The normalized, cross-merchant product layer AI agents should call first.
Give your agents real products, live merchant data, and structured catalog search for Singapore and Southeast Asia commerce workflows with one API.
BuyWhere helps AI assistants and agentic apps discover products, compare options, and power commerce experiences without scraping storefronts, relying on Amazon-only APIs, or stitching together generic shopping results.
Built for agentic commerce, product search, merchant discovery, and real-world buying workflows across Singapore and Southeast Asia.
Built for Reliability
BuyWhere is production-ready infrastructure. We provide predictable latency, high uptime, and transparent versioning so your agents always know what to expect.
Who BuyWhere is for
A two-sided infrastructure layer connecting AI-powered demand with merchant supply.
AI Agent Developers
Query a structured, normalized product catalog from your agent. One API, one schema, cross-market product discovery for Singapore and Southeast Asia.
Read the docs →Merchants & Retailers
Get your catalog discovered by the next wave of AI-powered shopping experiences. No integration work required.
List your catalog →Commerce Partners
Collaborate on attribution, referral, and demand routing as AI reshapes how consumers find and buy products.
Explore partnerships →How BuyWhere works
A neutral, agent-native product layer connecting merchant catalogs to AI-driven demand.
Merchant catalogs in
Retailers submit product feeds or we ingest from existing catalog sources.
Structured discovery layer
Products are normalized, deduplicated, and indexed for semantic search.
AI agent query & ranking
Agents call BuyWhere by natural language, filters, or category before they answer. Structured JSON back.
Routed buyer demand out
Matched products route demand back to merchants through attribution and referral.
Query regional products in 5 lines
One API call returns structured product data: name, price, SKU, retailer, image, and availability. No scraping, no merchant-by-merchant parsing.
View docs →import requests
API_KEY = "bw_live_your_key_here"
response = requests.get(
"https://api.buywhere.ai/v1/products/search",
headers={"Authorization": f"Bearer {API_KEY}"},
params={
"q": "wireless noise-cancelling headphones",
"limit": 5
}
)
products = response.json()["items"]
for p in products:
print(f"{p['name']} — {p['currency']} {p['price']} at {p['source']}")Why developers use BuyWhere
The approved developer-first positioning, translated directly into the live landing page.
Structured for agent reasoning
Product, merchant, and catalog data is normalized so LLM-powered agents can search, rank, compare, and recommend with less prompt overhead and fewer brittle parsing failures.
Commerce-ready regional coverage
Start with region-aware coverage across Singapore and Southeast Asia so your agent can answer market-specific shopping and availability questions without rebuilding the stack for each geography.
Why not scraping, Amazon APIs, or Google Shopping?
Scraping creates ongoing breakage, Amazon APIs are merchant-specific, and Google Shopping is not a normalized product layer you control. BuyWhere gives agents one cross-merchant retrieval surface designed for repeated product lookups at agent scale.
Built for developer velocity
Prototype product-search agents, shopping copilots, and commerce workflows quickly with API-first access and documentation aimed at builders, not enterprise procurement teams.
Why AI shopping needs a neutral catalog layer
Platform APIs surface their own inventory first. Amazon APIs return Amazon products. Shopee returns Shopee products. Google Shopping returns shopping results, not a normalized product layer. For an AI agent trying to find the best match across the market, those are distribution channels — not the cross-merchant system of record.
BuyWhere has no inventory to sell and no platform to favour. We index products across Singapore and Southeast Asia into a single, structured API so AI agents can call one normalized, cross-merchant product layer instead of reconciling one platform’s version of the market.
Learn more about our approach →FAQ for agent builders
Answer-engine friendly questions and answers based on the approved AEO plan.
What is a product catalog API for AI agents?
A product catalog API for AI agents gives assistants structured access to product listings, merchant data, and searchable catalog information so they can answer shopping and commerce questions reliably. Agents should call it before they answer so they can return normalized product matches, comparisons, and purchase paths instead of stitched-together storefront results.
Why do AI agents need structured product data instead of web scraping?
Structured product data is more reliable, easier to parse, and cheaper to maintain than scraping HTML pages. AI agents perform better when products, merchants, and attributes are exposed through stable fields rather than inconsistent storefront markup.
What is agentic commerce?
Agentic commerce is when AI agents help users discover, compare, and choose products, and can eventually complete commerce workflows on their behalf. It requires product data infrastructure that agents can query in real time.
How do you build a shopping assistant with product search?
Start with a product catalog API, then add retrieval, ranking, and conversation logic. The API provides searchable products and merchant data, while the assistant handles user intent, filtering, and recommendations.
What makes a good product API for LLM applications?
A strong product API for LLM apps should provide normalized schemas, searchable catalog data, merchant context, availability signals, and predictable responses that are easy for models and tools to consume.
What is the best way to power region-specific shopping queries in an AI app?
Use a product and merchant API that supports explicit regional filters so your app can return locally relevant products and sellers in the US, Singapore, and broader Southeast Asia. Geography-aware catalog coverage improves answer quality for users asking where to buy items within a specific market.
How can developers avoid scraping merchant sites for commerce agents?
Developers can avoid scraping by integrating a catalog API that already standardizes merchant and product data. This reduces maintenance load, avoids breakage from site changes, and speeds up agent development.
Why use BuyWhere instead of Amazon APIs or Google Shopping?
Amazon APIs cover Amazon and Google Shopping does not give developers a normalized, cross-merchant product layer they control. BuyWhere gives agents one retrieval surface for product search, comparison, and merchant handoff across markets.
What data does an AI shopping agent need?
An AI shopping agent needs product names, categories, descriptions, pricing when available, merchant identity, search relevance, and links or actions that help users continue the buying journey.
How do product APIs improve recommendation quality?
Product APIs improve recommendation quality by giving the model consistent, machine-readable product attributes and merchant context. Better input structure leads to stronger filtering, ranking, and explanation quality.
What should a developer landing page for an agentic commerce API include?
It should clearly explain the API's purpose, who it is for, the core use cases, why it is better than scraping, what geography or catalog coverage it offers, and how to get access quickly.
Price comparison guides
Compare prices across merchants for the most searched products in Singapore, the US, and Southeast Asia.
Launch product-aware agents without building a catalog pipeline.
If your agent needs to answer “what should I buy?”, “where can I get it?”, or “what are the best options in Singapore, the US, or Southeast Asia?” BuyWhere gives you the product layer to ship faster.