Your catalog, ready to be asked — not just listed.
A queryable interface to your enriched catalog, built for AI assistants, agents and applications that need to reason about products: what fits, what's compatible, what actually satisfies what the shopper asked for.
Powered by the Semantic Commerce Engine + Brand Knowledge Engine — part of the Semantic Commerce Layer™
One question in. One grounded answer out. Any protocol.
Same answer, delivered in the shape each protocol expects.
Protocols define how agents exchange commerce data. The layer governs what they receive — one grounded answer, every protocol's shape. If the catalog doesn't support a fact, the field comes back empty. Never invented.
What we claim: answers grounded in the actual catalog, structured for machine reasoning.
What we don't claim: we don't control what the consuming system does with the answer.
A file can be read. A catalog has to be asked.
Feeds and exports answer one question: here is everything. But an AI system doesn't want everything — it wants the product that matches an intent expressed in words: something for dry skin that won't clog pores, a cable that works with this laptop, a gift under a certain size.
Answering that requires structured product meaning behind an interface that can be queried, with answers grounded in the real catalog instead of assembled from a model's assumptions.
Built for the systems doing the asking.
- AI assistants and shopping agents that need real product grounding
- Applications building product discovery, comparison or recommendation
- Platforms serving many merchants that need one consistent product interface
- Enterprise teams building internal commerce tooling
Not a fit if you need a scheduled file delivery — that's Feeds or Export.
What we need from you.
- 1.The catalog source (yours, or your merchants').
- 2.Read access, only if the platform requires it.
- 3.The kind of questions your system needs to answer.
How we handle access
- Private, rotatable access key per consumer — revocable at any time
- Keys stored hashed and never logged; strict isolation between catalogs
- Read-only relationship with the source store
- Rate limits and access scope agreed before anything goes live
What the interface does.
Serves your enriched catalog through a queryable interface built for machine consumption.
Answers questions about products in terms of meaning — what a product is, what it's for, who it suits, what it's compatible with — not just keyword matching over titles.
Grounds every answer in the real catalog, so the consuming system isn't guessing.
Stays current as the underlying catalog changes.
Rule we don't break: Verified, never invented. Only what the catalog supports.
What you get.
- A queryable interface to the enriched catalog
- Product meaning structured for machine reasoning
- Private access credentials, scoped and revocable
- Consistent behavior across every catalog you connect
- Documentation for the teams integrating it
Three steps.
Catalogs, question types, expected volume.
The catalog is enriched and served behind your credentials.
Your system queries it; answers stay grounded in the catalog.
What teams ask.
- How is this different from a feed?
- A feed publishes everything on a schedule; this is asked a question and returns the product that matches. Feeds are for destinations that pull a full catalog; Catalog Agentic™ is for systems that need to reason — matching an intent described in words to a real product with real attributes.
- What kind of questions can a system ask?
- Questions about meaning rather than keywords: what a product is for, who it suits, what it's compatible with, what constraints it satisfies. That's possible because the catalog is enriched with structured attributes first — the interface is only as good as the product meaning behind it.
- Can it answer something my catalog doesn't support?
- No, by design. Answers are grounded in the enriched catalog, so a system asking about a fact your catalog doesn't contain gets an honest empty answer instead of an invented one.
- How is access controlled?
- Each consumer gets a private, rotatable key that can be revoked at any time. Keys are stored hashed and never logged, isolation between catalogs is enforced, and rate limits and access scope are agreed before anything goes live.
- What does integration involve for my team?
- Credentials, an interface and documentation — your system queries it the way it would query any other service you integrate. There's no model to host, no index to maintain on your side and no catalog copy to keep in sync; the enrichment and the serving stay on our side of the line.
- What about volume and response times?
- Both are agreed before anything goes live, alongside rate limits and access scope. We'd rather size it honestly with you than publish a number that doesn't survive your actual traffic.
- Why is this Contact Sales instead of a price on the page?
- Because scope genuinely varies: how many catalogs, what volume, what integration. A single number on a page would be wrong for almost everyone who reads it, so we scope it with you instead of guessing in public.
Give your system a catalog it can actually reason about.
Where this sits
- Part ofNSOLVIA's Semantic Commerce Layer™NSOLVIA's framework for practising IEO on commerce data — the interpretability layer between catalogs and the systems that read them.
- Alongside it
- the Agentic Catalog Readiness Audit™ — The free instrument: reads a real product the way machine systems do and reports its interpretability baseline.
- the Agentic Catalog Optimizer™ for Shopify — Writes your verified product data into Shopify's native category attributes.
- the AI-Ready Commerce Feeds™ product — Enrich the catalog once and deliver it in the format each destination expects.
Catalog Agentic™sits inside NSOLVIA's interpretability stack — an open discipline, the framework that implements it, and the instruments that apply it.