Jobs to Be Done
- Feed fresh SERP and product data into feature stores on a schedule
- Serve real-time search as a retrieval tool for RAG and agent applications
- Backfill evaluation sets when a new model candidate needs benchmarking
- Monitor production model outputs against live search ground truth
- Handle retries, concurrency, and schema stability so upstream scraping never breaks training
Common Workflows
Feature store hydration job
Run a nightly Airflow DAG that hits Scavio for the top 10K tracked queries, normalizes results into a Feast feature view, and publishes features to online and offline stores so ranking models always train and serve on the same SERP snapshot.
Example: airflow dag: scavio.batch(queries) -> feast.ingest(feature_view='serp_features_v3')
Retrieval backend for RAG services
Expose Scavio behind an internal gRPC retrieval service that LLM apps call when their vector store misses. The service caches hot queries in Redis, falls back to Scavio for cold ones, and returns normalized passages ready to pass into a generation step.
Example: grpc retrieve(query) -> redis.get or scavio.google(query).organic -> chunk -> context
Continuous evaluation harness
Every deploy triggers an eval suite that re-runs 2K held-out queries through the production LLM and grades answers against fresh Scavio SERPs using an LLM judge, posting regressions to a dashboard so the team catches accuracy drops before customers do.
Example: on deploy: for q in evalset: judge(model(q), scavio.google(q).snippets)
Canary drift detection
A sidecar service samples 1 percent of production queries, compares current model outputs against Scavio results, and alerts when semantic divergence crosses a threshold. This surfaces silent data drift that batch metrics miss.
Example: sample(prod_queries, 0.01) -> cosine(embed(model.out), embed(scavio.snippet))
Pain Points Scavio Solves
- Upstream scrapers fail silently and corrupt training sets for days
- Maintaining a scraping fleet steals time from actual model work
- Inconsistent JSON schemas from free tools break pipeline contracts
- Throughput caps stop large-scale eval runs from finishing in time
Tools ML Engineers Pair With Scavio
Airflow, Feast, Ray, Redis, Kubernetes, Weights and Biases. 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.batch(queries=top_10k, platform='google', concurrency=64)"},
)
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
Google News
News search with headlines and sources
Amazon
Product search with prices, ratings, and reviews
Community, posts & threaded comments from any subreddit