Search that understands what people mean.
Customers type “a warm light for a small reading nook,” not a product name. Neural Search uses embeddings and intent modeling to match meaning, not strings — and the same index is queryable by AI agents and answer engines.
The shift
Most search bars were built for the way people used to shop.
- 01
Customers don't search the way your catalog is named.
A shopper types “warm pendant for a small dining nook.” Your catalog says “Scandi-style brass ceiling lamp, 6-bulb, dimmable.” The right product exists. They never see it.
- 02
Slow search bars feel broken.
A two-second results page gets abandoned. Mobile, desktop, in-store kiosk — every device expects results before the user lets go of the keyboard.
- 03
Your search should get smarter, not stay flat.
Real customers click, save, abandon and buy — every action is signal. Without a system that learns from those signals, your search bar peaks on launch day and never improves.
- 04
Agents and humans need separate lanes.
If both hit the same search bar, agent traffic floods the behavioral data and the recommender starts ranking for bots instead of buyers. Agents query via API, humans via the bar.
Live demo
Three queries. Three ways it beats keyword search.
The same engine handles a natural-language intent query, a typo'd product search and a compound mood, material and room request.
- cozy
- reading
- armchair
- compact
- warm
- 94%#1
Boucle Swivel
Lounge · oat
- 91%#2
Hollis Lounge
Mid-century
- 88%#3
Loft Armchair
Compact · walnut
- 84%#4
Sling Chair
Tubular · leather
Read asskandi pendnt walnutscandi pendant walnut
- scandi
- pendant
- walnut
- warm wood
- 96%#1
Walnut Drum
Pendant · 18"
- 91%#2
Brass Cluster
Scandi · brass
- 88%#3
Matte Dome
Scandi · matte
- 84%#4
Globe Pendant
Oak ceiling rose
- dining
- warm
- brass
- amber
- accent
- 95%#1
Brass Chandelier
6-bulb · vintage
- 92%#2
Amber Globe
Hand-blown · single
- 89%#3
Forge Linear
Brass · 36"
- 82%#4
Warm LED Strip
Cabinet · concealed
How a query is answered
Is AI search even needed?
Usually not. Each query is read before the search runs and sent down the simplest path that fits it — so the model is there for the questions that need it, not every keystroke.
- 01
SKU lookup
Does it look like a SKU?
“PL-9-AB-3K”Straight to the exact product. - 02
Keyword search
Is it short, or just numbers?
“pendnt”Keyword match, typos forgiven. - 03
Hybrid search
Is it a real phrase?
“warm light over a long kitchen island”Keyword and meaning in one request, ranked together. - 04
Language model
Does it need understanding?
“dimmable, 9 ft island, aged brass to match my faucet, here by the 20th”Read into filters, then searched.
One path per query, chosen before it runs. Each step down does more work.
What's built in
Everything that ships with Neural Search.
Instant typo-tolerant search
Results even when shoppers misspell, abbreviate or mash words together. The bar never returns “no matches” when something close exists in your catalog.
Natural-language understanding
“Warm pendant for a dining nook” gets parsed for mood, material and room — not just keyword overlap. Customers type how they think, not how the catalog was indexed.
AI Design Assistant
A conversational layer that helps shoppers visualize the room, complete the look and discover items they didn't know to search for.
AI product recommendations
Personalized cross-sells, alternates and related items — tuned per customer per session by what they're actually doing, not what a generic rule says.
Behavior-trained ranking
Search, click, favorite, cart and purchase signals weighted into the model — each step a stronger vote than the last. The more your customers shop, the smarter the results get.
Priority product surfacing
Boost high-margin or high-inventory items into top results when relevance ties. The engine works for your business, not just for the click-through rate.
Cross-industry learning, per-account tuning
The recommender improves from patterns across the entire Neural network, while every account stays customized to its vertical, its catalog and its shoppers.
Detailed search reporting
See what shoppers search for, what converts and where the gaps are — so you can fill the product holes the search bar reveals.
Built for humans, kept clean of bots
Agents query the catalog through structured APIs, never the human search bar. Bot signals don't enter the training pipeline, so the recommender stays accurate to what real customers want.
Questions
Asked before you ask.
What makes Neural Search different from standard keyword search?
Keyword search matches the literal words in a query against the literal words in a product record. Neural Search uses embeddings to match meaning — so a query like “warm light for a reading nook” surfaces the right products even when none of them use those exact words.
Does it handle product attributes and specifications?
Yes. The semantic index is structured-attribute-aware, so queries that reference specs, finishes, dimensions or use-cases are scored against actual product data — not just free-text descriptions.
What happens to zero-results queries?
Neural Search reduces zero-results pages because it understands intent rather than requiring an exact string match. Queries that would have returned nothing instead surface the closest relevant products.
Can AI agents and answer engines query the index?
That's a first-class use case. The same semantic index that powers your storefront search is queryable by AI agents and answer engines.
Does it require perfectly clean product data?
No — Neural Search is tolerant of real-world data quality. That said, enriched, structured data from Data Enrichment makes the index sharper. The two layers are designed to work together.
How does it integrate with our existing storefront?
Neural Search runs as an API layer your storefront queries instead of — or alongside — your existing search. We scope the integration against your stack. No rip-and-replace required.
Keep exploring
More in Technology.
- Marketing InfrastructureFragmented catalogs and siloed systems become structured, agent-accessible architecture — the data layer that decides whether agents can find you.
- AI ShoppingConversational AI that knows your catalog, speaks your brand and guides shoppers from discovery to confident purchase — for humans and AI agents alike.
- PricingOne plan launches your storefront, adds your products and services, imports your customers and gets you paid — plus capacity and Neural Labs services when you want them.