Written by: Arjun Karnik, Growth Marketing Specialist
Key Takeaways
- AI search services now fall into three buckets: enterprise platforms, consumer engines, and developer APIs, each serving a different job from internal search to buyer-facing visibility.
- Enterprise platforms like Azure AI Search, Vertex AI Search, and Amazon Kendra let organizations build secure, AI-powered search over private data, with strengths tied to each cloud ecosystem.
- Consumer AI engines such as Perplexity, Google AI Mode, Microsoft Copilot, and You.com now shape B2B research, and most buyers already use AI chatbots when evaluating software.
- Developers can embed AI search through APIs like Exa, Tavily, and Brave Search API, choosing based on semantic retrieval needs, latency, and independence from Big Tech indexes.
- Generative Engine Optimization (GEO) now drives brand visibility in AI answers; see how Arjun Karnik’s test lab gets businesses cited across AI search surfaces.
Enterprise AI Search Services: Building Search Over Your Own Data
Enterprise AI search platforms give you managed infrastructure to build custom search, retrieval-augmented generation (RAG), and AI agent applications over private data. The three dominant cloud platforms each align with a specific ecosystem.
Azure AI Search (Microsoft)
Azure AI Search is Microsoft’s fully managed, cloud-hosted search service that connects enterprise and web content to AI. It enables agents and large language models to produce grounded answers using context, chat history, and multi-source signals. It supports full-text, vector, hybrid, and multimodal queries over both indexed local content and remote sources.
Key capabilities include AI enrichment pipelines for chunking, vectorizing, and preparing raw content, semantic ranking, agentic retrieval for parallel, iterative, LLM-assisted search, and native Model Context Protocol (MCP) support. Data sources include Azure Blob Storage, Azure Cosmos DB, Microsoft SharePoint, and Microsoft OneLake. Enterprise security runs through Microsoft Entra ID, Azure Private Link, document-level access control, and role-based access.
Pricing follows two models. Dedicated uses provisioned capacity billed per hour based on Search Units, where cost equals replicas times partitions times hourly rate. Serverless (Preview) is consumption-based, billed per Compute Unit per hour plus per-GB storage. The Serverless Developer tier is in preview with billing beginning September 13, 2026. A Free tier exists for development and testing, limited to 50 MB storage and three indexes.
Best for: Organizations already invested in the Microsoft and Azure ecosystem that need scalable, secure enterprise search with agentic retrieval.
Vertex AI Search (Google Cloud)
Vertex AI Search is Google Cloud’s generative AI-powered search service for websites and enterprise databases. It delivers grounded answers with grounding verification. It supports hybrid search and grounding against BigQuery, Cloud Storage, Confluence, and SharePoint, with integration across the Gemini model family. Google rebranded Vertex AI Agent Builder as the Gemini Enterprise Agent Platform at Cloud Next 2026, retaining the same services and billing model.
Pricing is usage-based, with third-party sources reporting roughly $1.50 to $6.00 per 1,000 queries depending on tier, and 10,000 free queries per month for new accounts. The platform inherits Google Cloud’s HIPAA, FedRAMP, and SOC 2 certifications.
Best for: Teams with existing Google Cloud expertise and enterprises in regulated industries that require HIPAA or FedRAMP compliance.
Amazon Kendra (AWS)
Amazon Kendra is AWS’s enterprise search service that uses machine learning to locate accurate answers from unstructured and structured files. It supports semantic search, natural language queries, and connectors to popular data sources. Pricing is usage-based, billed per index-hour for storage and query units, with additional costs for connectors that handle document ingestion and sync.
Best for: Organizations deeply embedded in the AWS ecosystem that need ML-powered search across disparate data sources.
Enterprise AI Search Services Comparison
| Provider | Type | Key Capabilities | Best Use Case |
|---|---|---|---|
| Azure AI Search | Cloud platform | Hybrid vector and keyword search, AI enrichment, semantic ranking, MCP support, agentic retrieval | Enterprise RAG and agentic retrieval in Microsoft ecosystems |
| Vertex AI Search | Cloud platform | Generative AI search, grounded answers, grounding verification, Gemini model integration | Regulated industries and Google Cloud-native teams |
| Amazon Kendra | Cloud platform | ML-powered semantic search, natural language queries, broad data source connectors | AWS-centric organizations searching unstructured data |
See how Arjun’s GEO test lab gets businesses cited across AI surfaces.
Consumer AI Search Engines: Where Your Buyers Ask Questions
Consumer AI search engines now replace or augment traditional web search with conversational answers, real-time discovery, and deep research summaries. These surfaces are where your customers research purchases. A G2 survey of 1,076 B2B software buyers in March 2026 found that 71% use AI chatbots for software research, and 69% chose a different vendor than planned based on what the assistant told them.
Perplexity
Perplexity is a research-first AI search engine that leads every answer with numbered inline citations for one-click verification. That citation-first design makes it the engine of choice for research, due diligence, and any task requiring a defensible answer. Its scale reflects that role. Perplexity processed 780 million monthly queries in 2025 and reached a $20 billion valuation with over 45 million active monthly users. AI search engines like Perplexity now drive over 40% of B2B product-discovery interactions. A free tier is available, and Pro costs $20 per month.
Google AI Mode
Google AI Mode delivers a full conversational search experience inside Google, grounded in Google’s index and powered by Gemini. Sundar Pichai reported at Google I/O in May 2026 that AI Overviews surpassed 2.5 billion monthly active users and AI Mode passed 1 billion monthly active users within its first year. Google AI Mode uses query fan-out to break complex questions into multiple sub-searches and synthesize results. That mechanic makes optimizing for the visible prompt alone insufficient. Google AI Mode is free.
Microsoft Copilot
Microsoft Copilot is a free GPT-class assistant grounded in Bing and woven into Windows, Edge, and Microsoft 365. It offers inline citations and can reason over personal files and email. Copilot has 33 million active users globally, with weekly sessions increasing more than 90% during 2025. It works best for productivity and Microsoft ecosystem users and is free.
You.com
You.com is a productivity-oriented AI search engine that provides access to multiple AI models, including GPT-4, Claude, and Gemini, in a single interface. Its customizable search modes appeal to power users who want to experiment across models without subscribing to multiple platforms. A free tier exists, with paid plans available.
Consumer AI Search Engines Comparison
| Engine | Best Suited For | Key Focus | Free vs. Paid |
|---|---|---|---|
| Perplexity | Research and due diligence | Live citations, synthesis, conversational follow-ups | Free tier; Pro $20/mo |
| Google AI Mode | General web queries | Conversational search with the widest reach of any AI answer surface | Free |
| Microsoft Copilot | Productivity and Microsoft ecosystem | Integration with Windows, Office apps, enterprise data | Free |
| You.com | Power users and multi-model experimentation | Multi-model access, customizable search modes | Free tier; paid plans available |
AI Search APIs for Developers: Building Search Into Your Product
Developers who need AI-powered search features can move faster with APIs than with full enterprise platforms. Three independent options dominate the market in 2026.
Exa is a neural web search API built on an embeddings-based index that favors semantic discovery. Pricing is $7 per 1,000 search requests, with a free tier of $20 in signup credits plus $10 monthly. Exa works best when meaning-based retrieval matters more than raw SERP fidelity.
Tavily is an agent-native web-access layer that searches the live web and returns clean, LLM-ready snippets with minimal post-processing. Pricing runs approximately $8 per 1,000 basic searches, with a free tier of 1,000 credits per month. It maintains the official LangChain integration and is the lowest-friction default for general RAG and agent pipelines.
Brave Search API operates its own independent index of over 30 billion pages. Pricing is $5 per 1,000 requests. After Microsoft retired the Bing Search API in August 2025, Brave became the largest independent Western index available to developers. That independence makes it the pick for privacy-sensitive use cases and teams that avoid dependency on a single Big Tech index.
The choice between them depends on whether you need semantic discovery with Exa, LLM-ready snippets with minimal glue code from Tavily, or an independent index with low latency from Brave.
How to Choose the Right AI Search Service: A Decision Framework
- If you need internal enterprise search over your company’s private data, start with Azure AI Search for Microsoft-centric stacks, Vertex AI Search for Google-centric stacks, or Amazon Kendra for AWS-centric stacks.
- If you are a marketer or business leader focused on brand visibility, prioritize consumer AI search engines such as Google AI Mode for reach, Perplexity for research and citations, and Microsoft Copilot for high query volume.
- If you are a developer building a search feature into your own product, evaluate AI search APIs like Exa, Tavily, or Brave Search API based on retrieval needs and latency requirements.
- If budget is a constraint, start with free tiers. Vertex AI Search offers 10,000 free queries per month, Perplexity has a useful free tier with unlimited basic searches, and Google AI Mode is free.
Why Being Cited in AI Search Matters for Your Business
AI answers now capture attention at the point of search, which reduces clicks to traditional results. Pew Research Center found that when an AI summary appeared in Google results, users clicked a traditional search result in only 8% of visits, versus 15% without one, tracking 900 US adults across 68,879 searches in March 2025.
B2B buyers now start with AI chatbots instead of classic search results. G2’s March 2026 survey of 1,076 B2B software buyers found that 71% use AI chatbots for software research, 69% chose a different vendor than planned based on what the assistant told them, and 33% bought from a vendor they had never previously heard of.
The audience using AI assistants is already massive. OpenAI reported 900 million weekly active ChatGPT users in February 2026. As mentioned earlier, Google’s AI surfaces now reach billions of users monthly.
Similarweb clickstream data shows the zero-click rate for Google searches reached 68.01% in January through April 2026, up from 60.45% in 2024, with only 276 out of every 1,000 Google searches resulting in a click to the open web. The buyer reads the answer where they asked it. When a name in that answer earns their trust, they type it directly into a browser bar. The content still did its job, even if it did not leave a clean click trajectory behind.
By 2026, brand visibility in search depends less on page position in ranked results and more on whether a brand is cited within AI-generated responses from systems such as Google AI Overviews and Bing generative search. Traffic arriving from chatgpt.com and similar referrers converts the way word of mouth converts, because an assistant effectively recommended you.
The Solution: Generative Engine Optimization (GEO)
GEO focuses on getting your content cited, mentioned, and recommended in AI answers. Arjun Karnik runs a public test lab under his own name and documents exactly what gets a business mentioned, cited, and recommended in AI answers, publishing both wins and misses.
Four mechanics separate GEO from traditional SEO. First, fan-out queries: a single buyer prompt triggers dozens of hidden retrieval queries underneath, and the answer is assembled from what comes back. That structure means optimizing for the visible prompt alone targets the wrong surface. In a test on Arjun’s own site, pages rewritten to match extracted ChatGPT fan-out queries earned citations while control pages did not.
Second, freshness is the game itself, not just a maintenance task. In Arjun’s own decay tracking, pages can drop 78% to 99% in two months without updates. Seer Interactive analyzed 47,097 AI citations across 7,683 pages in ChatGPT, Gemini, and Perplexity between March and June 2026, and found that 75% of cited pages had been updated within the last year, with pages cited consistently across all four months averaging under six months since their last update. Content freshness accounts for 40% of Perplexity’s ranking signal, and pages under 30 days old receive 3.2× more citations than older content.
Third, topical authority matters. Coverage built through pillars and clusters earns authority in this channel, and that authority comes from depth on a topic rather than inherited backlinks. Fourth, structured content matters. Query language in URLs, titles, H1s, and H2s, combined with schema markup on everything, makes pages machine-parseable at the retrieval layer.
On Arjun’s own site, new articles reached thousands of monthly Google impressions within weeks, and the GEO subfolder became the only source of new impressions on the domain in 60 days. His method is self-verifying: ask an AI assistant about his topics and see who gets cited.
Watch the GEO test lab in action with a live walkthrough.
How to Get Started with GEO: A Practical Playbook
- Run a visibility audit. Baseline where your business is mentioned, cited, or absent across ChatGPT, Gemini, Perplexity, and Google AI Overviews. Treat this as the starting line and the control group.
- Fix technical plumbing. Unblock AI crawlers, add schema markup, and make pages machine-parseable. If the retrieval layer cannot read the site, nothing downstream matters.
- Map fan-out queries. Extract the hidden questions behind buyer prompts directly from ChatGPT rather than inferring them from keyword tools. The target is the machine’s questions, not the human’s visible query.
- Align buyer language. Rewrite URLs, titles, H1s, and H2s to match the language buyers actually use. In Arjun’s own test, relabelling a jargon-heavy page to buyer language produced citations within weeks of that specific change.
- Publish at machine cadence. Deploy an AI article engine that publishes structured pages at a cadence humans cannot match. Via AI Growth Agent (partnership disclosed), this runs at 5 to 8 autonomous actions per day, including new articles and updates.
- Run the freshness loop. Set impression-decay tripwires that auto-queue content updates when performance drops. This keeps the library from decaying in place.
- Measure citations. Track citations, mentions, and share of voice across ChatGPT, Google AI Overviews, Perplexity, and Gemini, plus AI referrers in analytics. Treat whatever you measure as a floor, not a ceiling.
This program operates as a system rather than a one-time fix. The Semrush AI Visibility Study found that AI citations change 40 to 60% month over month, which means a fixed library of any size decays without a continuous refresh loop.
Frequently Asked Questions
Is GEO just SEO with a new name?
The target changed. SEO focuses on rankings on a human-readable list of blue links. GEO focuses on citation inside a machine-generated answer. The retrieval mechanics differ. SEO earns authority through backlinks and domain authority, while GEO earns it through topical coverage. SEO optimizes against the query the buyer typed. GEO optimizes against dozens of fan-out queries the buyer never sees. The success metric also differs, shifting from rankings to citations, mentions, and share of voice. The rank report that used to be the point now measures a surface the buyer increasingly skips.
Will Google penalize AI-generated content?
Google penalizes low-quality content and rewards relevant, structured, fresh, specific content regardless of how it was produced. The production method is not the variable being judged. Quality, structure, and freshness are. Content built for citation still performs in Google. As shown on Arjun’s own site, GEO content also performs well in traditional Google search. The two channels reward the same underlying content properties.
How do I measure AI search visibility?
Track share of answer across ChatGPT, Google AI Overviews, Perplexity, and Gemini, plus AI referrers such as chatgpt.com in analytics, plus impressions and decay curves in Google Search Console. Add one honest caveat. Buyers frequently copy an answer and paste a name into a browser, which shows up as direct traffic and never gets attributed to the AI answer that caused it. Whatever you measure is a floor, not a ceiling. The right response is to instrument for citations and share of answers rather than to keep grading a channel on the click metric it no longer reliably produces.
Can I wait a year before addressing AI search visibility?
Answers gain incumbency over time. Early citations become tomorrow’s record, and the cost of entry rises as settled answers harden. This pattern mirrors the early SEO window. Early movers gained outsized returns, then latecomers paid to catch up. Relevance and freshness still beat tenure in this channel, so a challenger can outrun an incumbent whose library is stale. That window narrows as more businesses build structured, fresh, topically authoritative content libraries.
What does it cost compared to what I do now?
Market benchmarks place AI content engines at roughly $5,000 per month, versus roughly $10,000 per month for 7 to 10 human-written articles with no refresh loop. The second number buys better prose. The first buys volume, structure, and freshness, which are the three things the channel actually rewards. These are market benchmarks, not Arjun’s rates. The more important cost comparison is the opportunity cost of waiting. Every month without a structured GEO program is a month during which competitor citations harden into the default answer for your category.
Conclusion
AI search services now span three distinct markets. Enterprise platforms power search over private data. Consumer engines shape how buyers research purchases. Developer APIs embed search into products. The right choice depends entirely on your use case. Azure AI Search, Vertex AI Search, and Amazon Kendra serve organizations building internal search infrastructure. Perplexity, Google AI Mode, Microsoft Copilot, and You.com are the surfaces where brand visibility now influences vendor selection. Exa, Tavily, and Brave Search API give developers independent retrieval without Big Tech dependency.
Regardless of which service you choose to build with, the rise of AI search means your business must now be cited, mentioned, and recommended inside AI answers. Ranking on a list is no longer enough. Being in the answer is a vendor-selection event. Being absent from it leaves you invisible.
Ready to get cited in AI answers? Schedule a demo with Arjun Karnik.
