# How Profound helps brands win AI Search with high-quality web research and content creation powered by Parallel

By integrating Parallel's Search and Deep Research APIs, Profound, the marketing platform for the AI era, enables marketing teams to create factually grounded, high-quality content that earns citations from AI answer engines like ChatGPT, Perplexity, Claude, and Gemini. Profound powers brands including MongoDB, Ramp, Docusign, Gamma, Zapier, Indeed, and more.

Tags:Case Study
Reading time: 4 min
Profound + Parallel Web Systems

## Key highlights

  • - Improvement in content citation rates across AI answer engines
  • - Web research-grounded content generation reduced from days to minutes
  • - Factual accuracy verified automatically via Parallel web search

## The rise of AI search and a new kind of content problem

AI answer engines are reshaping how consumers discover products and brands. Instead of just clicking through a list of links, people now ask ChatGPT and Claude their questions and AI responds with an answer citing a handful of sources. Getting mentioned in those answers is the future of discovery.

Profound built the category-defining platform for Answer Engine Optimization, helping enterprise brands monitor their AI visibility, understand what AI is saying about them, and take action to improve their presence. Brands like Ramp have used Profound to boost AI visibility by 7x.

Monitoring is only half of the equation, though. To actually get cited by AI, brands need to publish content that’s comprehensive, factually accurate, and structured in ways that answer engines favor, known in the SEO world as EEAT[EEAT](https://developers.google.com/search/docs/fundamentals/creating-helpful-content) (Experience, Expertise, Authoritativeness, and Trustworthiness). Creating EEAT content at scale with the necessary depth and accuracy for ranking previously required deep research, dedicated writing teams, and long turnaround times.

> _"Before we built Profound Agents, our customers used our data to get an audit of exactly where their AI visibility could be improved, but it took weeks to produce the well-researched, authoritative content that answer engines actually cite. To close this gap between insight and action, we launched Profound Agents that can perform deep research and write high-quality, AEO-optimized content in minutes."_

> — Dylan Babbs, Co-founder, Profound

## From visibility monitoring to automated content agents

Profound has expanded beyond visibility analytics into automated AEO agents that help marketers generate high-quality, citation-worthy content. To power these agents, Profound needed reliable infrastructure to retrieve accurate information from the web for conducting deep, multi-source research on any topic.

Profound integrated Parallel’s Search and Task APIs into their Agents workflow platform to offer two core web search nodes:

Parallel Nodes in Profound Agents

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/27e791465c03f1deb12eeb8c180c60662faf2422-1894x1562.png)

**Web Search (Search API) node for fact-checking and grounding**

Marketers use Parallel-powered search to verify claims in AI-generated content, quickly gather supporting data points for articles in progress, and to ensure every piece of published content is factually correct. When a content agent generates a draft, Parallel search validates the claims before anything goes live.

**Deep Research (Task API) node for comprehensive topic research**

For content that needs to be genuinely comprehensive, the kind of in-depth material most likely to be cited by answer engines, Profound uses Parallel’s Task API to conduct multi-source investigations on the given topic. A typical agent combines Profound data and brand-specific inputs with Parallel's deep research output, then uses an LLM to synthesize both into a polished, authoritative article grounded in accurate and fresh web data.

Parallel Deep Research node in Profound Agents

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/5af4e5657660001df6d0714b1b552d283fdc3191-1532x1080.gif)

In addition to being available for custom Agents, Parallel's deep research (Task API) is the web search and synthesis engine behind Profound's Content workflow, which guides users through creating high-quality, well-researched, and targeted AEO content in just minutes.

Profound Content workflow using Parallel deep research

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/3dbe9f2ad5d6495fd1a42414172360dfc1593417-1700x1080.gif)

## Why Profound chose Parallel

Profound tested multiple web search providers before selecting Parallel based on the quality of the outputs their agents were able to produce:

_"We were excited to partner with Parallel as our preferred search API because of the comprehension and accuracy of their outputs. The research came back structured, well-sourced, and ready to feed directly into our content generation agents. They provided more than just surface-level results and avoided hallucination”._

## Powering the next era of AI visibility

The combination of Profound's AEO platform and Parallel's web intelligence infrastructure creates a closed loop: monitor how AI talks about your brand, identify content opportunities, generate factually grounded content at scale, and measure the impact on AI citations and visibility.

> _"What we’ve built with Parallel is the full AEO workflow — from identifying where a brand is underperforming in AI search, to automatically producing the kind of deeply researched content that answer engines want to cite, to measuring the lift. That loop didn't exist before because the research step was the bottleneck. Parallel removed it.”_

For Profound's clients, this means faster content production, higher factual accuracy, and ultimately more revenue driven by AI search visibility. Comprehensive content that is grounded in real, verifiable research is exactly the kind of material that answer engines prefer to cite, and with Parallel, Profound can produce it at the speed and scale that marketing teams need to stay competitive.



Parallel avatar

By Parallel

March 4, 2026

## Related Posts49

How Harvey is expanding legal AI internationally with Parallel

- [How Harvey is expanding legal AI internationally with Parallel](https://parallel.ai/blog/case-study-harvey)

Tags:Case Study
Reading time: 3 min
Tabstack + Parallel Case Study

- [How Tabstack by Mozilla enables agents to navigate the web with Parallel’s best-in-class web search](https://parallel.ai/blog/case-study-tabstack)

Tags:Case Study
Reading time: 5 min
Parallel | Vercel

- [Parallel Web Tools and Agents now available across Vercel AI Gateway, AI SDK, and Marketplace](https://parallel.ai/blog/vercel)

Tags:Product Release
Reading time: 3 min
Product release: Authenticated page access for the Parallel Task API

- [Authenticated page access for the Parallel Task API](https://parallel.ai/blog/authenticated-page-access)

Tags:Product Release
Reading time: 3 min
Introducing structured outputs for the Monitor API

- [Introducing structured outputs for the Monitor API](https://parallel.ai/blog/structured-outputs-monitor)

Tags:Product Release
Reading time: 3 min
Product release: Research Models with Basis for the Parallel Chat API

- [Introducing research models with Basis for the Parallel Chat API](https://parallel.ai/blog/research-models-chat)

Tags:Product Release
Reading time: 2 min
Parallel + Cerebras

- [Build a real-time fact checker with Parallel and Cerebras](https://parallel.ai/blog/cerebras-fact-checker)

Tags:Cookbook
Reading time: 5 min
DeepSearch QA: Task API

- [Parallel Task API achieves state-of-the-art accuracy on DeepSearchQA](https://parallel.ai/blog/deepsearch-qa)

Tags:Benchmarks
Reading time: 3 min
Product release: Granular Basis

- [Introducing Granular Basis for the Task API](https://parallel.ai/blog/granular-basis-task-api)

Tags:Product Release
Reading time: 3 min
How Amp’s coding agents build better software with Parallel Search

- [How Amp’s coding agents build better software with Parallel Search](https://parallel.ai/blog/case-study-amp)

Tags:Case Study
Reading time: 3 min
Latency improvements on the Parallel Task API

- [Latency improvements on the Parallel Task API ](https://parallel.ai/blog/task-api-latency)

Tags:Product Release
Reading time: 3 min
Product release: Extract

- [Introducing Parallel Extract](https://parallel.ai/blog/introducing-parallel-extract)

Tags:Product Release
Reading time: 2 min
FindAll API - Product Release

- [Introducing Parallel FindAll](https://parallel.ai/blog/introducing-findall-api)

Tags:Product Release,Benchmarks
Reading time: 4 min
Product release: Monitor API

- [Introducing Parallel Monitor](https://parallel.ai/blog/monitor-api)

Tags:Product Release
Reading time: 3 min
Parallel raises $100M Series A to build web infrastructure for agents

- [Parallel raises $100M Series A to build web infrastructure for agents](https://parallel.ai/blog/series-a)

Tags:Fundraise
Reading time: 3 min
How Macroscope reduced code review false positives with Parallel

- [How Macroscope reduced code review false positives with Parallel](https://parallel.ai/blog/case-study-macroscope)

Reading time: 2 min
Product release - Parallel Search API

- [Introducing Parallel Search](https://parallel.ai/blog/introducing-parallel-search)

Tags:Benchmarks
Reading time: 7 min
Benchmarks: SealQA: Task API

- [Parallel processors set new price-performance standard on SealQA benchmark](https://parallel.ai/blog/benchmarks-task-api-sealqa)

Tags:Benchmarks
Reading time: 3 min
Introducing LLMTEXT, an open source toolkit for the llms.txt standard

- [Introducing LLMTEXT, an open source toolkit for the llms.txt standard](https://parallel.ai/blog/LLMTEXT-for-llmstxt)

Tags:Product Release
Reading time: 7 min
Starbridge + Parallel

- [How Starbridge powers public sector GTM with state-of-the-art web research](https://parallel.ai/blog/case-study-starbridge)

Tags:Case Study
Reading time: 4 min
Building a market research platform with Parallel Deep Research

- [Building a market research platform with Parallel Deep Research](https://parallel.ai/blog/cookbook-market-research-platform-with-parallel)

Tags:Cookbook
Reading time: 4 min
How Lindy brings state-of-the-art web research to automation flows

- [How Lindy brings state-of-the-art web research to automation flows](https://parallel.ai/blog/case-study-lindy)

Tags:Case Study
Reading time: 3 min
Introducing the Parallel Task MCP Server

- [Introducing the Parallel Task MCP Server](https://parallel.ai/blog/parallel-task-mcp-server)

Tags:Product Release
Reading time: 4 min
Introducing the Core2x Processor for improved compute control on the Task API

- [Introducing the Core2x Processor for improved compute control on the Task API](https://parallel.ai/blog/core2x-processor)

Tags:Product Release
Reading time: 2 min
How Day AI merges private and public data for business intelligence

- [How Day AI merges private and public data for business intelligence](https://parallel.ai/blog/case-study-day-ai)

Tags:Case Study
Reading time: 4 min
Full Basis framework for all Task API Processors

- [Full Basis framework for all Task API Processors](https://parallel.ai/blog/full-basis-framework-for-task-api)

Tags:Product Release
Reading time: 2 min
Building a real-time streaming task manager with Parallel

- [Building a real-time streaming task manager with Parallel](https://parallel.ai/blog/cookbook-sse-task-manager-with-parallel)

Tags:Cookbook
Reading time: 5 min
How Gumloop built a new AI automation framework with web intelligence as a core node

- [How Gumloop built a new AI automation framework with web intelligence as a core node](https://parallel.ai/blog/case-study-gumloop)

Tags:Case Study
Reading time: 3 min
Introducing the TypeScript SDK

- [Introducing the TypeScript SDK](https://parallel.ai/blog/typescript-sdk)

Tags:Product Release
Reading time: 1 min
Building a serverless competitive intelligence platform with MCP + Task API

- [Building a serverless competitive intelligence platform with MCP + Task API](https://parallel.ai/blog/cookbook-competitor-research-with-reddit-mcp)

Tags:Cookbook
Reading time: 6 min
Introducing Parallel Deep Research reports

- [Introducing Parallel Deep Research reports](https://parallel.ai/blog/deep-research-reports)

Tags:Product Release
Reading time: 2 min
BrowseComp / DeepResearch: Task API

- [A new pareto-frontier for Deep Research price-performance](https://parallel.ai/blog/deep-research-benchmarks)

Tags:Benchmarks
Reading time: 4 min
Building a Full-Stack Search Agent with Parallel and Cerebras

- [Building a Full-Stack Search Agent with Parallel and Cerebras](https://parallel.ai/blog/cookbook-search-agent)

Tags:Cookbook
Reading time: 5 min
Webhooks for the Parallel Task API

- [Webhooks for the Parallel Task API](https://parallel.ai/blog/webhooks)

Tags:Product Release
Reading time: 2 min
Introducing Parallel: Web Search Infrastructure for AIs

- [Introducing Parallel: Web Search Infrastructure for AIs ](https://parallel.ai/blog/introducing-parallel)

Tags:Benchmarks,Product Release
Reading time: 6 min
Introducing SSE for Task Runs

- [Introducing SSE for Task Runs](https://parallel.ai/blog/sse-for-tasks)

Tags:Product Release
Reading time: 2 min
A new line of advanced Processors: Ultra2x, Ultra4x, and Ultra8x

- [A new line of advanced Processors: Ultra2x, Ultra4x, and Ultra8x ](https://parallel.ai/blog/new-advanced-processors)

Tags:Product Release
Reading time: 2 min
Introducing Auto Mode for the Parallel Task API

- [Introducing Auto Mode for the Parallel Task API](https://parallel.ai/blog/task-api-auto-mode)

Tags:Product Release
Reading time: 1 min
A linear dithering of a search interface for agents

- [A state-of-the-art search API purpose-built for agents](https://parallel.ai/blog/search-api-benchmark)

Tags:Benchmarks
Reading time: 3 min
Parallel Search MCP Server in Devin

- [Parallel Search MCP Server in Devin](https://parallel.ai/blog/parallel-search-mcp-in-devin)

Tags:Product Release
Reading time: 2 min
Introducing Tool Calling via MCP Servers

- [Introducing Tool Calling via MCP Servers](https://parallel.ai/blog/mcp-tool-calling)

Tags:Product Release
Reading time: 2 min
Introducing the Parallel Search MCP Server

- [Introducing the Parallel Search MCP Server ](https://parallel.ai/blog/search-mcp-server)

Tags:Product Release
Reading time: 2 min
Starting today, Source Policy is available for both the Parallel Task API and Search API - giving you granular control over which sources your AI agents access and how results are prioritized.

- [Introducing Source Policy](https://parallel.ai/blog/source-policy)

Tags:Product Release
Reading time: 1 min
The Parallel Task Group API

- [The Parallel Task Group API](https://parallel.ai/blog/task-group-api)

Tags:Product Release
Reading time: 1 min
State of the Art Deep Research APIs

- [State of the Art Deep Research APIs](https://parallel.ai/blog/deep-research)

Tags:Benchmarks
Reading time: 3 min
Introducing the Parallel Search API

- [Parallel Search API is now available in alpha](https://parallel.ai/blog/parallel-search-api)

Tags:Product Release
Reading time: 2 min
Introducing the Parallel Chat API - a low latency web research API for web based LLM completions. The Parallel Chat API returns completions in text and structured JSON format, and is OpenAI Chat Completions compatible.

- [Introducing the Parallel Chat API ](https://parallel.ai/blog/chat-api)

Tags:Product Release
Reading time: 1 min
Parallel Web Systems introduces Basis with calibrated confidences - a new verification framework for AI web research and search API outputs that sets a new industry standard for transparent and reliable deep research.

- [Introducing Basis with Calibrated Confidences ](https://parallel.ai/blog/introducing-basis-with-calibrated-confidences)

Tags:Product Release
Reading time: 4 min
The Parallel Task API is a state-of-the-art system for automated web research that delivers the highest accuracy at every price point.

- [Introducing the Parallel Task API](https://parallel.ai/blog/parallel-task-api)

Tags:Product Release,Benchmarks
Reading time: 4 min
![Company Logo](https://parallel.ai/parallel-logo-540.png)

Contact

  • hello@parallel.ai[hello@parallel.ai](mailto:hello@parallel.ai)

Products

  • Search API[Search API](https://docs.parallel.ai/search/search-quickstart)
  • Extract API[Extract API](https://docs.parallel.ai/extract/extract-quickstart)
  • Task API[Task API](https://docs.parallel.ai/task-api/task-quickstart)
  • FindAll API[FindAll API](https://docs.parallel.ai/findall-api/findall-quickstart)
  • Chat API[Chat API](https://docs.parallel.ai/chat-api/chat-quickstart)
  • Monitor API[Monitor API](https://docs.parallel.ai/monitor-api/monitor-quickstart)

Resources

  • About[About](https://parallel.ai/about)
  • Pricing[Pricing](https://parallel.ai/pricing)
  • Docs[Docs](https://docs.parallel.ai)
  • Blog[Blog](https://parallel.ai/blog)
  • Changelog[Changelog](https://docs.parallel.ai/resources/changelog)
  • Careers[Careers](https://jobs.ashbyhq.com/parallel)

Info

  • Terms of Service[Terms of Service](https://parallel.ai/terms-of-service)
  • Customer Terms[Customer Terms](https://parallel.ai/customer-terms)
  • Privacy[Privacy](https://parallel.ai/privacy-policy)
  • Acceptable Use[Acceptable Use](https://parallel.ai/acceptable-use-policy)
  • Trust Center[Trust Center](https://trust.parallel.ai/)
![SOC 2 Compliant](https://parallel.ai/soc2.svg)
LinkedIn[LinkedIn](https://www.linkedin.com/company/parallel-web/about/)Twitter[Twitter](https://x.com/p0)GitHub[GitHub](https://github.com/parallel-web)
All Systems Operational

Parallel Web Systems Inc. 2026