She Used FigJam to Plan Her Outfits. Then She Found Elara.

The Problem

Meenal Pandey has loved fashion for as long as she can remember. Not in a passive, Pinterest-board kind of way. She was the person who actively hunted for unique pieces, experimented with reusing what she owned, and thought hard about how to get the most out of her wardrobe. For Meenal, style was never just an afterthought. Living in New York City, working as an AI engineer at Google, it was part of how she showed up every day.

But somewhere along the way, her wardrobe had gotten away from her. She owned a lot. Too much, really. And the problem wasn't that she didn't like what she had. It was that she couldn't figure out what she had. Pieces she loved sat buried and forgotten. When she couldn't visualise an outfit, she'd go buy something new instead of figuring out what she already owned. That cycle kept repeating: buy more, have less space, feel more overwhelmed, buy more again. Her spending kept climbing. Her closet kept growing. And every morning turned into a 20 to 25 minute standoff with a chaotic wardrobe that had everything and nothing at the same time.

She tried to solve this herself. Meenal started photographing individual pieces and dragging them into FigJam, Figma's collaborative canvas tool, arranging tops, bottoms, and shoes side by side to see if combinations worked. For a designer or engineer, it's a logical solution. But it was also a sign that no tool built specifically for this problem was good enough to use.

"I was literally using FigJam to plan my outfits. I'd photograph each piece, drop them onto a canvas, and manually arrange them just to see if they went together. It worked, but it was exhausting."

What She Tried Before

Meenal wasn't starting from zero when she came to Elara. She had already looked for solutions. She tried dedicated wardrobe apps, she tried AI tools she already trusted, and nothing stuck.

How the alternatives fell short
Alta - Not robust enough. The feature set felt limited and the experience wasn't built for someone with a real, complex wardrobe.
Whering - Tried it, but the UX felt clunky and the recommendations weren't personal enough to actually change how she dressed.
ChatGPT / Claude - Powerful tools, but not built for this. No wardrobe context, no visual understanding of her pieces, no memory of her style over time. The effort required to get a useful output was too high.
Elara - Robust wardrobe management, personalised recommendations, calendar-aware daily outfits, and a conversational interface that actually understands fashion.

She's an AI engineer. She knows what these tools can and cannot do. Her verdict was clear: the ease, the convenience, and the quality of recommendations Elara generates, no other platform came close.

Finding Elara

Meenal came across Elara through one of our founder's Instagram videos. The concept was immediately obvious to her: upload what you own, and Elara builds outfits from it, learns your style, and becomes your personal stylist over time. For someone already doing that manually in a design tool, this was worth trying.

Getting Started

She'll be the first to tell you: the setup takes some effort. Getting your wardrobe into Elara is not instant. You're uploading your pieces, letting the app catalog and analyse each one, building the foundation that everything else runs on. It asks for a few days of patience upfront.

"Yes, there's an initial setup. It takes some effort to get everything in. But once it's done, you realise just how powerful the tool is. Everything after that clicks into place, and you wonder how you ever managed your wardrobe without it."

That investment pays off fast. The moment the wardrobe is set up, Elara starts working. And the quality of what it returns makes the setup feel completely worth it.

What Elara Does That Nothing Else Did

The first thing Meenal noticed was how easy it was to get her wardrobe in. She could upload a single full-body photo wearing an outfit and Elara would automatically detect and catalog every piece from the image individually. Top, bottom, shoes, accessories, all pulled out and stored separately with a detailed breakdown: fit, style, colour, fabric, brand, and more. No other app she tried did this. Most expected her to upload each piece one by one. Elara let her dump everything in naturally, the way she actually takes photos.

Once her wardrobe was set up, the experience shifted. Instead of opening a closet and staring at it, she started opening Elara and typing. "Give me something for a client meeting but still comfortable." "I want a casual Saturday look that doesn't repeat what I wore last weekend." Elara would pull from what she owned, understand the context, and put together outfits that actually worked. When she needed something new, it would pull from her favourite stores, Zara and H&M, without her needing to open those apps, add filters, or scroll through hundreds of results. She just described what she wanted and got it.

There was also the Style Graph. In the background, Elara was quietly tracking every reaction she had, every outfit she liked or dismissed, every preference she mentioned in conversation. Over time, the recommendations got sharper. Not because she filled out a quiz, but because Elara paid attention. It learned what she actually wore, not just what she said she liked.

And the daily outfit feature changed her mornings entirely. Elara checks her calendar, sees what's happening that day, and generates an outfit recommendation every morning as a notification. She opens the app, sees the look, and she's done. No more standing in front of the wardrobe for 20 minutes trying to piece something together before work.

Where She Is Now

Meenal still loves fashion as much as she always did. The difference is that now her wardrobe actually works for her. She knows what she owns. She knows what goes with what. She gets dressed quickly, confidently, and without the Sunday night dread of figuring out what to wear all week.

She went from running outfit planning sessions in a design tool to opening an app and typing a sentence. That is the whole difference. And for someone who was already spending serious time and energy trying to solve this problem manually, Elara didn't just save her effort. It gave her back the part of fashion she actually loved.