A structured data API sounds abstract until you see what teams actually run on it. Below are seven pipelines we see in production, each with the endpoints it uses and the shape of the workflow. They all share one property: the hard part (getting clean data out of well-defended sites) is behind the API, so the pipeline itself is small.

# 1. Price and availability monitoring

The classic. A retailer or brand watches its own listings and the surrounding market across marketplaces.

* **Endpoints:** `amazon/product_detail`, `amazon/offers`, `walmart/products`, `ebay/items`
* **Workflow:** a saved task per product set, scheduled daily or hourly, with `freshness: "fresh"` because stale prices defeat the purpose. Batch requests keep large catalogs to a handful of calls, and the run history becomes your price timeline.

We went deep on this pattern in [scraping e-commerce prices at scale](https://capzy.ai/blog/scraping-ecommerce-prices-at-scale).

# 2. Search rank tracking

Where do you, and everyone you compete with, show up for the queries that matter?

* **Endpoints:** `google/search`, `bing/search`, `duckduckgo/search`
* **Workflow:** one scheduled run per keyword set per engine. Each result row carries its position, so rank-over-time is just plotting the same query across daily runs. The [SERP scraping guide](https://capzy.ai/blog/scraping-search-results-serp) covers why doing this against the live engines yourself is harder than it looks.

# 3. Brand and sentiment listening

What are people saying about your product right now, and where?

* **Endpoints:** `twitter/search`, `reddit/search`, `youtube/search` plus comments, `tiktok/search`
* **Workflow:** scheduled searches for your brand terms, new results diffed against the last run, and mentions routed to wherever your team lives. Reddit and YouTube comment threads are where the honest feedback hides.

# 4. Real estate market feeds

Listings, price cuts, and inventory across markets, without an agent portal login.

* **Endpoints:** `zillow/search` and `zillow/property`, `realtor/search`, `leboncoin/search`, `immobiliare/search`, `loopnet/search` for commercial
* **Workflow:** a saved search per market segment on a daily schedule, exported as XLSX for the analysts. Cross-country coverage means one pipeline shape serves the US, France, and Italy teams.

# 5. Marketplace seller intelligence

Who sells against you, what do they list, and how are they rated?

* **Endpoints:** `amazon/seller`, `amazon/seller_products`, `amazon/seller_feedback`, `ebay/sellers`
* **Workflow:** start from the offers on your key listings, resolve the seller IDs, then walk their storefronts on a weekly schedule. This is the pipeline that turns "someone is undercutting us" from a hunch into a table.

# 6. Content and trend research

What is rising, what is peaking, and what should you make next?

* **Endpoints:** `youtube/trends`, `google/trends`, `tiktok/trending`, `reddit/subreddits` and top posts
* **Workflow:** weekly scheduled pulls feeding a research dashboard. Because every endpoint returns the same envelope, adding a platform to the dashboard is one more saved task, not a new integration.

# 7. AI answer monitoring

A new one, and growing fast: when someone asks an AI assistant about your category, does your brand appear, and what does the answer say?

* **Endpoints:** `chatgpt/ask` and the brand-visibility endpoints
* **Workflow:** a scheduled set of category questions, with answers and citations captured as structured records. Marketing teams treat this like rank tracking for the AI era.

# The pattern behind all seven

Look at the workflows again and the same skeleton appears every time:

* **A saved task** defines the pull: platform, action, params, freshness
* **A schedule** runs it on cadence, with batch requests handling volume
* **Freshness modes** decide which reads hit the live site and which reuse recent records
* **History and exports** turn runs into timelines, spreadsheets, and downstream feeds

None of these pipelines needed a scraper, a browser fleet, or a parsing layer. That is the real pitch for a Data API: the moat work (staying unblocked and correctly parsed on major sites, which is Capzy's core business) sits behind the endpoint, and what is left for you is genuinely small.

# Start with one

If one of the seven matched a thing your team already wants, the path is short: find the endpoint in the Data API docs, make one call with `freshness: "auto"`, and check the fields. The first pipeline usually ships in an afternoon, and the second one reuses everything you learned in the first.

What Can You Build With a Data API? Seven Real Pipelines