Visibility
Sentiment
Exa
Exa is an AI-powered search API company that provides fast, high-quality web search capabilities for developers and enterprises building AI applications. Their API enables real-time search, crawling, and research functionality with sub-200ms response times, trusted by leading tech companies like Notion, Vercel, and AWS.
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Sentiment
AI Perception Summary
Exa has successfully captured the LLM-Ops market with a dominant 86% mention rate, yet it is currently surrendering the 'Enterprise Web Scraping' and 'Deep Research' categories to rivals like Tavily and Firecrawl. While the brand enjoys elite placement in Google AI Overviews with an average position of 2.8, mixed sentiment across ChatGPT and Claude indicates a critical need to refine technical documentation and third-party validation.
Value Proposition
The best search API for AI - providing fast, high-quality web search with sub-200ms latency, comprehensive data coverage across industries, and enterprise-grade security with zero data retention.
Overview
Exa is an AI-powered search API company that provides fast, high-quality web search capabilities for developers and enterprises building AI applications. Their API enables real-time search, crawling, and research functionality with sub-200ms response times, trusted by leading tech companies like Notion, Vercel, and AWS.
Mission
Build a world with perfect search
Products & Services
AI Platforms
How often do different AI platforms reference Exa?
Conversation Topics
What conversations is Exa included in — or excluded from?
Personas
Who does each AI platform recommend Exa to, and when?
Key Insights
What AI visibility analysis reveals about this brand
Site Health for AI Visibility
How well Exa's website is optimized for AI agent discovery and comprehension.
Can AI bots find your pages?
SSL, mobile, doctype basics
Titles, descriptions, headings
Word count, depth, freshness
Structured data for AI comprehension
Open Graph, Twitter cards
How well AI can parse your content
Warnings
Meta description may be truncated (178 characters)
Shorten to under 160 characters.
Page has 4 H1 tags. Best practice is one.
Use a single H1 for the main heading, and H2-H6 for subheadings.
Page size is moderately large
Consider optimizing if page feels slow.
Want a full technical audit with AI-specific recommendations?
Run a free visibility scanBrand Voice & Style
How AI perceives Exa's communication style and personality
Exa communicates with confident technical authority while remaining accessible to developers of all levels. The brand voice is clean, precise, and data-driven, emphasizing performance metrics and benchmarks to substantiate claims. There's an understated confidence that lets the product speak for itself, avoiding hype in favor of clear, factual statements. The tone balances professionalism with developer-friendly approachability, using straightforward language that respects the technical sophistication of their audience.
Core Tone Traits
Technically Precise
Uses specific metrics, benchmarks, and technical terminology accurately without unnecessary jargon
Confidently Understated
Makes bold claims backed by data rather than marketing hyperbole
Developer-Friendly
Speaks directly to technical audiences with clear, actionable information
Performance-Focused
Consistently emphasizes speed, quality, and reliability metrics
Related Ecosystem
Related products and services that AI mentions in conversations alongside or instead of Exa
Citations
Sources that AI assistants cite. Getting featured here improves visibility.
https://r.jina.ai/
Referenced in 3 queries
https://dev.to/clickit_devops/langchain-vs-langgraph-which-llm-framework-should-you-use-2k1p#:~:text=LangChain%20is%20one%20of%20the,iteration%2C%20experiment%2Dheavy%20workflows.
Referenced in 1 query
https://developers.openai.com/api/docs/guides/tools-web-search/#:~:text=Using%20the%20Responses%20API%2C%20you,print(response.output_text)
Referenced in 1 query
https://www.reddit.com/r/LLMDevs/comments/1mhlr1a/how_i_connected_my_llm_agents_to_the_live_web/#:~:text=Over%20the%20past%20few%20weeks,built%20on%20top%20of%20Crawlbase.
Referenced in 1 query
https://www.pcmag.com/picks/the-best-ai-search-engines#:~:text=Unimpressive%20deep%20research-,Why%20We%20Picked%20It,Perplexity%20Review
Referenced in 1 query
https://aws.amazon.com/blogs/machine-learning/reducing-hallucinations-in-large-language-models-with-custom-intervention-using-amazon-bedrock-agents/#:~:text=Remediating%20hallucinations%20is%20crucial%20for,to%20generate%20the%20final%20output.
Referenced in 1 query
https://documentation.suse.com/suse-ai/1.0/html/AI-preventing-hallucinations/index.html#:~:text=A%20well%2Ddefined%20prompt%20guides,reducing%20the%20likelihood%20of%20hallucinations.&text=Use%20specific%20language%20that%20guides,or%20paraphrasing%20from%20established%20sources.
Referenced in 1 query
https://www.reddit.com/r/AI_Agents/comments/1q6ktgc/how_to_avoid_hallucinations_when_calling_live_data/#:~:text=Use%20the%20latest%20models.,Be%20more%20deterministic.
Referenced in 1 query
https://www.reddit.com/r/AI_Agents/comments/1r2qj1c/how_to_stop_your_ai_agents_from_hallucinating/#:~:text=I've%20been%20building%20agentic,7%20Go%20to%20comments%20Share
Referenced in 1 query
https://www.linkedin.com/pulse/why-your-ai-agent-keeps-hallucinating-even-when-you-tell-norris-ru4nc#:~:text=Force%20citations,but%20it's%20a%20useful%20tool.
Referenced in 1 query
https://www.ada.cx/blog/preventing-hallucinations-in-ai-best-practices-for-customer-service-ai-agents/#:~:text=Use%20automated%20tools:%20Use%20a,guidance%20to%20correct%20its%20reasoning.
Referenced in 1 query
https://www.cdata.com/blog/guide-to-real-time-data-access-for-llm-applications#:~:text=Connectors%20create%20secure%2C%20permission%2Daware,On%2Ddemand%20transformation
Referenced in 1 query
Goals & Content Ideas
Ideas to help AI agents better understand the business and be more likely to use Exa's resources to help users.
Dominate Enterprise Web Scraping Search Visibility
Exa is currently invisible in high-value enterprise queries around web scraping and structured data extraction, allowing competitors like Firecrawl to own this narrative. This goal focuses on creating authoritative content that positions Exa as the premier solution for enterprise data ingestion, targeting the specific technical keywords and use cases that LLMs reference when recommending scraping solutions. Social media will amplify technical deep-dives and benchmark comparisons to build citation-worthy content.
Optimize Documentation for LLM Sentiment Shift
With 60% mention rates on Claude and ChatGPT but mixed sentiment, Exa's technical documentation lacks the specific proof points LLMs need to confidently recommend our solution. This goal focuses on restructuring documentation with clear reliability metrics, uptime data, and direct competitive comparisons that LLMs can easily parse and cite. We'll create highly quotable, fact-dense content optimized for AI ingestion patterns.
Win Agentic Web Access Comparison Searches
Tavily leads with 88 total mentions across AI platforms, capturing users searching for agentic web access solutions. This goal creates definitive comparison content that directly addresses 'Exa vs. Tavily' and 'Exa vs. Brave Search' queries with objective benchmarks and use-case analysis. Social channels will distribute key differentiators to build awareness and generate backlinks that strengthen our comparison page authority.
Capture Automated Research Tool Workflow Queries
Exa is notably absent from deep research workflow queries despite this being a core use case for our technology. This goal targets developers building automated research tools with specific API implementation examples, code snippets, and architecture guides that LLMs will surface when users ask about research automation. Content will showcase practical implementations that demonstrate Exa's superiority for research workflows.
Recommended Actions
Deploy a targeted content campaign focused on 'Enterprise Web Scraping' and 'Structured Web Data Extraction'.
Exa is currently invisible in these high-value enterprise queries, allowing Firecrawl and traditional scrapers to dominate the narrative for data ingestion.
Optimize technical documentation for LLM ingestion to pivot 'mixed' sentiment to 'positive' in Claude and ChatGPT.
With mention rates near 60% on these platforms but mixed sentiment, the LLMs likely lack specific, high-intent proof points regarding Exa's reliability vs. competitors.
Create 'Exa vs. Tavily' and 'Exa vs. Brave Search' comparison guides focused on 'Agentic Web Access'.
Tavily currently leads in total mentions (88); neutralizing their lead requires direct comparison content that emphasizes Exa's superior positioning for AI agents.
Target the 'Automated Research Tool' workflow queries with specific API implementation examples.
Exa was notably absent from deep research workflow queries, a major use case for its technology that competitors are currently capturing.
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