The Problem
AI-generated SEO content uses stale training data: wrong pricing, outdated tool names, missing new competitors. Manual fact-checking each article costs 30-60 minutes of editing time.
The Scavio Solution
Before generating content, search for the target keyword to get current rankings, PAA questions, and competitor data. Pass this as grounding context to the AI writer. Generates accurate, current content at $0.025/article in search costs.
Before
Before SERP grounding, a content team published 20 AI-generated articles per month. Each required 45 minutes of fact-checking. Monthly editing cost: 15 hours x $75/hour = $1,125.
After
After adding SERP grounding, the same team generates content with current data built in. Fact-checking drops to 10 minutes per article. Monthly search cost: $0.50 (20 articles x $0.025). Editing cost drops to 3.3 hours x $75 = $250. Net savings: $875/month.
Who It Is For
Content teams, SEO professionals, AI writing tool builders, and marketing teams producing data-driven content at scale.
Key Benefits
- $0.025/article for 5 grounding searches
- Current competitor pricing and features in content
- PAA questions become article sections automatically
- AI Overview data informs content structure
- Fact-checking time drops from 45 to 10 minutes
Python Example
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def grounding_context(keyword):
queries = [keyword, f'{keyword} pricing', f'{keyword} alternatives',
f'{keyword} vs', f'{keyword} review']
context = {}
for q in queries:
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json={'query': q, 'country_code': 'us',
'include_ai_overview': True}).json()
context[q] = {
'top_3': [r['title'] for r in data.get('organic_results', [])[:3]],
'paa': [p['question'] for p in data.get('people_also_ask', [])],
'aio': bool(data.get('ai_overview')),
}
return context
# Pass to LLM as system prompt context
ctx = grounding_context('project management tools 2026')
print(f'Grounding with {sum(len(v["paa"]) for v in ctx.values())} PAA questions')JavaScript Example
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function groundingContext(keyword) {
const queries = [keyword, `${keyword} pricing`, `${keyword} alternatives`];
const ctx = {};
for (const q of queries) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H,
body: JSON.stringify({query: q, country_code: 'us', include_ai_overview: true})
}).then(r => r.json());
ctx[q] = {
top3: (r.organic_results || []).slice(0, 3).map(r => r.title),
paa: (r.people_also_ask || []).map(p => p.question),
};
}
return ctx;
}
groundingContext('project management tools').then(c => console.log(JSON.stringify(c, null, 2)));Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit