Scavio for LLM Failure Detection via Search Validation

The Problem

LLMs hallucinate facts, cite non-existent papers, and state outdated information with high confidence. Without external validation, these failures reach end users and erode trust in AI-powered products.

How Scavio Helps

• Search validates LLM claims against current web data
• Catches outdated pricing, version numbers, and facts
• Detects fabricated citations and non-existent URLs
• Reddit surfaces community-reported LLM failures
• Automated validation pipeline for production LLM outputs

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "LLM claims 'Tavily pricing starts at $20/month.' Validation pipeline searches Google for current Tavily pricing page. Finds actual price is $30/month. Flags the output as incorrect before it reaches the user. Over 1000 validated outputs, catches 12% error rate on pricing claims and 8% on version numbers.":

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 product teams, QA engineers for LLM applications, researchers studying LLM reliability

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