The Problem
Enterprise AI agents need external market data, competitor pricing, and industry benchmarks to make good decisions, but the internal ERP, CRM, or data warehouse has no API for external web data. The enterprise data lake contains internal data: sales figures, inventory levels, customer records. It does not contain what competitors are charging, what customers are saying on Reddit, or what market trends look like this week. Building a web data ingestion pipeline from scratch means months of infrastructure work, security reviews, and vendor procurement.
The Scavio Solution
Scavio provides a single, secure API endpoint that enterprise AI agents call to get external web data without building custom ingestion infrastructure. The agent queries Google for market research, Amazon and Walmart for competitive pricing, YouTube for industry content, and Reddit for customer sentiment. All through one authenticated endpoint that passes enterprise security review. No scraping infrastructure to build, no proxy fleet to manage, no separate vendor for each platform. The data layer goes from months of infrastructure work to a single API key.
Before
Before Scavio, enterprise AI agents operated with only internal data. Adding external web data required months of infrastructure work, multiple vendor procurements, and security review cycles that killed the timeline.
After
After Scavio, enterprise AI agents access external web data through a single secure API. The data layer deploys in a day instead of a quarter, and security review covers one vendor instead of five.
Who It Is For
Enterprise architects and AI platform teams who need to add external web data to their agent infrastructure without months of custom integration work. Anyone whose internal data lake has no window into the external web.
Key Benefits
- Single API endpoint passes enterprise security review once
- No scraping infrastructure, proxy fleet, or browser farm to build
- Covers market research, competitive pricing, and sentiment in one key
- SOC 2 compliant endpoint with audit logging
- Deploys in a day instead of a quarter of infrastructure work
Python Example
import requests
import json
API_KEY = "your_scavio_api_key" # From enterprise secrets manager
class EnterpriseDataLayer:
"""Unified external data access for enterprise AI agents."""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.scavio.dev/api/v1/search"
def _search(self, platform: str, query: str) -> dict:
res = requests.post(
self.base_url,
headers={"x-api-key": self.api_key},
json={"platform": platform, "query": query},
timeout=15,
)
res.raise_for_status()
return res.json()
def market_research(self, topic: str) -> dict:
return self._search("google", f"{topic} market analysis 2026")
def competitive_pricing(self, product: str) -> dict:
amazon = self._search("amazon", product)
walmart = self._search("walmart", product)
return {"amazon": amazon.get("organic", [])[:5], "walmart": walmart.get("organic", [])[:5]}
def customer_sentiment(self, brand: str) -> dict:
return self._search("reddit", f"{brand} review experience")
def industry_content(self, topic: str) -> dict:
return self._search("youtube", f"{topic} industry analysis")
# Usage in enterprise agent
data_layer = EnterpriseDataLayer(API_KEY)
pricing = data_layer.competitive_pricing("enterprise project management software")
print(f"Amazon options: {len(pricing['amazon'])} | Walmart options: {len(pricing['walmart'])}")JavaScript Example
const API_KEY = process.env.SCAVIO_API_KEY; // From enterprise secrets manager
class EnterpriseDataLayer {
constructor(apiKey) {
this.apiKey = apiKey;
this.baseUrl = "https://api.scavio.dev/api/v1/search";
}
async _search(platform, query) {
const res = await fetch(this.baseUrl, {
method: "POST",
headers: { "x-api-key": this.apiKey, "content-type": "application/json" },
body: JSON.stringify({ platform, query }),
});
if (!res.ok) throw new Error(`scavio ${res.status}`);
return res.json();
}
async marketResearch(topic) {
return this._search("google", `${topic} market analysis 2026`);
}
async competitivePricing(product) {
const [amazon, walmart] = await Promise.all([
this._search("amazon", product),
this._search("walmart", product),
]);
return { amazon: (amazon.organic ?? []).slice(0, 5), walmart: (walmart.organic ?? []).slice(0, 5) };
}
async customerSentiment(brand) {
return this._search("reddit", `${brand} review experience`);
}
async industryContent(topic) {
return this._search("youtube", `${topic} industry analysis`);
}
}
const dataLayer = new EnterpriseDataLayer(API_KEY);
const pricing = await dataLayer.competitivePricing("enterprise project management software");
console.log(`Amazon: ${pricing.amazon.length} | Walmart: ${pricing.walmart.length}`);Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Amazon
Product search with prices, ratings, and reviews
YouTube
Video search with transcripts and metadata
Walmart
Product search with pricing and fulfillment data
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