Scavio for Support Agent Knowledge Search

The Problem

AI support agents trained on static knowledge bases give outdated answers about pricing, features, and policies that changed after the last training update. Customers get wrong information and lose trust.

How Scavio Helps

• Answers grounded in current web data
• No manual knowledge base updates needed
• Reddit search surfaces real customer experiences
• Reduces hallucination in support responses
• Works as MCP tool in Claude-based support agents

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Customer asks 'does your product integrate with Salesforce?' Support agent searches Google for current integration page rather than relying on 3-month-old training data. If integration was added last week, agent finds it. If page is missing, agent correctly says it's not available instead of hallucinating.":

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 Customer support teams, CX engineers building AI support agents, SaaS companies with AI chat support

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