Jobs to Be Done
- Monitor category narrative across news, search, and creator content
- Spot competitor pricing, positioning, and launch shifts early
- Validate new wedge ideas before committing engineering resources
- Measure own brand traction in organic and branded search
- Feed weekly insight digests to investors and the leadership team
Common Workflows
Weekly category digest
Every Sunday a script calls Scavio for 50 category keywords across Google News, YouTube, and Amazon, summarizes notable shifts with an LLM, and emails the founder a two-page Monday briefing covering narrative, competitor moves, and interesting creators.
Example: scavio.multi(['google_news','youtube','amazon'], kws) -> llm.summarize -> email
Investor update feed
Scavio tracks brand mentions in Google News and YouTube weekly. Counts, sentiment, and top coverage links flow into a Notion doc that auto-renders the next investor update section on brand momentum and category heat.
Example: scavio.google_news('"Acme"', recency='7d') + scavio.youtube('Acme') -> notion
Wedge opportunity scan
When the team debates a new wedge, the founder runs a 1-hour Scavio sprint hitting 200 adjacent queries across Search and YouTube. The output shows demand, existing solutions, and content gaps, enough signal to kill or fund a prototype.
Example: scavio.batch('wedge_queries.csv', platforms=['google','youtube'])
Pain Points Scavio Solves
- Too many disconnected tools to get one market view
- Consultant research reports arrive months out of date
- Hard to quickly sanity-check claims from team or investors
- Weekly intel work eats founder time that should go to customers
Tools Founders Pair With Scavio
Notion, Slack, Superhuman, ChatGPT, Linear, Pitch. Scavio returns structured JSON that fits into any of these tools.
Quick Start
import requests
response = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": "your_scavio_api_key"},
json={"query": "scavio.google_news('vertical ai agents', recency='7d', country='us')"},
)
data = response.json()
# Analyze results for your workflow
for result in data.get("organic_results", [])[:10]:
print(result["title"], "-", result["link"])Platforms You Will Use
Web search with knowledge graph, PAA, and AI overviews
Google News
News search with headlines and sources
YouTube
Video search with transcripts and metadata
Amazon
Product search with prices, ratings, and reviews
Community, posts & threaded comments from any subreddit