The Problem
LLMs confidently state outdated or fabricated prices for SaaS tools, APIs, and physical products. A model trained in 2024 will quote 2024 prices in 2026. Users trust these prices and make purchasing decisions based on them.
The Scavio Solution
Before prompting the LLM for any price-related answer, fetch the current SERP for the pricing query and inject the top snippets into the prompt as grounding context. Instruct the model to use only the provided context for price claims.
Before
User asks an AI assistant 'How much does Firecrawl cost?' The model answers '$16/month' based on training data. The user does not check and makes a budget decision based on an incorrect or outdated price.
After
The assistant fetches 'Firecrawl pricing 2026' from SERP, injects the current pricing page snippet into the prompt, and answers with the current price citing the source. Price is verifiable and current.
Who It Is For
Developers building AI assistants, chatbots, or research agents that answer questions about pricing, availability, or any time-sensitive facts.
Key Benefits
- Eliminates price hallucinations for any product or service
- Works with any LLM (Claude, GPT-4o, Llama, Gemini)
- One search credit per grounding call ($0.005)
- Generalizes to any factual claim: release dates, feature availability, availability
Python Example
import requests
import anthropic
SCAVIO_KEY = "your-scavio-api-key"
def grounded_price_answer(product: str) -> str:
# Fetch current pricing data
r = requests.post(
"https://api.scavio.dev/api/v1/search",
json={"query": f"{product} pricing 2026", "num_results": 5},
headers={"x-api-key": SCAVIO_KEY}, timeout=15
)
snippets = "\n".join(
f"{res['title']}: {res.get('snippet','')}"
for res in r.json().get("organic_results", [])[:3]
)
prompt = f"""Current pricing data from search:\n{snippets}\n\nUsing ONLY the above, what does {product} cost? State the price and cite the source."""
client = anthropic.Anthropic()
msg = client.messages.create(model="claude-sonnet-4-6", max_tokens=256,
messages=[{"role": "user", "content": prompt}])
return msg.content[0].text
print(grounded_price_answer("Firecrawl"))JavaScript Example
const SCAVIO_KEY = 'your-scavio-api-key';
async function groundedPriceAnswer(product) {
const res = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'x-api-key': SCAVIO_KEY },
body: JSON.stringify({ query: `${product} pricing 2026`, num_results: 5 })
});
const data = await res.json();
const context = (data.organic_results ?? []).slice(0, 3)
.map(r => `${r.title}: ${r.snippet ?? ''}`).join('\n');
// Pass context to your LLM of choice
return context;
}Platforms Used
Web search with knowledge graph, PAA, and AI overviews