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Perplexity challenges Google and Microsoft with new AI tools for code and content

The news: AI search startup Perplexity has expanded its offerings with the launch of Perplexity Labs, a suite of enterprise AI tools designed for code generation, app development, and marketing campaign creation. 

Available to Pro subscribers ($20/month), Perplexity Labs can be used to generate reports, spreadsheets, dashboards, and web apps in minutes using AI-driven search, code execution, and data visualization. Pro subscribers receive 50 Labs queries per month.

Along with recent content partnerships, acquisitions, and upcoming products (Comet browser), Perplexity’s non-search expansion suggests broader ambitions.

From search to enterprise services: Perplexity’s move beyond search into general AI services positions it to challenge major players in enterprise automation. Its new features could also deepen adoption among the 87% of ad professionals already using AI tools, according to the IAB.

  • Key competitors include Google (Gemini, Vertex AI) for AI-driven analytics, Microsoft (Copilot) for workplace automation, and specialized platforms like Notion for document generation and OpenAI for advanced AI models.
  • Perplexity’s advantage lies in combining search, data structuring, and file creation in one workflow—but it must prove reliability to contend with incumbents.

Pricing and uniformity are pros: By bundling AI search, real-time code execution, and file creation into one tool, Labs reduce the need to switch platforms—streamlining enterprise productivity for startups and teams that lack deep technical expertise.

Query limitations and lack of interoperability are cons: The 50-query-per-month cap may hinder enterprise adoption, while the lack of compatibility with existing business tools might make it less appealing than services like Salesforce’s Agentforce.

Our take: Perplexity Labs is a promising step toward AI-powered productivity, especially for lean teams seeking fast, low-cost automation. But query caps and limited integration indicate it might not be able to meet the demands of enterprise use at scale.  

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