Scavio for Answer Engine Building

The Problem

Users want instant, sourced answers instead of a list of blue links. Building an answer engine requires real-time search across multiple platforms, content extraction, and LLM-based synthesis -- all from a single query.

How Scavio Helps

• Multi-platform search results in structured JSON for LLM consumption
• Google Knowledge Graph for factual grounding
• YouTube transcripts for in-depth source material
• Reddit threads for community-validated answers

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "what is the best way to deploy a next.js app":

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 startups, developer tool builders, search product teams

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your answer engine building 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.