Perplexity targets the enterprise market with Enterprise Pro to help companies research markets and draft product road maps. The startup uses real-time web search and selective source curation to provide answers. Dmitry Shevelenko, Perplexity chief business officer, notes that 40% of all Perplexity user queries involve a follow-up question. The company plans to introduce ads in related questions in at least one or two quarters, though these ads will not appear above answers or allow advertisers to skew results through paid placement.
Perplexity faces significant legal and operational hurdles in its attempt to capture enterprise data. The ongoing Reddit vs. Perplexity lawsuit, which saw a court hearing on July 31, 2026, alleges that Perplexity used scraping intermediaries to gather Reddit data. Reddit claims Perplexity bypassed technical defenses by scraping Google search engine results pages to find content. Judge Paul Engelmayer dismissed motions from Perplexity and SerpApi to close the case, allowing the litigation to proceed. This legal pressure challenges the way Perplexity uses retrieval-augmented generation to pull and summarize public data.
One major failure point in enterprise AI deployment involves the difference between generative models and agentic AI. When an AI agent makes a mistake in an execution flow, the error can result in an irreversible operational action, such as modifying a database record or initiating a transaction. Most enterprise governance programs focus on detection, which identifies errors after a model produces an output. This approach fails when an agent executes a hallucinated intention. If an agent misinterprets a goal or selects an inappropriate tool, a detection layer only provides a notification after the damage occurs.
Glean and Perplexity capability comparison
Glean operates as an enterprise search and agent layer for large companies with knowledge spread across Slack, Confluence, Drive, and Notion. It targets 500-plus seat companies that require unified search and compliance review. Glean uses a knowledge graph built over years to provide answers and actions across SaaS tools. In contrast, Perplexity focuses on a generative search platform that uses real-time web search and human-guided evaluation to improve answer quality.
| Feature | Perplexity Enterprise Pro | Glean |
|---|---|---|
| Primary Use Case | Market research and product road maps | Unified search and employee Q&A |
| Deployment Model | SaaS | SaaS |
| Data Source | Real-time web and selective curation | Internal SaaS knowledge graph |
| Target Audience | Research and media users | 500+ seat companies |
Glean’s competitors include Kestra, Onyx, and Dust. Kestra provides an open-source engine for building custom RAG pipelines using YAML, while Onyx allows for self-hosting to ensure data ownership. Dust provides modular assistant builders for different departments. You should consider if your organization needs a tool that answers questions or a tool that performs scheduled tasks.
Implementation risks and costs
Scaling AI search involves expenses that extend beyond initial licensing. Organizations often spend 40% to 60% of delivery time on data infrastructure and preparation, including document extraction, metadata tagging, and index construction. Perplexity aims to win by building a data moat, but SEO experts note that most companies lack the training data required to compete with giants like Google or Microsoft.
Implementing AI agents introduces unique security and operational challenges. 40% of organizations identify security and compliance as the primary obstacle to scaling agentic AI. Additionally, 48% of respondents cite the difficulty of orchestrating multiple components as a main challenge. Security teams face increased monitoring requirements when integrating models, APIs, and external systems.
| Cost Component | Estimated Scale |
|---|---|
| Basic AI Search Deployment | $15,000 to $40,000 |
| Mid-tier Semantic Search | $40,000 to $120,000 |
| Advanced Agentic Deployment | Exceeds $500,000 |
The divergence between training conditions and inference conditions remains a persistent cause of performance decline in production. Kian Katanforoosh, cofounder of Workera, states that companies often fail because they ignore the third pillar of AI development: an AI-trained workforce. This leads to unknown unknowns where employees do not realize the extent of their own knowledge or the limitations of the tool.
The shift toward agentic AI also creates a massive expansion in identity governance needs. Companies must govern new identities, including service accounts and autonomous agents, to maintain zero trust environments. Gartner forecasts that 75% of SOC teams will experience an erosion of foundational security analysis skills by 2030 because of overdependence on automation. Can enterprise security frameworks keep pace with the speed of autonomous agent identity creation?
