A note for Hemant

I pulled their whole catalogue apart. Then I rebuilt the store around what I found.

Every product, price, photograph and word on this page is theirs, live from freshstartfurniture.co. What I added is the layer underneath: a structured attribute pass over all 160 listings, which is what makes the room builder and the trade-in desk possible at all.

It runs. The appraisal is a real vision model call on Cloudflare Workers AI, not a mock. Open the source, it is one file and one function.

Anirudh · AMB · aimybusiness.in
Same-Day Local Delivery  |  Call us (737) 406-4097  |  We Offer Cash for Your Furniture!
Austin, Texas · refurbished in house · one of everything

One hundred and five thousand dollars of furniture. Thirty two thousand to take it home.

Every piece here is a single object we restored ourselves, photographed where it stands, and will never have again. When it goes, it is gone.

The floor, right now

Shop by what actually matters

Their own site offers Shop by Room, Budget and Colour. None of it worked, because none of it was in the data. It is now, along with material, style, and whether the thing fits through your door.

The thing a resale store cannot do with filters

Furnish a room from what is on the floor tonight

Nobody walks in wanting "a sofa". They are moving into a one bed in North Austin on Saturday with $1,200. Only the person standing in the warehouse can answer that. Now the site can.

$1,200
Set a budget and a doorway, then build. It solves against the 157 pieces in stock right now, not a catalogue.
Live · Cloudflare Workers AI · llama-4-scout vision

Photograph your old furniture. Get a number in seconds.

You already buy furniture from the public, and you already say you are the only store where someone can trade in and get credit. Today that runs on a form and a phone call. Here it runs on a photograph, priced against your own comparable stock.

The photograph submitted for appraisal
The model identifies the piece, then your own catalogue prices it. Nothing about the offer is guessed by the model.
Why we sent you this

Nothing above is a mockup. Every number came out of your own store.

We pulled your product feed, your collections and your homepage on 29 and 30 July 2026, then verified every claim below against that data. Here is what we found and what we did with it.

The pattern behind all of it. Your inventory is one of one. Every listing needs its own copy, its own tags, its own photographs, forever, and none of that labour ever amortises. That is what caps how fast you can grow, not traffic.

So the fix is not a prettier storefront. It is a pass that turns a photograph into a finished listing, and an attribute layer that lets the same 160 pieces actually be found and combined. Both are running on this page.

What is not real yet The trade-in offer band is modelled at 25 to 40 percent of the resale price your own comparable stock implies. Those percentages are our assumption, not your margin, and they are the first thing we would replace with your real numbers.

Cart and checkout are demonstration only, nothing is charged.

The attribute layer was generated once, offline, by a model pass over your existing descriptions. 147 of 160 alt texts were written that way. 13 listings had no copy to work from, which is exactly the gap the live pass closes.

Product photographs and the video are hotlinked live from your Shopify CDN. They are yours and they can change.