Help shoppers decide with the data you already own.
Your product already knows the catalog, stock, orders, and policy. We turn that context into guidance shoppers can use, with the source visible and the merchant in control.
- Commerce infrastructure
- Online marketplaces
- Product discovery
- Returns and retention
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Lightest matchTrail Lite Anorak
210 g, water resistant
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Not fully waterproofSummit Hardshell
420 g, 3-layer
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Heavier, warmer
Find a product
Three points where commerce products lose the shopper.
- 01
Search does not match how shoppers speak.
A request the catalog could answer returns no result, or sends the shopper to the wrong shelf.
- Combine keyword and meaning
- Turn intent into usable filters
- Recover empty results with close matches
46%
of desktop ecommerce sites receive a mediocre or worse search rating
Baymard, 2026 - 02
Product data leaves simple questions open.
A missing specification makes the shopper leave, guess at purchase or send the item back.
- Extract attributes from text and images
- Send uncertain values for review
- Publish clean feeds by channel
10%
of the largest ecommerce sites do not provide consistently detailed product descriptions
Baymard, 2021 - 03
Returns default to refunds.
The exchange path is hidden, while unusual or risky requests wait in the same slow queue.
- Suggest an exchange from the reason
- Read policy and order together
- Send unusual cases to a person
19.3%
of online sales expected to come back as returns in 2025
NRF and Happy Returns, 2025
An AI layer for commerce products and operations.
Established product teams and commerce companies bring us a specific break in discovery, purchase or retention. We connect the layer to the catalog, orders and customer systems already in use.
For teams that build commerce software
Commerce platforms
- Admin assistant for order work
- Product copy drafts from source data
- Catalog questions in plain language
- Storefront help through checkout
Search and product discovery
- Keyword and vector retrieval
- Shopping assistant with constraints
- Answers grounded in product data
- Reports on shopper questions
Merchandising and recommendations
- Rules written in plain language
- Recommendations within guardrails
- Outfit ideas for a real occasion
- Gift finder with reviewable logic
Catalog and product content
- Attributes from images and copy
- Consistent image tags
- Background and variant image briefs
- Listing draft from one source image
Returns and post-purchase
- Exchange options from the reason
- Risk check at return request
- Decisions for delivery claims
- Size guidance from prior returns
Support and customer reviews
- Support agent with order actions
- Pre-sale product guidance
- Themes from customer feedback
- Review summaries for approval
For teams that run a commerce business
Brands, manufacturers and distributors selling direct or wholesale. We help the store answer better questions and keep more completed orders moving after purchase.
- 01
Turn emailed trade orders into checked carts.
Read orders from email, PDF or spreadsheet into a structured cart. Price, stock and credit still come from the ERP, not from the model.
Talk to us about this - 02
Make wholesale reorders easy to review.
Show each buyer their pricing, case packs, credit and stock, then offer a saved order, CSV import or a replacement when a line is unavailable.
Talk to us about this - 03
Offer the exchange before the refund.
Use the return reason to suggest the right size, color or compatible item from live stock, with margin rules and a person for exceptions.
Talk to us about this - 04
Give subscribers a useful reason to stay.
Offer pause, skip, swap or cadence changes based on why the customer is leaving, with a direct fix for an expired payment method.
Talk to us about this
Four rules for a trustworthy commerce layer.
- 01
Recommend what can ship today.
The result respects current stock, price and merchandising rules before it reaches a shopper.
- 02
Let the merchant approve the change.
Generated copy, rules and review summaries wait for a person to check them before publication.
- 03
Flag uncertainty instead of guessing.
An unclear attribute goes to review with its source rather than appearing as a product fact.
- 04
Offer an exchange, then route the exception.
The return reason shapes the next option, and unusual cases reach the team with context.
Where this industry usually starts
AI Experience (AX) Audit
Search and recommendation features can look active while shoppers still leave or return the wrong item. We audit the path from question to order and choose a baseline such as search conversion or exchange rate.
AI Product & UX Design
Commerce guidance has to respect live stock, price, fit, and policy. We make the recommendation explainable, reviewable, and useful at the moment a shopper or merchant decides.
AI Discovery (AEO) & Growth
If shoppers ask assistants where to find a product, your catalog and policies need to be readable and well sourced. We improve the public discovery path and connect it back to the relevant product or store.
Commerce demand changes by season and hour.
The product has to answer on a quiet Tuesday, during a sale and through the returns wave after a holiday. We design for those patterns and the systems behind them.
A season of store traffic
- 1
Sale peaks
A flash sale or Black Friday sends search, cart and stock checks up within the same hour.
- 2
Holiday peaks
Gift searches build for weeks, then the January return wave gives every order a new decision.
- 3
Product launch peaks
A drop puts many shoppers on one product page, asking variations of the same question.
- ~
Quiet weeks
Most weeks are calmer, so average traffic is a weak basis for sizing the experience.
The layer sits above the commerce stack.
Your catalog, order record and customer systems remain the source of truth. The AI layer turns them into a clearer shopping or operating step.
Storefront and commerce
Shopify, BigCommerce
Enterprise commerce
Salesforce Commerce Cloud
Product information
Akeneo, Salsify
Payment processing
Stripe
Product search
Algolia
Customer support
Gorgias, Zendesk
Return operations
Loop, Narvar
Customer messaging
Klaviyo
The questions that decide it.
What does it cost to add AI search or shopping help?
We start with the AI Experience Audit, a fixed price two week sprint. It produces a fixed build price, and the audit fee is credited to that build. Current numbers are on the pricing page.
Should we buy an AI engine or build the shopping experience?
Can you work with our existing commerce platform and tools?
How do we stop the assistant from inventing a return policy?
Will generated product copy create search problems?
How can our catalog appear in AI shopping conversations?
Which metric should we watch after the commerce feature launches?
Show us where the commerce journey loses momentum.
One call, 20 minutes. We will identify the missing product layer, the data it needs and the metric that would show whether it helped.
- NDA from the first conversation.
- Response within 24 hours, guaranteed.
- Founder-led from first call to handoff.
