Scavio for Meeting Notes to Agent Memory

The Problem

Meeting decisions sit in unstructured transcripts disconnected from AI agent knowledge bases, forcing users to re-explain past decisions to their agent repeatedly.

How Scavio Helps

• Meeting decisions auto-extracted and stored in agent memory
• Web context enrichment adds current data to meeting references
• Agent answers questions about past meetings with cited sources
• Structured entries replace unstructured transcript dumps
• Pipeline cost under $0.10 per meeting for web enrichment

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "meeting transcript to agent knowledge base enrichment API 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 Teams building AI agents that need meeting context, product managers, and engineering leads who want meeting decisions searchable by AI

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

Start with the free tier (250 credits/month, no credit card required) and scale to paid plans when you need higher volume.