# How AirOps creates citation-worthy content at scale, powered by Parallel

## **Key highlights**

• - Parallel is the web data layer across AirOps, with Task, Search, and Monitor APIs powering steps inside the platform's Workflows and Playbooks.
• - Deep web research from Parallel composes with each brand's voice, internal context, and proprietary data to produce content optimized for answer engines.
• - Search powers citation sourcing during drafting and fact-checking before publish, so claims are verified against the live web in the same workflow that produces them.
• - Monitor watches the web for relevant changes and triggers content refreshes automatically, keeping published pages accurate as source material shifts.

## **About AirOps**

AirOps is a content engineering platform that combines brand knowledge, human review, and AI workflows so marketing teams can run content as a continuously maintained system rather than a series of one-off projects. AirOps powers content programs at brands including Webflow, Chime, Ramp, Carta, Apollo, and Docebo.

## A second wave of search demands a new content system

The way people discover brands has changed. A user asks ChatGPT, Claude, Perplexity, or Gemini a question and gets a synthesized answer drawn from a handful of cited sources. Earning a place in those answers is the new front line of organic visibility, and it requires content that's comprehensive, factually accurate, and structured the way answer engines reward.

Producing that kind of work used to mean long research cycles, dedicated writing teams, and a heavy fact-checking pass after every draft. Maintaining it as the web changed around you was a harder problem still. Statistics went stale, citations broke, sources got superseded, and visibility decayed between quarterly audits.

AirOps set out to make content engineering a repeatable system. To do that at scale, the platform needed a reliable web data layer underneath every workflow that touches the open web.

**_"Our customers don't just need content fast. They need content that's accurate, deeply researched, and on-brand. Web research had to become a first-class building block inside AirOps for any of that to be true at scale. Parallel gave us a research layer that drops directly into our workflows and produces outputs our agents can build on."_**

**— Amr Shafik, VP of Product, AirOps _(placeholder for customer review)_**

## Parallel web tools and agents help power AirOps

Parallel is the web data provider across the AirOps platform. Wherever an AirOps workflow needs information from the open web, Parallel handles the retrieval, synthesis, and monitoring. The same APIs power custom workflows that AirOps customers build themselves and the core steps inside AirOps Playbooks for blog creation and refresh.

**Deep web research, composed with brand context.** Inside the blog creation Playbook, Parallel's Task API runs deep, multi-source research on the topic. It handles querying, retrieval, and synthesis, returning a structured research brief with citations, reasoning, and calibrated confidence scores via Parallel Basis. AirOps then composes that research with each brand's Brand Kit, internal expertise, and proprietary data to generate articles that are deeply researched and recognizably the brand's. The output is the kind of comprehensive, well-sourced piece that answer engines prefer to cite, produced in a single Playbook run.

**Citations sourced during drafting.** Parallel's Search API powers the citation step inside the same Playbook, surfacing the most relevant URLs for any claim and pulling dense, LLM-ready excerpts directly into the workflow. AirOps customers identify and validate sources as the article comes together, not afterward.

**Fact-checking before publishing.** The blog refresh Playbook uses Parallel Search to verify claims in existing content against the live web. AirOps surfaces out-of-date statistics, broken citations, and superseded sources before anything goes back into production.

**Refresh workflows that trigger themselves.** Parallel's Monitor API powers the trigger for the blog refresh Playbook, watching the web for new research, regulatory changes, competitor updates, and revised product details. When relevant new information appears, the Playbook fires automatically for the affected pages.

AirOps evaluated several web research providers before standardizing on Parallel.

> **_"We tested every major web search provider against the kinds of research our agents actually need to do. Parallel's outputs came back structured, well-cited, and dense with the right information. We could feed them directly into our content workflows without an entire cleanup pass to filter out hallucinations or thin sourcing."_**

> **— Amr Shafik, VP of Product, AirOps _(placeholder for customer review)_**

## The impact

With Parallel underneath the platform, content marketing inside AirOps runs as a continuously maintained system. Every article that ships starts from accurate, well-sourced research, carries the brand's voice and proprietary perspective, and stays accurate as the web changes around it.

For AirOps customers, that means three things they couldn't operationalize before. Research is no longer the bottleneck between visibility insight and published content. Fact-checking happens inside the workflow that produces the article, not as a separate pass after the fact. And refresh stops being a quarterly project, because the workflow notices when source material moves and re-runs itself for the affected pages.

Marketing teams don't win answer engine visibility with one great article. They win it with hundreds of articles that stay accurate over time. Pairing Parallel's research and monitoring with the Brand Kit and human review steps inside AirOps is what makes that operationally possible.