Scavio for RAG Pipeline

The Problem

LLMs have a knowledge cutoff and can hallucinate. RAG fixes this by retrieving current data before generating a response. But most RAG pipelines only search a static knowledge base.

How Scavio Helps

• Real-time web data for RAG retrieval
• Multi-platform search in a single API call
• Structured JSON responses ready for LLM consumption
• Knowledge graphs and PAA for richer context

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "what is the best laptop for programming in 2026":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

Built for AI engineers, LLM application developers

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your rag pipeline solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.