Scavio for RAG Accuracy Optimization for Consulting

The Problem

Consulting RAG systems retrieve from knowledge bases indexed weeks ago. Client questions about recent market shifts, competitor moves, or regulatory changes return stale or missing data.

How Scavio Helps

• Hybrid retrieval: vector store + live web search
• Automatic fallback when vector confidence is low
• Time-sensitive query detection triggers web search
• Cost: $0.005 per web search fallback
• Works with Pinecone, Weaviate, Chroma, or any vector store

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "EU AI Act compliance timeline 2026":

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 Consulting firms and research teams building RAG-powered knowledge systems

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your rag accuracy optimization for consulting 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.