The Problem
LLMs confidently generate wrong brand facts: outdated pricing, discontinued products, features that do not exist, and availability in regions where the brand does not operate. Marketing teams and consultants relying on AI for competitive research unknowingly base decisions on hallucinated data. Manual verification of every AI-generated brand fact defeats the purpose of using AI.
The Scavio Solution
Ground every brand research query with a live search before the LLM generates its answer. Search for '[brand] pricing 2026' or '[brand] features' via Scavio, pass the fresh results into the prompt as context, and instruct the LLM to only cite facts from the provided search results. This turns the LLM from an unreliable memory into a reliable synthesizer of current data.
Before
Ask AI: 'What does Notion charge?' AI says $8/user/month for Team plan (correct in 2024, wrong in 2026). Consultant builds a comparison deck with wrong pricing. Client notices. Credibility damaged.
After
Search-grounded workflow: Scavio searches 'Notion pricing 2026'. Fresh pricing page data goes into prompt. AI reports current pricing with citation. Cost: $0.005. Consultant's comparison deck is accurate.
Who It Is For
Marketing teams, consultants, and analysts who use AI for competitive research and need brand facts to be accurate and current rather than hallucinated.
Key Benefits
- Eliminates brand pricing hallucinations with live data
- Each brand lookup costs $0.005 -- 50 brands for $0.25
- LLM synthesizes fresh data instead of relying on stale training
- Every fact comes with a source URL for verification
- Works with any LLM -- not tied to a specific model provider
Python Example
import requests, os
API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY, "Content-Type": "application/json"}
def grounded_brand_research(brand: str, aspect: str = "pricing") -> str:
query = f"{brand} {aspect} 2026"
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers=H,
json={"query": query, "country_code": "us"},
timeout=10,
)
results = resp.json().get("organic_results", [])[:5]
context = "\n".join(
f"Source: {r['link']}\n{r['title']}: {r['snippet']}" for r in results
)
# Feed this context into your LLM prompt
prompt = f"""Based ONLY on the following search results, summarize {brand}'s {aspect}.
Do not use any information not present in these sources.
Cite the source URL for each fact.
Search results (retrieved just now):
{context}
Summary:"""
return prompt
# Ground brand research with live data
prompt = grounded_brand_research("Notion", "pricing")
print(prompt)
# Pass this prompt to your LLM for accurate, cited outputJavaScript Example
const API_KEY = process.env.SCAVIO_API_KEY;
const H = {"x-api-key": API_KEY, "Content-Type": "application/json"};
async function groundedBrandResearch(brand, aspect = "pricing") {
const query = `${brand} ${aspect} 2026`;
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: H,
body: JSON.stringify({ query, country_code: "us" }),
});
const results = (await res.json()).organic_results || [];
const context = results.slice(0, 5)
.map(r => `Source: ${r.link}\n${r.title}: ${r.snippet}`)
.join("\n");
const prompt = `Based ONLY on the following search results, summarize ${brand}'s ${aspect}.
Do not use any information not present in these sources.
Cite the source URL for each fact.
Search results (retrieved just now):
${context}
Summary:`;
return prompt;
}
const prompt = await groundedBrandResearch("Notion", "pricing");
console.log(prompt);
// Pass this prompt to your LLM for accurate, cited outputPlatforms Used
Web search with knowledge graph, PAA, and AI overviews
Amazon
Product search with prices, ratings, and reviews