Scavio for Privacy-First Local Agent

The Problem

Enterprise and privacy-conscious users want AI agents that don't send conversation data to cloud LLM providers. Local inference + cloud search is the minimal trust boundary: search queries are less sensitive than full conversations.

How Scavio Helps

• Conversation data stays on local hardware
• Only search queries (typically short, factual) leave the machine
• Minimal trust boundary: search API sees queries, not context
• Works with any local model: Qwen, Llama, Mistral, Phi
• Scavio MCP works with local runtimes that support MCP protocol

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "User asks sensitive legal question → local Llama generates search query → Scavio search (only the search query leaves the machine) → inject results → local Llama answers with citations → zero cloud LLM exposure":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

Built for Enterprise security teams, privacy-conscious developers, regulated industries (healthcare, legal, finance), GDPR-compliant AI products

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your privacy-first local agent solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.