preprocessing
Model selection & testing

⚡ Performance Optimization

Speed matters.

Users won’t wait 30 seconds just to try a t-shirt.

So I optimized:

Image size before processing
API response time
Queue handling

Goal: < 5 seconds result

💡 Why This Matters for Shopify Stores

Torziva helps store owners:

📈 Increase conversions
🔁 Reduce returns
😄 Improve user experience
🛍️ Boost engagement

This is especially powerful for:

Fashion brands
D2C stores
Custom clothing businesses

🔮 What’s Next

I’m currently working on:

Multi-angle try-on
Video-based try-on
Better realism with advanced models
Mobile optimization

🙌 Final Thoughts

Building Torziva has been an incredible journey.

From idea → prototype → working SaaS, I learned:

AI is powerful, but not plug-and-play
UX matters more than tech
Speed is everything

If you’re building in AI + e-commerce, this space is just getting started.

🔗 Try Torziva

If you’re curious, you can check it out here:

👉 https://www.torziva.site/

I’d love your feedback 🙌

🏷️ Tags

#ai #shopify #saas #webdev #startup #machinelearning

I Tried to Build a Startup Without a Team --- Here's What Happened

Suman Gorai — Sat, 21 Mar 2026 04:25:59 +0000

🚀 Intro

Everyone talks about building startups with a team.\
But I wanted to try something different.

What if I build everything alone --- product, backend, frontend,
marketing... everything?

This is the story of what actually happened.

💡 The Idea

I wanted to build a simple SaaS product that helps freelancers create
invoices and get paid easily.

No big funding.\
No team.\
Just me, my laptop, and a lot of ideas.

⚙️ The Stack I Chose

To keep costs low, I used:

Next.js (Frontend)
Supabase (Backend + Auth + DB)
Vercel (Deployment)
PayPal (Payments)

Everything was either free or very low cost.

😵 The Reality Hit Me

Building alone sounds cool... until you actually do it.

I had to fix bugs and design UI
Handle backend logic and database issues
Think about marketing and user acquisition
Debug things at 2 AM with no one to help

There were days I felt completely stuck.

📉 Biggest Mistakes I Made

I started building without validating the idea\
I ignored marketing until the product was "perfect"\
I tried to do everything instead of focusing on what matters

These mistakes slowed me down a lot.

🔥 What Actually Worked

Keeping the product simple
Using free tools to reduce risk
Learning by building instead of overthinking
Sharing progress publicly

That's what kept me going.

🎯 Final Thoughts

Building a startup alone is possible... but it's not easy.

You'll feel stuck.\
You'll doubt yourself.\
But you'll also learn faster than ever.

If you're thinking of building something solo --- just start.

You'll figure things out along the way.

📢 CTA

If you're building something solo, I'd love to hear your story.

Drop it in the comments 👇

🚀 I Built an AI Virtual Try-On for eCommerce (To Fix a Huge Conversion Problem)

Suman Gorai — Thu, 19 Mar 2026 03:34:12 +0000

Hey devs 👋

I’ve been working on something interesting lately — an AI-powered virtual try-on tool for eCommerce stores.

🤔 The Problem

One thing I kept noticing while exploring online stores:

Customers hesitate to buy because they can’t visualize how a product will look on them.

This leads to:

❌ Low conversion rates
❌ High return rates
❌ Poor user confidence

And honestly… it makes sense.

💡 The Idea

So I thought:

What if users could instantly try products using AI, directly from the product page?

No apps. No friction.

Just click → try → decide.

🛠️ What I Built

An AI virtual try-on system that:

Lets users try products in real-time
Works directly inside eCommerce stores
Requires minimal integration
Focuses on speed and simplicity

⚙️ How It Works (Simple Overview)

User visits a product page
Clicks "Try Now"
AI processes the input
Shows a realistic preview

All within seconds.

🎯 Who It’s For

Shopify store owners
D2C brands
Fashion & accessory sellers
Anyone struggling with conversions

📈 Goal

The main goal is simple:

Help store owners increase conversions and reduce returns using AI.

🔥 What I Need From You

I’m still improving this, so I’d love feedback from the dev community 🙌

What features would you add?
Any ideas to make it more useful?
What would you improve technically?

🌐 Try It Out

If you’re curious, you can try it here:
👉 https://torziva.site

Thanks for reading!
Would love to hear your thoughts 🚀

How I Fixed a Silent 500 Error in My Supabase Edge Function

Suman Gorai — Wed, 18 Mar 2026 03:15:24 +0000

How I Fixed a 500 Error That Was Silently Killing My SaaS

I launched Torziva — an AI virtual try-on
tool for fashion stores — and users were getting 500 errors on
every try-on attempt.

The weird part? Supabase logs showed nothing. Completely blank.

The Symptom

Browser console: Failed to load resource: 500
Supabase edge function logs: blank
fal.ai dashboard: requests weren't even arriving

Classic "where is the request dying?" mystery.

The Investigation

Step 1 — Checked Vercel env vars

My edge function was hosted on Supabase but the request
originated from Vercel. Turned out FAL_API_KEY was missing
from Supabase secrets — it was only in Vercel.

Added it to Supabase → still failing.

Step 2 — Checked the actual error

Added more granular try/catch logging and finally saw it:

Error: Processing timeout. Please try again.

Step 3 — Found the real culprit

My polling loop was set to 60 attempts × 2000ms = 120 seconds.

Supabase edge functions have a default timeout of 60 seconds.

The function was dying before fal.ai even finished processing.

The Fix

Two changes in the edge function:

// Before
const maxAttempts = 60;
num_inference_steps: 30,

// After  
const maxAttempts = 25;  // 25 × 1500ms = ~37 seconds
num_inference_steps: 20, // faster generation

Also changed polling interval from 2000ms to 1500ms.

Total processing time dropped from ~90s to ~35s.
Now well within Supabase's limit.

The Lesson

If your Supabase edge function logs are blank, the function
isn't crashing — it's timing out before it can log anything.

Always check:

Are your secrets in Supabase, not just Vercel?
Is your total execution time under 60s?
Are external API calls (fal.ai, OpenAI, etc.) taking longer than expected?

Stack

Supabase Edge Functions (Deno)
fal.ai IDM-VTON for try-on generation
Next.js 14 + Vercel frontend

If you're building anything with fal.ai or Supabase edge
functions, happy to answer questions in the comments.

And if you run a fashion store — this is what powers
torziva.site 👕

How I built an AI virtual try-on button for fashion stores using fal.ai and Supabase

Suman Gorai — Tue, 17 Mar 2026 03:53:06 +0000

The problem I was trying to solve

Fashion ecommerce has a dirty secret — 30-40% of all purchases get
returned. The #1 reason? "It didn't look right on me."

Shoppers can't tell how a dress will fit their body from a flat
product photo. So they buy, try, and return.

I built Torziva to fix this.

What Torziva does

Torziva adds a "Try On" button to any fashion store's product page.
When a shopper clicks it:

They upload their photo
AI places the exact garment on their body
They see a realistic on-model photo — in under 30 seconds

Real fabric texture. Real fit. Real colors. On their actual physique.

The tech stack

Here's what I used to build it:

fal.ai — for the AI image generation / virtual try-on model
Supabase — auth, database, storage
Next.js 14 — frontend + API routes
Vercel — deployment

The core flow is simple:

User uploads photo → 
API sends to fal.ai with garment image → 
fal.ai returns generated image → 
Display result to user

Why fal.ai?

I evaluated a few image generation APIs. fal.ai won because:

Fast inference (~30 seconds for try-on)
Pay per generation ($0.075/image)
Good try-on model quality for fashion garments
Simple API, easy to integrate

The Supabase setup

I used Supabase for three things:

Auth — email + Google OAuth for store owners
Database — storing generation history, store configs
Storage — saving generated images

Supabase MCP made the setup surprisingly fast.
Connected directly from Cursor and scaffolded the schema in minutes.

Challenges I ran into

1. Garment detection quality
The AI works best with flat lay photos on white backgrounds.
Dark backgrounds or heavy shadows confuse the model.
Had to add input validation and guidance for store owners.

2. Shopify integration
Getting the Try On button to appear correctly on Shopify product
pages required careful theme injection. Shopify's script tag API
made this manageable but took longer than expected.

3. Pricing
At $0.075/generation, pricing the product correctly was tricky.
Had to account for Shopify's 15% fee on top.
Ended up with a usage-based model that scales with store size.

Results so far

Launched on Product Hunt on March 12
Listed on Peerlist, BetaList
First users onboarding now

Still early days but the feedback from fashion store owners has
been positive — especially the "no photoshoot needed" angle.

What's next

Full Shopify app (in review)
Support for more garment types
Bulk generation for store catalogs

If you're building anything in fashion tech or ecommerce AI,
would love to connect. And if you run a fashion store —
try it free at torziva.site.

Happy to answer any questions about the tech stack in the comments 👇