SIE for Commerce software teams
Product data arrives messy. Your catalog agent still has to place it correctly.
SIE gives teams building commerce software one API for text search, image search, reranking and structured extraction. Combine listing copy with product images, map each item to your taxonomy and return evidence your catalog team can inspect.
Self-host SIE in your infrastructure, or ask about managed deployment through SIE Cloud.
Seller copy, product image and candidate Shopify taxonomy paths
The result returns a ranked path, normalized attributes and the evidence used to choose it.
The operational problem
The model call is only one part of the job.
Seller feeds do not arrive in your taxonomy. Titles are inconsistent, attributes go missing and the image often contains the detail that the copy leaves out.
01
Seller language does not match your catalog
A listing can describe the right product with words that never appear in the category path.
02
Copy alone misses visible evidence
Shape, fittings, colour and packaging can change the correct branch of the taxonomy.
03
Each model creates another service
Text retrieval, visual matching and extraction often ship as separate stacks with separate operating work.
04
A wrong path spreads downstream
Bad classification affects filters, recommendations, analytics and the product page a shopper finally sees.
How SIE helps
One catalog agent, one inference surface
Your application controls the workflow. SIE runs the open source models behind each step and returns typed results your catalog service can use.
Category path, normalized fields, scores and a review route when confidence is low.
Where it fits
Use SIE inside the product your team is already building.
Catalog onboarding
Place new supplier listings into an existing taxonomy.
Product discovery
Search across titles, descriptions and images from one request.
Attribute normalization
Turn seller wording into the fields your filters and feeds expect.
Review queues
Send uncertain or conflicting listings to a catalog specialist.
Representative task benchmark · Rerank
Price, quality and speed for catalog reranking
One measured model job from this workflow. These figures come from SIE task evaluations, not from the industry example on this page.
499 Federal Register questions with 20 candidates each, under two relevance rules. A question is right when the requested document ranks first under both rules.
Recorded example
The image corrects a copy-only catalog error
The existing SIE example uses a labelled Shopify product record. The same listing is ranked with copy alone, then with its image and copy together.
Seller copy and a product photograph are checked against the catalog taxonomy.
Each step uses the model suited to that job through the same inference engine.
The agent returns the chosen branch and keeps the comparison available for review.
Deployment
Run SIE where your data can go.
The workflow uses the same SIE API whether you choose managed infrastructure or run it under your control.
Use SIE Cloud
Let the SIE team operate model serving, scaling and updates. Talk to us about availability and the right setup for your workload.
Get startedRun SIE in your infrastructure
Keep models and data inside your environment. Your team controls hardware, networking, access and deployment policy.
View the deployment guide