Retrieval Built for Commerce
Structured product data for agents — the grounded retrieval layer that turns your catalog into agentic commerce data AI sellers can actually use.
Semantic search for ecommerce, indexed by meaning, intent, and use-case — not keywords.
A semantic retrieval layer for AI commerce
The grounded retrieval layer for RAG-based AI agents
A semantic retrieval API over your enriched, machine-readable catalog. AI agents query it by meaning, intent and use-case and get back ranked, structured products. NSOLVIA does retrieval; your LLM does generation.
A brain that truly understands your products
Give your AI assistant a brain that truly understands your products — so when shoppers (or agents) ask by meaning instead of keywords, your catalog answers in the right context.
Agentic commerce data your AI sellers can query
Semantic search for ecommerce, exposed as structured product data for agents — across the five intent surfaces that matter.
Agentic Commerce Retrieval
Autonomous shopping agents call the API by meaning and use-case, and get back ranked, structured products grounded in your real catalog.
Product Comparison
Structured attribute-level retrieval lets agents compare options across brands and variants — no hallucinated specs.
Product Discovery
Shoppers find products by meaning instead of keywords — same retrieval layer that powers NSOLVIA MSA.
Goal-Oriented Shopping
Agents map outcomes ('help me sleep better,' 'host 8 guests') to in-catalog SKUs.
Occasion/Use-Case Matching
Wedding, gift for mom, rainy commute — products retrieved by the moment they fit.
The knowledge layer between catalog and AI
NSOLVIA Semantic RAG feeds NSOLVIA MSA(your digital salesperson) and any external AI agent that needs grounded commerce knowledge. It's a standalone product — the same retrieval layer that powers MSA is available as an API for builders, agencies and platforms.
The agentic endpoints live in data.nsolvia.com. The marketing, pricing, and onboarding live here.
Semantic RAG is launching soon.
Want early access? Run the Agentic Catalog Readiness Audit to see how your catalog scores for grounded retrieval.
Audit My Catalog Free →The questions developers and teams ask
What happens when my bot or agent gives wrong product answers?
Generic bots guess or hallucinate, which erodes shopper trust and loses sales. Semantic RAG performs retrieval over enriched embeddings of your real catalog — every answer comes back with the exact product, attribute, and source, never invented.
What is Semantic RAG?
It’s a queryable “brain” built from your enriched catalog — retrieval that powers conversational answers, recommendations, and agents with accurate, catalog-grounded data.
How is it different from Catalog Export?
Catalog Export hands you the enriched data as an asset to take anywhere. Semantic RAG turns that data into a retrieval engine your bots and agents can query.
Who is it for?
Merchants, platforms, agencies, and development teams that want to power their own assistants or agents with answers grounded in real product data.
Does it depend on the other products?
It builds on your enriched catalog (from NSOLVIA DATA), so product catalog enrichment comes first.
Is it available now?
It’s coming soon.