Product recommendation AI that understands how furniture is chosen
'Customers also viewed' widgets recommend by click patterns — but furniture is bought by constraints: the wall is 96 inches, the dog sheds, the budget is $3,000, and it needs to arrive before Thanksgiving.
ShowroomOps recommends the way a good designer does: it gets the constraints first, searches your live catalog against them, and presents a short list of pieces that actually fit — with an honest recap of the trade-offs.
Free · a 30-minute walkthrough with the operator who built it, not a sales rep
Store Concierge
Answers from your catalog, policies, and floor inventory
Hartley Performance Sectional
112" · L-shape
$3,295 $4,695
In stock — on the showroom floor
See productMarlow Chaise Sectional
108" · reversible chaise
$2,850
In stock — ships in 5–7 days
See productEverett Track-Arm Sofa + Ottoman
104" combined
$2,390 $3,190
Custom fabrics — 4–6 weeks
See productThe real problem
Why furniture recommendations usually miss
Recommendation tech built for fashion and gadgets breaks on furniture's physics: size, material, configuration, and lead time aren't preferences — they're constraints.
Click-based similarity ignores fit
Two sofas can look identical in a grid and differ by 14 inches. A recommendation that doesn't know the shopper's dimensions is decoration, not selling.
Filters can't hold a real brief
'Performance fabric, seats five, fits a 110-inch wall, under $3,500, here before the holidays' is a normal furniture brief. No filter sidebar expresses it — so shoppers give up and leave.
Availability is an afterthought
Recommending a 16-week custom order to someone who needs a sofa this month wastes the shopper's trust. Lead time is a first-class constraint in furniture, and most engines ignore it.
No recap, no reasoning
A grid of eight 'similar items' makes the shopper do the comparison work. A good associate presents three options and says which one fits best and why. That recap is where the sale happens.
What we install
Recommendations built like a design consult
Constraint-first search over your live catalog, presented cards-first with a concise recommendation — the pattern we proved on our own floor.
Conversational constraint capture
The concierge draws out size, material, room, budget, and timeline in plain conversation — no forms, no 20-field filter panel.
Live catalog search with hard constraints
Dimensions, fabric properties, price, and current availability are enforced, not suggested. If nothing truly fits, it says so and shows the nearest options honestly.
Cards first, recap second
Two or three product cards with image, price, sale strikethrough, and stock status — then a short recap recommending the best fit and naming the trade-offs.
Showroom and designer escalation
When the brief deserves human taste — full-room projects, custom upholstery — the concierge books a design appointment or routes to the showroom instead of over-promising.
Full module walkthrough on the product page.
First implementation
Built and proven inside a real furniture store
We tuned this recommendation pattern on our own store's traffic — cards first, recap second, honest availability — before offering it to other retailers.
ShowroomOps was built inside our own high-end furniture store — one large destination showroom, a deep new-and-consignment catalog, and a sales floor that closes most of the revenue. Live catalog, real policies, real customers. We share the store's identity and full detail privately, on the teardown call.
- outsold in one, at pace
- 5 yrs
- 2026 store revenue is pacing past the previous five years of sales combined.
- June revenue, year over year
- 4.05×
- $628K in June 2026, with July projected near $765K.
- chat-assisted revenue in 14 days
- $27.5K
- Orders matched to concierge conversations after auto-answer went live — chat volume roughly doubled, and matched revenue ran at a ~$60K/month pace.
How we count: We report the store's numbers as the store's numbers and assisted revenue as matched orders — never claimed lift from any single tool. The growth came from the whole operating stack working together: buying, marketing, catalog, concierge, and measurement. We'd rather under-claim and show you the methodology.
Read the full case study →Engagement
How retailers work with us
Recommendation quality depends on catalog data quality — implementation includes structuring your dimensions, fabric, and lead-time data so the engine has something true to search. Full pricing detail here.
Custom
Done for youfrom $75K setup
+ $15K/month
We build and run the whole operating stack for you — scoped per engagement, scaling up for multi-location and multi-brand operators.
Self-Setup System
You implement it$25K one-time
+ $500/month software
The same system we run in our own store, handed to your team to implement in logical order with our complete self-setup guide.
The 90-day launch guarantee
“If we do not launch your concierge, ingest your catalog and policies, train it on your top questions, and generate the agreed qualified sales conversations or showroom/design leads within 90 days, we work free until we do, up to two additional months.”
This is an implementation guarantee, not a revenue guarantee — nobody can honestly guarantee revenue in a seasonal, omnichannel business. What we can guarantee is that the system gets built, launched, and producing the agreed conversations and leads, or you stop paying the retainer while we finish the job.
The launch guarantee applies to Custom, done-for-you engagements. The Self-Setup System is a one-time purchase your team implements with the guide.
Questions
Frequently asked
How is this different from a 'related products' widget?
Related-products widgets rank by click similarity and show up at the bottom of a PDP. This is an interactive concierge that captures a shopper's actual constraints — dimensions, fabric needs, budget, timeline — and searches your live catalog against them, then explains its recommendation. One is decoration; the other is a sales conversation.
What catalog data does it need?
At minimum: your product feed with pricing, variants, and inventory. Recommendation quality improves sharply with structured dimensions, fabric/material attributes, and lead times — and implementation includes enriching those fields where your catalog is thin. That data work also improves your PDPs and ad feeds as a side effect.
What if nothing in our catalog matches the request?
The concierge says so — and shows the closest real options with the trade-off named ('4 inches longer than your wall' or 'arrives in January, not November'). It can also route to a designer or your custom-order program. Pretending a near-miss is a match is how you lose furniture customers.
Does it recommend across categories — rugs, tables, lighting?
Yes. Once a shopper's room and style context exists in the conversation, the concierge can suggest coordinating pieces from your catalog. It's attach-rate selling done the way a designer does it, not a 'frequently bought together' guess.
Can we control what it recommends first?
You can set merchandising priorities — floor models to move, house brands, margin tiers — and the concierge respects them within the shopper's constraints. What it won't do is recommend something that fails the brief just because it's promoted. Trust is the asset.
Watch it work a real brief against your catalog
In your private teardown, we'll run genuine shopper briefs against your current site — and show you what a constraint-first recommendation engine would have answered instead.
The teardown is free and delivered live on a 30-minute call by the operator who built the system. We onboard a small number of retailers at a time — each system is built from real catalog and policy data.