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How to Build a Marketing Research Agent in 2026

Stitch-free marketing research agent: SERP, Reddit, YouTube under one credit pool. Drops into n8n or Claude Code.

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An r/MarketingandAI thread complained that AI marketing agents 'reorganize the same limitations.' The fix is to separate the data layer from the orchestration layer. This tutorial wires Scavio as a marketing research agent's data layer.

Prerequisites

  • Python 3.10+
  • Scavio API key
  • Anthropic or OpenAI key

Walkthrough

Step 1: Define competitor + topic set

5-10 competitors, 5-15 topics.

Python
COMPETITORS = ['firecrawl', 'tavily', 'serper']
TOPICS = ['mcp server', 'ai agent search', 'web scraping 2026']

Step 2: Daily competitor digest

SERP + Reddit + YouTube per competitor.

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

def competitor_digest(name):
    return {
        'serp': requests.post('https://api.scavio.dev/api/v1/search', headers=H, json={'query': name}).json(),
        'reddit': requests.post('https://api.scavio.dev/api/v1/reddit/search', headers=H, json={'query': name}).json(),
        'youtube': requests.post('https://api.scavio.dev/api/v1/youtube/search', headers=H, json={'query': name}).json(),
    }

Step 3: Topic-level visibility tracking

Where does each competitor show up?

Python
def topic_share(topic):
    r = requests.post('https://api.scavio.dev/api/v1/search', headers=H, json={'query': topic, 'include_ai_overview': True}).json()
    return r

Step 4: LLM brief composition

Claude turns raw data into 200-word brief.

Python
import anthropic
client = anthropic.Anthropic()

def brief(competitor, data):
    msg = client.messages.create(model='claude-sonnet-4-6', max_tokens=400,
        messages=[{'role':'user','content':f'200-word competitor brief on {competitor}: {str(data)[:6000]}'}])
    return msg.content[0].text

Step 5: Schedule weekly

n8n cron or simple crontab.

Bash
# 0 8 * * 1 /usr/bin/python /path/to/marketing.py

Python Example

Python
# Weekly run: 10 competitors × 3 surfaces + 15 topics = 45 credits = $0.19. Negligible at the Project tier.

JavaScript Example

JavaScript
// TS version uses the Anthropic SDK.

Expected Output

JSON
Weekly digest with per-competitor 200-word brief plus topic-level visibility share. Drops into Slack or email.

Related Tutorials

  • How to Track Whether Your SaaS Shows Up in AI Search
  • How to Build an AI Visibility Audit Pipeline

Frequently Asked Questions

Most developers complete this tutorial in 15 to 30 minutes. You will need a Scavio API key (free tier works) and a working Python or JavaScript environment.

Python 3.10+. Scavio API key. Anthropic or OpenAI key. A Scavio API key gives you 50 free credits on signup.

Yes. The free tier includes 50 credits on signup, which is more than enough to complete this tutorial and prototype a working solution.

Scavio has a native LangChain package (langchain-scavio), an MCP server, and a plain REST API that works with any HTTP client. This tutorial uses the raw REST API, but you can adapt to your framework of choice.

Related Resources

Use Case

Marketing Agent Research API

Read more
Solution

Marketing Agent Data Layer

Read more
Use Case

Marketing Agent Competitive Research

Read more
Best Of

Best AI Deep Research Agent Stack in 2026

Read more
Best Of

Best Research Skills for Claude Code (2026)

Read more
Solution

AI Marketing Agent Data Layer

Read more

Start Building

Stitch-free marketing research agent: SERP, Reddit, YouTube under one credit pool. Drops into n8n or Claude Code.

Get Free API KeyRead the Docs
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