Let AI agents understand
your products.
An AI shopping agent doesn't browse — it filters. It needs the attributes, variants, availability, shipping conditions, and fit signals a shopper would ask about, in a form it can compare across brands. Nile compiles your catalog into that context and publishes it where agents look, so your products make the shortlist in ChatGPT, Gemini, and Perplexity.
Why product data decides discovery
When we scored 40 premium DTC beauty brands on agent readiness, crawlability criteria passed at 47–92% while content-quality criteria collapsed to 12–40%. Agents could reach the pages; they could not compare the products.
Agents compare, not read
An agent resolves “running shoes under $120 for daily training, size 10, ships to the US” into filters. If your data doesn't answer size, use case, price, and shipping, you're not ranked lower — you're not in the set.
Keyword feeds don't help
UCP and similar schemas replace keyword-stuffed feeds with a handful of semantic fields. Ranking stops being about tricks and starts being about whether the data is genuinely good.
Every agent ranks differently
What ChatGPT shortlists is not what Gemini or Perplexity shortlists. Discovery has to be measured per channel with real agent sessions, then tuned.
How discovery works
Catalog, variants, and policies come in
Shopify, feed, CSV, or API. Products, options, media, price, stock, shipping rules, and return policies sync automatically.
Nile builds agent-readable context
Attributes are normalised per category; fit, use case, compatibility, and comparison facts are structured; approved claims and reviews become explanation material.
Context is published on every rail
UCP for Google's agent surfaces, ACP for ChatGPT's, MCP for tool access, Agent Skills for distribution — the same context, compiled per channel.
Ranking is measured and tuned
Sandboxed agent sessions per channel show how each assistant shortlists and describes you; you adjust context, not code, and watch ranking move.
What an agent gets from Nile
Category-specific facts
The fields agents filter on per category — sizing and fit, materials, ingredients, compatibility, dimensions, ratings — normalised so they compare across brands.
Options & availability
Every variant with its own price and stock, so “size 10 in black” resolves to a real, in-stock offer rather than a parent product.
Delivery & eligibility
Shipping country, speed, and cost conditions the agent needs before it can promise delivery — read from your live rules.
Use case & comparison
Who a product is for, what it is not for, and how it differs from alternatives — the questions assistants are asked and need to quote.
Reviews & approved claims
Structured review signal and only the claims you approved, so recommendations are explainable and on-brand.
Price & stock, rechecked
Continuously synced and validated again at cart and checkout, so discovery never leads to a stale price or an out-of-stock item.
What it does for the business
Make the shortlist
Products become comparable on the terms agents actually use, in the assistants where research now starts.
Explainable recommendations
Agents can say why a product fits, using your approved claims and structured reviews — not a scraped guess.
Ranking you can see
Per-channel ranking across query sets and intents, measured with real agent sessions, so tuning is evidence-based.
No re-platforming
Nile compiles from the catalog you already have; PDPs, PIM, and checkout stay as they are.
Control over what agents say
Discovery sits on the same transaction guarantees as the rest of Nile (confirmation, live rechecks, click-through attribution, sandboxed measurement — see the merchant page). The controls that matter for discovery itself are about what agents may say:
Approved claims only
Agents explain products with the claims you set; nothing is invented and nothing off-limits is quoted.
Exclusions & regions
Products, categories, and regions you exclude are never exposed to agents in the first place.
Context you can edit
Where data is thin, Pro lets you add fit, use-case, and comparison facts in plain language; export or disconnect at any time.
Implementation steps
Connect the catalog
Shopify app, feed, CSV, or API. Variants, price, stock, and shipping rules sync immediately.
Review compiled context
See how Nile normalised attributes and fit facts per category; fill gaps in plain language with Pro.
Watch agent sessions
Sandboxed sessions per channel show what each assistant shortlists for your key queries.
Tune and track
Adjust context, watch ranking across query sets and intents, and pay commission only on completed sales.
Questions about AI product discovery
What is AI product discovery?
How an AI shopping agent finds and shortlists products: by resolving a shopper's request into filters — attributes, price, size, shipping country, use case — and querying structured supply that answers them. It is a data problem before it is a content problem.
How do AI agents understand variants, stock, and shipping?
Only if each variant carries its own price, availability, and shipping eligibility. Nile compiles variants as real offers and reads price, stock, and shipping rules from your live store, rechecked at cart and checkout.
Is this the same as GEO or feed optimization?
No. GEO optimizes prose to be cited; feed optimization stuffs keywords into a shopping feed. Agents compare on a small set of semantic fields and live availability. Nile builds that context and publishes it on the rails agents read — see the comparison.
How do I know how each assistant ranks my products?
Nile measures ranking per channel with sandboxed agent sessions across your query sets and intents, so you see how ChatGPT, Gemini, and Perplexity each shortlist you and what changes when you adjust context.
Do I have to rewrite my product pages?
No. Nile compiles from the catalog you already have. Where data is thin, Pro lets you add fit, use-case, and comparison facts in plain language rather than editing PDPs.
Which product fields matter most for being shortlisted?
The ones an agent filters on: precise attributes per variant, current price and availability, shipping eligibility for the shopper's location, fit or compatibility, and approved comparative claims. Long descriptions rank below a correct size table and a live stock flag.
More solutions
The same backend, framed for the team that is asking. Enterprise controls for all of them are on the enterprise page.
Merchants & brands
Make a single store readable and buyable for AI shopping agents.
Marketplaces
Expose multi-seller inventory to agents with seller rules and routing intact.
Retail & multi-brand
Run agentic commerce as a governed channel across banners, regions, and stores.
Headless & custom stacks
Connect a custom or headless storefront by API without touching checkout.
Make the shortlist.
No setup fee. No retainer. Commission only on completed, attributed sales.
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