AI Search Built to Keep Sparky Accurate at Walmart's Scale

Roughly half of Walmart's U.S. app users have now used Sparky, and those shoppers build 35% larger carts. As Sparky moves into stores, automatic reordering, and new surfaces, its product and pricing answers only hold up if the search underneath them does too. You.com gives the unified Global Tech org one AI search layer, with real-time and traceable results delivered fast enough to run at Walmart's scale—across Walmart U.S., Sam's Club, and every international market.

Powering web search for leading enterprises.

Tailored to find, synthesize and ship.

Used to power AI systems, AI Agents, research tools, data pipelines, and more for Walmart’s engineering and product teams.

Search API

Take advantage of the world’s best Search API, delivering real-time results at record speed.

from youdotcom import You

with You("<apiKey>") as you:
    res = you.search.unified(
        query="best practices for scaling microservices architecture in production",
    )

    # Each result contains a title, URL, snippets and more
    if res.results and res.results.web:
        for result in res.results.web:
            print(f"Title: {result.title}")
            print(f"URL: {result.url}")
            if result.snippets:
                print(f"Snippet: {result.snippets[0]}\n")

Contents API

Fetch full page content for multiple URLs, streamlining content aggregation and analysis across various sources.

from youdotcom import You
from youdotcom.models import ContentsFormats

with You("<apiKey>") as you:
    res = you.contents.generate(
        urls=[
            "https://www.python.org",
            "https://www.example.com",
        ],
        formats=[ContentsFormats.HTML],
    )

    # Access the full page content in your chosen format
    for page in res:
        print(f"Title: {page.title}")
        print(f"HTML: {page.html}...")

Research API

Our tiered Research API—#1 in DeepSearchQA—pushes the frontier of accuracy and latency across both simple and complicated tasks.

from youdotcom import You
from youdotcom.models import ResearchEffort
 
you = You(api_key_auth="api_key")
 
res = you.research(
    input="Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?",
    research_effort=ResearchEffort.LITE,
)
 
print(res.output.content)
 
for i, source in enumerate(res.output.sources, 1):
    print(f"[{i}] {source.title or 'Untitled'}: {source.url}")

Finance Research API

Our Finance Research API delivers cited, source-reconciled financial intelligence across filings, macro data, and multi-step calculations. No infra required.

import requests

url = "https://api.you.com/v1/finance_research"
headers = {
    "X-API-Key": "<YOUR_API_KEY>",
    "Content-Type": "application/json",
}
payload = {
    "input": "Compare the free cash flow generation and capital allocation strategies of Apple, Microsoft, and Alphabet over the past three fiscal years",
    "research_effort": "deep",
}

response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()
data = response.json()

print(data["output"]["content"])

for i, source in enumerate(data["output"]["sources"], 1):
    print(f"[{i}] {source.get('title') or 'Untitled'}: {source['url']}")

1B+

Queries served monthly

57%

of the Fortune 500

99.9%

Uptime SLA & white-glove enterprise support

82%

Real-time accuracy on LiveQA — vs. Tavily 74%, Exa 68%

Walmart's Global Tech workflows

See How It Works

Ground Sparky's conversational shopping recommendations in real-time, cited product and market context, on one search layer that scales across every division. Keep pricing, inventory, and promotional answers current as they change by the minute. And hold answer quality to the same standard whether Sparky is answering in the Walmart app, in a store, or on a partner surface, so the experience stays Walmart's to control.