Real-Time Data for LLMs

The Problem

LLMs ship with a fixed knowledge cutoff. The moment a user asks about earnings from last week, a product that launched yesterday, or a tournament result from an hour ago, the model either hallucinates a confident answer or refuses. Traditional retrieval pipelines rely on static vector stores that were indexed days or months earlier. For any question involving news, prices, sports, or trending topics, stale retrieval is functionally the same as no retrieval. Teams end up shipping assistants that feel smart in demos but wrong in production the minute a user asks something time-sensitive.

The Scavio Solution

Scavio gives your LLM a live window into Google, YouTube, Amazon, and Walmart with a single REST call. Every search returns structured JSON that can be dropped straight into a tool call or a grounding context block. There is no crawler to maintain, no proxy pool to rotate, no HTML to parse. You send a query, you get normalized results in under two seconds, and your model answers with facts that existed thirty seconds ago. Organic results, AI Overviews, Knowledge Graph panels, and product cards all arrive in the same predictable schema.

Who It Is For

AI engineers building chatbots, copilots, and agents who need their LLM to answer time-sensitive questions accurately. If your users ask about news, prices, or anything that changed this week, this is for you.

Key Benefits

• Sub-two-second median latency from query to structured JSON
• Live Google AI Overviews and Knowledge Graph exposed as typed fields
• One API key covers Google, YouTube, Amazon, and Walmart
• Normalized schema means your prompt template never changes per platform
• No proxy rotation, CAPTCHA solving, or headless browsers to operate

Python Example

import requests

API_KEY = "your_scavio_api_key"

def fresh_context(query: str) -> str:
    response = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": "google", "query": query, "ai_overview": True},
        timeout=10,
    )
    data = response.json()
    blocks = []
    if data.get("ai_overview"):
        blocks.append(data["ai_overview"]["text"])
    for result in data.get("organic", [])[:5]:
        blocks.append(f"{result['title']}: {result['snippet']}")
    return "\n\n".join(blocks)

print(fresh_context("nvidia earnings q1 2026"))

JavaScript Example

const API_KEY = "your_scavio_api_key";

async function freshContext(query) {
  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: "google", query, ai_overview: true }),
  });
  const data = await res.json();
  const blocks = [];
  if (data.ai_overview) blocks.push(data.ai_overview.text);
  for (const r of (data.organic ?? []).slice(0, 5)) {
    blocks.push(`${r.title}: ${r.snippet}`);
  }
  return blocks.join("\n\n");
}

console.log(await freshContext("nvidia earnings q1 2026"));

Platforms Used