Scavio for RAG with Citations Pipeline

The Problem

RAG citations fail when the LLM cannot tie a claim back to a source URL. Scavio's typed JSON gives every retrieved snippet a link field, making the citation step deterministic.

How Scavio Helps

• Deterministic citation marker mapping
• Multi-source citations (web + Reddit)
• Per-query cost ~$0.005-0.02
• Validates citation correctness via regex
• Marks community sources distinctly

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "answer with citations: best mcp practices 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 RAG pipeline builders, AI search product teams, knowledge-base SaaS authors, Anthropic + OpenAI developers

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

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.