Jobs to Be Done
- Build labeled datasets from Google, YouTube, and Amazon results for model training
- Feature-engineer SERP signals (position, sitelinks, knowledge panel) for ranking models
- Benchmark embeddings against fresh 2026 search results to detect data drift
- Run ad-hoc exploratory analysis on product reviews, video transcripts, and news corpora
- Cross-validate proprietary data against live public search as a ground-truth layer
Common Workflows
Dataset generation for fine-tuning
Query thousands of long-tail keywords across Google and YouTube, then clean results into a Parquet dataset. Join transcripts with SERP snippets and ship the labeled corpus into a Hugging Face dataset used for domain-specific fine-tuning runs.
Example: for each query in keywords.csv: scavio.google(q).results + scavio.youtube(q).transcripts -> parquet -> s3://datasets/2026-q2/
Query-intent classification features
Pull 50K SERPs, extract feature snippets like shopping carousels, knowledge panels, and people-also-ask boxes, and encode them as categorical features in a gradient-boosted classifier that predicts commercial vs informational intent with much higher recall.
Example: scavio.google('best noise cancelling headphones 2026', device='desktop') -> feature_vector
Review-based churn signal extraction
Ingest Amazon and Walmart reviews for competitor SKUs, run sentiment and topic models, and feed the resulting signals into a churn-prediction pipeline that correlates product complaints with subscription cancellations across a retail client portfolio.
Example: scavio.amazon.reviews(asin='B0X...') -> bertopic -> join(churn_events)
Pain Points Scavio Solves
- Residential proxies and CAPTCHA solvers break mid-experiment and waste GPU time
- Scraped HTML needs constant parser maintenance when Google redesigns the SERP
- Training data goes stale fast when models are retrained every quarter
- Rate limits on homegrown scrapers cap dataset size below what models need
Tools Data Scientists Pair With Scavio
Jupyter, Pandas, DuckDB, Hugging Face, scikit-learn, Airflow. 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('llm evaluation frameworks', num=100, 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
YouTube
Video search with transcripts and metadata
Amazon
Product search with prices, ratings, and reviews
Google News
News search with headlines and sources
Community, posts & threaded comments from any subreddit