The Problem
LLMs hallucinate outdated pricing, deprecated APIs, and wrong version numbers because their training data is months old. Without live search grounding, every factual claim needs manual verification.
The Scavio Solution
Inject live search results into the LLM context window before generation. Scavio returns structured SERP data including AI Overviews, Knowledge Graph, and organic results that the model can cite directly. 3-5 grounding searches per task cost $0.015-0.025.
Before
LLM confidently states Stripe API is v2023-10-16 and charges 2.9% + 30 cents. Both are wrong in 2026. User trusts the output and ships broken code.
After
Pre-generation search confirms Stripe API is v2026-04-01 at 2.7% + 30 cents. LLM cites the source. User ships correct code on the first try.
Who It Is For
AI engineers building chatbots, copilots, and agents that need factually accurate responses grounded in current data.
Key Benefits
- Eliminate hallucinated pricing and version numbers
- Structured results reduce token waste vs raw HTML
- AI Overview extraction for quick factual grounding
- 3-5 searches per task at $0.015-0.025 total
- Works with any LLM via simple context injection
Python Example
import requests, os
API_KEY = os.environ["SCAVIO_API_KEY"]
def ground_with_search(query: str) -> str:
"""Fetch live search data to ground LLM responses."""
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
json={"query": query, "country_code": "us"},
timeout=10,
)
data = resp.json()
context_parts = []
if data.get("ai_overview"):
context_parts.append(f"AI Overview: {data['ai_overview'].get('text', '')}")
if data.get("knowledge_graph"):
kg = data["knowledge_graph"]
context_parts.append(f"Knowledge Graph: {kg.get('title', '')} - {kg.get('description', '')}")
for r in data.get("organic_results", [])[:5]:
context_parts.append(f"[{r.get('position')}] {r.get('title', '')}: {r.get('snippet', '')}")
return "\n".join(context_parts)
# Inject into LLM prompt
grounding = ground_with_search("stripe api pricing 2026")
prompt = f"Based on this current data:\n{grounding}\n\nAnswer: What is Stripe's current API version and pricing?"
print(prompt)JavaScript Example
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function groundWithSearch(query) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {method:'POST', headers:H, body:JSON.stringify({query, country_code:'us'})});
const d = await r.json();
const parts = [];
if (d.ai_overview) parts.push('AI Overview: ' + d.ai_overview.text);
for (const r of (d.organic_results||[]).slice(0,5)) parts.push('[' + r.position + '] ' + r.title + ': ' + r.snippet);
return parts.join('\n');
}
const grounding = await groundWithSearch('stripe api pricing 2026');
console.log('Grounding context:\n' + grounding);Platforms Used
Web search with knowledge graph, PAA, and AI overviews