Content Ideation from Real Search Signals

Content ideation based on brainstorming produces mediocre topics. Content ideation based on search signals produces topics with proven demand. The difference: when you check what people actually search for on Google, ask about on Reddit, and watch on YouTube, you find specific questions and gaps that generic brainstorming misses entirely.

Three-platform research

Google shows what ranks and what gaps exist in the SERP landscape. Reddit shows what questions people ask and what pain points they have. YouTube shows what video content exists and where there are gaps. Combining all three gives you a complete picture of demand that any single platform misses.

import requests, os

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

def research_topic(topic: str) -> dict:
    data = {}
    for platform in ['google', 'reddit', 'youtube']:
        resp = requests.post('https://api.scavio.dev/api/v1/search',
            headers=H, json={'platform': platform, 'query': topic}, timeout=10)
        data[platform] = [r['title'] for r in resp.json().get('organic', [])[:10]]
    return data

signals = research_topic('CRM for small business')
# Google: what articles rank
# Reddit: what questions people ask
# YouTube: what tutorials exist

Finding gaps, not duplicating what exists

The goal is not to list what already exists. It is to find what does not exist but should. If Google shows 10 "best CRM" listicles but Reddit has 50 threads asking "how to migrate from spreadsheets to a CRM," the gap is a migration guide, not another listicle. Reddit questions that have no corresponding Google content are your highest-value content opportunities.

Feed signals into an LLM for synthesis

Take the raw search results from all three platforms. Feed them into an LLM with a prompt like: "Based on these demand signals, generate 5 content ideas. Each must address a specific gap. Include a suggested title and format." The LLM synthesizes patterns across platforms that manual review would take hours to spot. The search data grounds the LLM in real demand instead of letting it brainstorm generic topics.

Cost of research-driven ideation

A thorough research session: 10 topic areas, 3 platforms each, 30 API calls. Cost: $0.15. Compare to hours of manual research or $100+ per month for content research tools like Semrush. The API approach gives you raw data; you provide the strategic thinking.