# How Gumloop built a new AI automation framework with web intelligence as a core node

Gumloop[Gumloop]($https://www.gumloop.com/) is an AI automation framework that combines data, apps, and AI in a drag and drop interface. With Gumloop, any person regardless of technical ability, can build time-saving workflows that are always grounded in real-world information via the Parallel Task API.

## **Why automation needs web intelligence**

When building any automation, it’s critical to be able to access data on the web. Without the web, a lead scoring model won’t know a prospect just raised funding. A partnerships outreach campaign will reach the wrong person because the decision maker changed jobs. A competitor monitoring database will miss a new product launch.

“Our users needed automation that could research and discover current information, not just process what was already in their systems,” explains Max Brodeur-Urbas, Founder and CEO of Gumloop. “In order to build the sophisticated workflows they wanted, they needed real-time access to real information.”

## **Building web intelligence into AI automation architecture**

When Gumloop evaluated web research solutions, Parallel's Task API stood out for its declarative and structured approach. Instead of defining how to extract data from specific sources, Gumloop workflows simply specify what information they need. "Find the current CTO of this company." "Research recent funding rounds for these startups." "Identify which of these companies use Salesforce." Parallel will then figure out the best way to get the answer and return structured output fields that are always verifiable.

Within the span of days, Gumloop was able to productionize Parallel as a native node in their workflow builder. In Gumloop, users send entity lists and research objectives to the Parallel “AI Web Research” node, which orchestrates web crawling, data extraction, and synthesis. The structured outputs flow directly into subsequent workflow nodes—no manual parsing or transformation required.

## **Gumloop web intelligence workflows in production**

With Parallel powering its web intelligence nodes, Gumloop users have built sophisticated automation workflows that combine internal data, app integrations, AI reasoning, and real-time web research:

**Content generation pipelines** research topics before writing. Marketing teams input topics like "AI adoption in healthcare"— workflows gather current statistics and industry developments. This structured research feeds into AI writing nodes that generate accurate content grounded in real-time web research.

**Competitive intelligence systems** monitor competitor websites, news mentions, and market activities. When Parallel detects relevant changes—pricing updates, new features, leadership changes—Gumloop workflows update internal systems and trigger appropriate responses.

**Dynamic enrichment pipelines** process lists of companies, people, or organizations, researching and extracting specified attributes: revenue figures, employee counts, technology stacks, recent news, or industry-specific data points.

**Intelligent lead qualification** workflows research prospects in real-time before scoring and routing. A sales team can check for recent IT investments, technology migrations, and hiring patterns. High-intent signals trigger immediate sales alerts while others enter appropriate nurture tracks.

**Contextual outreach automation** researches each contact before engagement, gathering recent company news, role changes, or industry developments. This intelligence feeds into message personalization—referencing actual events rather than generic templates.

## **Pushing AI automation forward with Gumloop and Parallel**

Today, thousands of Gumloop workflows rely on Parallel's web intelligence infrastructure, executing millions of research queries monthly. The impact extends beyond efficiency—it's enabling entirely new automation paradigms that weren't possible before.

The partnership between Gumloop and Parallel demonstrates a fundamental shift in automation architecture: from closed systems operating on internal data to open platforms that leverage the entire web as an intelligence layer. As AI automation frameworks continue to evolve, web intelligence isn't just a nice-to-have—it's the foundation that connects automated systems to the ever changing real world.