Solution

Stop LLMs From Citing Outdated Prices

LLMs confidently cite prices from their training data, but those prices are months or years stale. A user asks what something costs, the model answers with a specific dollar amount

The Problem

LLMs confidently cite prices from their training data, but those prices are months or years stale. A user asks what something costs, the model answers with a specific dollar amount from 2024 or early 2025, and the user makes a purchasing decision based on wrong information. The model has no concept of price being time-sensitive data, so it never qualifies its answer with uncertainty. This is liability for any application where pricing accuracy matters: shopping assistants, comparison tools, budget planners, or procurement agents.

The Scavio Solution

Scavio gives your agent live price data from Amazon and Walmart with a single API call. Before the agent cites any price, it queries the current listing and grounds its response in data from the last few seconds. The response includes the exact price, currency, availability status, and a link to the listing. Your agent goes from confidently wrong to verifiably correct, and the citation trail is auditable.

Before

Before Scavio, LLMs cited prices from training data that were months stale. Users made purchasing decisions based on confident-sounding wrong numbers with no indication of staleness.

After

After Scavio, every price the agent cites comes from a live search with a timestamp. Users get accurate prices with source links, and the application carries no pricing liability from stale training data.

Who It Is For

Developers building shopping assistants, price comparison tools, or procurement agents where citing a wrong price creates liability. Anyone whose LLM confidently quotes prices that are months stale.

Key Benefits

  • Live prices from Amazon and Walmart replace stale training data
  • Every price citation includes a source URL and timestamp
  • Sub-two-second latency fits inside a normal agent response turn
  • Availability status prevents recommending out-of-stock items
  • Eliminates pricing liability from stale LLM training data

Python Example

Python
import requests
from datetime import datetime

API_KEY = "your_scavio_api_key"

def get_live_price(product: str, platform: str = "amazon") -> dict:
    res = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": platform, "query": product},
        timeout=10,
    )
    res.raise_for_status()
    data = res.json()
    for item in data.get("organic", []):
        if item.get("price"):
            return {
                "product": item.get("title", ""),
                "price": item["price"],
                "currency": item.get("currency", "USD"),
                "link": item.get("link", ""),
                "platform": platform,
                "checked_at": datetime.utcnow().isoformat(),
            }
    return {"product": product, "price": None, "error": "no_price_found"}

def ground_price_claim(product: str) -> str:
    """Use this in your agent to ground any price claim."""
    amazon = get_live_price(product, "amazon")
    walmart = get_live_price(product, "walmart")
    prices = [p for p in [amazon, walmart] if p.get("price")]
    if not prices:
        return f"Could not verify current price for {product}."
    best = min(prices, key=lambda x: x["price"])
    return f"{best['product']} is currently ${best['price']} on {best['platform']} (checked {best['checked_at']})"

print(ground_price_claim("sony wh-1000xm5"))

JavaScript Example

JavaScript
const API_KEY = "your_scavio_api_key";

async function getLivePrice(product, platform = "amazon") {
  const res = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: { "x-api-key": API_KEY, "content-type": "application/json" },
    body: JSON.stringify({ platform, query: product }),
  });
  if (!res.ok) throw new Error(`scavio ${res.status}`);
  const data = await res.json();
  for (const item of data.organic ?? []) {
    if (item.price) {
      return {
        product: item.title ?? "",
        price: item.price,
        currency: item.currency ?? "USD",
        link: item.link ?? "",
        platform,
        checkedAt: new Date().toISOString(),
      };
    }
  }
  return { product, price: null, error: "no_price_found" };
}

async function groundPriceClaim(product) {
  const [amazon, walmart] = await Promise.all([
    getLivePrice(product, "amazon"),
    getLivePrice(product, "walmart"),
  ]);
  const prices = [amazon, walmart].filter((p) => p.price);
  if (!prices.length) return `Could not verify current price for ${product}.`;
  const best = prices.reduce((a, b) => (a.price < b.price ? a : b));
  return `${best.product} is currently $${best.price} on ${best.platform} (checked ${best.checkedAt})`;
}

console.log(await groundPriceClaim("sony wh-1000xm5"));

Platforms Used

Amazon

Product search with prices, ratings, and reviews

Walmart

Product search with pricing and fulfillment data

Google

Web search with knowledge graph, PAA, and AI overviews

Frequently Asked Questions

LLMs confidently cite prices from their training data, but those prices are months or years stale. A user asks what something costs, the model answers with a specific dollar amount from 2024 or early 2025, and the user makes a purchasing decision based on wrong information. The model has no concept of price being time-sensitive data, so it never qualifies its answer with uncertainty. This is liability for any application where pricing accuracy matters: shopping assistants, comparison tools, budget planners, or procurement agents.

Scavio gives your agent live price data from Amazon and Walmart with a single API call. Before the agent cites any price, it queries the current listing and grounds its response in data from the last few seconds. The response includes the exact price, currency, availability status, and a link to the listing. Your agent goes from confidently wrong to verifiably correct, and the citation trail is auditable.

Developers building shopping assistants, price comparison tools, or procurement agents where citing a wrong price creates liability. Anyone whose LLM confidently quotes prices that are months stale.

Yes. Scavio's free tier includes 250 credits per month with no credit card required. That is enough to validate this solution in your workflow.

Stop LLMs From Citing Outdated Prices

Scavio gives your agent live price data from Amazon and Walmart with a single API call. Before the agent cites any price, it queries the current listing and grounds its response in