The Problem
AI SDRs and sales agents need to research prospects before outreach, but traditional enrichment tools (Clearbit, ZoomInfo, Apollo) are built for human workflows with dashboards and CSV exports. AI agents need API-first enrichment that returns structured data in milliseconds, not browser-based tools that require manual interaction. The gap between enrichment tool design and agent consumption patterns creates friction in automated sales workflows.
The Scavio Solution
Build an agent-native enrichment pipeline using Scavio's multi-platform search. For each prospect, run parallel searches across Google (company context), Google Maps (office location, reviews), Reddit (brand sentiment), and Amazon (if product company). The agent receives structured JSON it can reason over immediately, building a prospect profile in seconds without human interaction.
Before
Before: An AI SDR agent had to call 3 separate enrichment APIs (Clearbit for firmographics, ZoomInfo for contacts, manual Google search for context). Total latency: 8 seconds per prospect. Monthly cost: $500+ across three vendors. The agent often failed when one API was slow or down.
After
After: The same agent runs parallel Scavio searches across 3 platforms in 2 seconds. Monthly cost: $45 for 9K enrichment queries (3 platforms x 3K prospects). Enrichment success rate: 99%+ vs 85% with the three-vendor stack.
Who It Is For
Sales engineering teams building AI SDRs and automated outreach agents. Anyone replacing manual prospect research with agent-native API workflows.
Key Benefits
- Enrich 3K prospects/mo across 3 platforms for $45 total
- 2-second parallel enrichment vs 8-second sequential multi-vendor calls
- Single API key replaces 3 vendor accounts and 3 billing relationships
- Google Maps adds office location, reviews, and business details
- Reddit search reveals brand sentiment and customer pain points
Python Example
import requests
from concurrent.futures import ThreadPoolExecutor
API_KEY = "your_scavio_api_key"
def search_platform(query: str, platform: str) -> dict:
r = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": platform, "query": query},
timeout=10,
)
return {"platform": platform, "data": r.json()}
def enrich_prospect(company: str) -> dict:
queries = [
(f"{company} company overview", "google"),
(f"{company} office", "google_maps"),
(f"{company} reviews complaints", "reddit"),
]
with ThreadPoolExecutor(max_workers=3) as pool:
results = list(pool.map(lambda q: search_platform(*q), queries))
return {r["platform"]: r["data"] for r in results}
profile = enrich_prospect("Acme Corp")
for platform, data in profile.items():
print(f"{platform}: {len(data.get("organic", []))} results")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function searchPlatform(query, platform) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform, query }),
});
return { platform, data: await res.json() };
}
async function enrichProspect(company) {
const queries = [
[`${company} company overview`, "google"],
[`${company} office`, "google_maps"],
[`${company} reviews complaints`, "reddit"],
];
const results = await Promise.all(queries.map(([q, p]) => searchPlatform(q, p)));
return Object.fromEntries(results.map(r => [r.platform, r.data]));
}
const profile = await enrichProspect("Acme Corp");
for (const [platform, data] of Object.entries(profile)) {
console.log(`${platform}: ${(data.organic || []).length} results`);
}Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Google Maps
Local business search with ratings and contact info
Community, posts & threaded comments from any subreddit