# How Lindy brings state-of-the-art web research to automation flows

## The automation data gap

No-code platforms excel at connecting internal systems but are limited when workflows need high-quality and fresh web data. A lead enrichment automation can pull from your CRM but can't reliably determine if a prospect company just raised Series B funding. A competitive monitoring workflow triggers on Slack mentions but lacks context about actual product changes or pricing updates. Meeting preparation automations schedule calls but don't surface recent leadership transitions or strategic announcements.

_"The web contains massive amounts of valuable data, but accessing it reliably has always been complex and time-consuming" _notes Everett Butler, Head of Marketing at Lindy.

Traditional approaches required high-friction processes like custom scraping scripts, managing rate limits, handling site structure changes, and building extraction logic.

## Processor-based intelligence scaling

Lindy mapped Parallel's Processor architecture directly to workflow complexity, creating a spectrum where users select capability based on task requirements:

• - **Lite/Base Processors** – Simple enrichments like extracting company domains or basic contact information. Single-source lookups that require minimal reasoning.
• - **Core Processor** – Multi-field enrichments across up to 10 output fields. Handles most structured data extraction tasks like gathering company size, funding stage, and tech stack from multiple pages.
• - **Pro and Ultra Processors** – Complex research requiring deep reasoning across sources. Multi-step analysis like synthesizing competitive positioning from product pages, case studies, LinkedIn activity, and news coverage.

_"You just provide your inputs and specify what you need. Parallel's tools in Lindy will perform live web retrieval and reasoning, optimized for top-quality results."_

This Processor selection eliminates the traditional tradeoff between speed and depth—users choose based on their needs.

## Different ways to use Parallel’s tools in Lindy

Lindy used Parallel to create two distinct nodes:

**Chat with Web** offers simple yet powerful grounded LLM chat completions so that users and agents can ask questions and get answers from the open web.

**Web enrichment** enables seamless data collection on user-provided inputs, like a list of companies with dimensions, such as headquarters location or year of founding, that you’d want to fill out in a spreadsheet.

## Results

By integrating Parallel as their web intelligence layer, Lindy eliminated the boundary between internal automation and external data. Workflows that previously stopped at system boundaries now extend across the open web with production-grade reliability.

Users can now build automations that automatically qualify and enrich leads before routing to sales, monitor competitors and update battle cards in real-time, research partnership opportunities, and maintain prospect profiles that update themselves as web data changes.

**Learn more about Parallel**

To get started with Parallel’s suite of web search APIs, check out our documentation[check out our documentation]($https://docs.parallel.ai/home).