Jobs to Be Done
- Embed Google, YouTube, and Amazon search into user-facing features
- Handle concurrency, retries, and rate limits without custom infra
- Return clean JSON the frontend can render directly
- Log, cache, and bill search calls like any other internal service
- Keep integration thin so scraping logic never lives in the product repo
Common Workflows
In-app search feature
A B2B app ships a search bar that returns real Google or Amazon results inside the product. The backend proxies calls through Scavio, applies per-workspace rate limits, and caches hot queries in Redis for 10 minutes to keep unit costs predictable.
Example: GET /api/search?q=... -> cache.get or scavio.google(q) -> cache.set(600)
Background enrichment jobs
When a user imports a list of URLs or products, a worker enqueues Scavio jobs that fetch SERP positions, competitor listings, or video coverage, writes results back to Postgres, and emits events for downstream services with a stable JSON contract.
Example: queue.job(enrich_product, asin) -> scavio.amazon.product(asin) -> pg.update
Webhook-driven alerts
Scavio Watch tracks customer-configured queries and POSTs changes to the product webhook endpoint. The service validates signatures, fans out notifications through email and Slack, and stores history for an audit view without the team operating any scraper infrastructure.
Example: POST /webhooks/scavio -> verify(sig) -> fanout(notify) -> pg.history
Pain Points Scavio Solves
- Running a scraper fleet is a full-time SRE job
- Proxy providers nickel and dime and still fail at peak
- Parsing HTML from four properties means four maintenance burdens
- Latency spikes from scrapers break customer-facing SLAs
Tools Backend Developers Pair With Scavio
Postgres, Redis, Temporal, Sidekiq, Datadog, Sentry. 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('restaurants near Central Park', location='New York')"},
)
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
Amazon
Product search with prices, ratings, and reviews
YouTube
Video search with transcripts and metadata
Walmart
Product search with pricing and fulfillment data