Solution

Marketing Agent Data Layer

AI marketing agents keep failing at the same thing: they reason well but the data layer is flat web summaries, stale SERP snapshots, or sparse Reddit threads. Agents trained on Tav

The Problem

AI marketing agents keep failing at the same thing: they reason well but the data layer is flat web summaries, stale SERP snapshots, or sparse Reddit threads. Agents trained on Tavily-style summaries miss the SERP structure (AI overviews, PAA, knowledge graph) that professional marketers use.

The Scavio Solution

Scavio is the structured data layer for marketing agents. Plug one API into any agent framework (LangChain, CrewAI, MCP, n8n) and get Google SERP with AI overviews, YouTube transcripts, Reddit threads, and competitor product pages in one uniform schema. The agent reasons over rich structure, not flat summaries.

Before

Tavily summaries produce generic agent output; teams build bespoke scrapers to fill the gap.

After

One Scavio key powers competitor monitoring, ad copy extraction, brand mention tracking, and AI-overview visibility checks.

Who It Is For

Marketing engineers and growth teams building AI agents for competitor monitoring, ad analysis, and brand visibility tracking.

Key Benefits

  • AI overviews, PAA, knowledge graph in structured JSON
  • YouTube transcripts for creator context
  • Reddit brand mention monitoring
  • Framework-native (LangChain, CrewAI, MCP, n8n)
  • Predictable credits per agent run

Python Example

Python
import os, requests
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}

def brand_pulse(brand):
    serp = requests.post('https://api.scavio.dev/api/v1/search',
        headers=H, json={'query': brand, 'include_ai_overview': True}).json()
    rdt = requests.post('https://api.scavio.dev/api/v1/search',
        headers=H, json={'platform': 'reddit', 'query': brand}).json()
    return {
        'ai_overview': serp.get('ai_overview'),
        'knowledge_graph': serp.get('knowledge_graph'),
        'reddit_mentions': len(rdt.get('posts', []))
    }

JavaScript Example

JavaScript
const H = { 'x-api-key': process.env.SCAVIO_API_KEY, 'content-type': 'application/json' };

async function brandPulse(brand) {
  const [serp, rdt] = await Promise.all([
    fetch('https://api.scavio.dev/api/v1/search', {
      method: 'POST', headers: H,
      body: JSON.stringify({ query: brand, include_ai_overview: true })
    }).then(r => r.json()),
    fetch('https://api.scavio.dev/api/v1/search', {
      method: 'POST', headers: H,
      body: JSON.stringify({ platform: 'reddit', query: brand })
    }).then(r => r.json())
  ]);
  return {
    aiOverview: serp.ai_overview,
    knowledgeGraph: serp.knowledge_graph,
    redditMentions: (rdt.posts || []).length
  };
}

Platforms Used

Google

Web search with knowledge graph, PAA, and AI overviews

YouTube

Video search with transcripts and metadata

Reddit

Community, posts & threaded comments from any subreddit

Frequently Asked Questions

AI marketing agents keep failing at the same thing: they reason well but the data layer is flat web summaries, stale SERP snapshots, or sparse Reddit threads. Agents trained on Tavily-style summaries miss the SERP structure (AI overviews, PAA, knowledge graph) that professional marketers use.

Scavio is the structured data layer for marketing agents. Plug one API into any agent framework (LangChain, CrewAI, MCP, n8n) and get Google SERP with AI overviews, YouTube transcripts, Reddit threads, and competitor product pages in one uniform schema. The agent reasons over rich structure, not flat summaries.

Marketing engineers and growth teams building AI agents for competitor monitoring, ad analysis, and brand visibility tracking.

Yes. Scavio's free tier includes 500 credits per month with no credit card required. That is enough to validate this solution in your workflow.

Marketing Agent Data Layer

Scavio is the structured data layer for marketing agents. Plug one API into any agent framework (LangChain, CrewAI, MCP, n8n) and get Google SERP with AI overviews, YouTube transcr