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 (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.