Best AI Search Engines and How to Use Them [2025]

But first, let's address the elephant in the room:

Are AI search engines “broken” by design?

AI search engines like Perplexity or ChatGPT are powered by Large Language Models (LLM) which are a type of generative AI. These models generate content based on patterns in their training data, which can lead to the creation of plausible but false or unverified information. Also, when seeking unique insights or original opinions on specialized topics, AI models often rely on prevalent data from the internet, which tends to reflect general consensus rather than unconventional perspectives.

Let’s take a look at another example of a query that we’ll use with ChatGPT Web Search functionality.

Here is the output:

Take a look at the last result—it's from one of my articles.

I would never use such a formal phrase as "thereby enhancing," yet AI confidently misquotes me and attributes it to me. You can visit the article on generative AI in finance to see that the quote attributed to me is nowhere to be found.

To be fair, some quotes were accurate. But it's this mix of real and fabricated information that makes AI search engines so treacherous.

Look closer at those quotes and you'll notice something interesting too—most are just opening statements from articles, the kind of high-level declarations that float to the top of search results. AI search engines can't magically surface minority viewpoints or specialized knowledge buried deep in lengthy discussions. In a way, AI doesn't know what it doesn't know, and even with web browsing capabilities, it struggles to piece together the information it's missing. Sure, getting three generic quotes and one fabricated one might seem like a decent batting average, but it exposes a deeper problem: these tools are just scratching the surface of available knowledge, then filling in the gaps with plausible-sounding fabrications.

But, what exactly happened here? Why are some quotes wrong?

The answer lies in how AI processes information and what happens under the hood.

AI search does not function like a traditional database lookup.

AI search engines rely on a combination of traditional keyword-based retrieval and LLMs to process, interpret, and generate responses. The retrieval process itself typically operates on keywords, ranking documents based on relevance scores. However, when an LLM is involved in processing search results, it relies on high-dimensional embeddings—numerical representations that capture semantic meaning rather than exact wording.

This means that while the search engine itself retrieves documents based on keyword matches and ranking algorithms, the LLM then processes this information and “thinks about it” by implicitly converting it into embeddings, which emphasize general context and meaning rather than preserving every precise detail or word-level accuracy. This step allows the model to summarize, rephrase, or extract relevant insights from the retrieved documents.

Because LLMs operate probabilistically, each processing step—whether retrieving, filtering, summarizing, or refining results—introduces a degree of abstraction. When multiple iterations occur, small deviations in meaning can accumulate. This is particularly noticeable in workflows where the LLM repeatedly refines responses. This approach can lead to inaccuracies, such as misquoting or blending context from multiple sources, because the process prioritizes general understanding over precise recall of specific words or facts.

So, what does it mean for us?

Well, you can think of AI search engines, or rather LLMs in general, like storytellers who remember the gist of every book but not every single word. AI turns pages into a "big idea" sketch instead of keeping a perfect copy of each sentence. This means the engine can tell you what a page is generally about, but sometimes it might mix up details or quote things slightly off, because it’s focusing on the overall message rather than every exact word.

There is a second problem too.

Let’s ask ChatGPT about "AI search engines for social media" and watch what happens:

Instead of understanding that we're looking for tools that surface crowdsourced insights (like Reddit Answers or Grok AI), it does a surface-level search and finds whatever ranks highest for "AI tools" + "social media." The result? You get a list of enterprise analytics platforms and engagement monitoring tools—because that's what dominates SEO-optimized search results, even though it's completely missing the point of our query.

Here is another example, this time of Perplexity AI finding “quotes” but ignoring the context about finance:

The list goes on and on. Quoting numbers or statistics gets even worse.

These examples all point to the same underlying problem: AI search engines are trying to be too clever without being truly intelligent. They're taking our complex questions, running basic keyword searches, and then using their language capabilities to mask the shallowness of their research. It's like having a smooth-talking assistant who's great at presenting but terrible at actual research.

Interestingly, according to a recent study, higher confidence in AI is negatively correlated with critical thinking effort—users trust AI more, think less, and accept outputs without scrutiny.

So what can we do about it? The solution isn't to abandon AI search—it's to get better at controlling how these tools work. Let's look at how to fix these fundamental flaws and make AI searches actually trustworthy.

How to fix the problem and make AI searches more accurate

Here's the thing about current AI search engines—while they're "agentic" in theory, their actual process is surprisingly basic. Behind the scenes, they're just running through a simple playbook: write some search prompts, run searches iteratively, visit a few pages, and cobble together an answer from whatever they find. It's like having a research assistant who only skims headlines and first paragraphs before declaring "I found it!"

This approach has four major flaws that make it unreliable:

• No search planning or good pre-search reasoning

• Lack of control over search depth

• Superficial reading and probability-based data extraction

• No transparency and difficult verification

First, these engines don't think enough before they search. They jump straight into broad queries without planning a strategic research approach. Imagine trying to research a complex topic by typing random related words into Google—that's essentially what they're doing.

Second, you have no control over search depth. Want to make sure the engine checks at least 20 sources before drawing conclusions? Too bad. It'll stop as soon as it thinks it has enough to give you a plausible answer, whether that's after checking two sources or twenty.

Third, these engines are lazy readers. They'll grab the first relevant-looking paragraph they see and move on, potentially missing crucial context or more insightful information further down the page. It's like citing a book after only reading its introduction.

Finally, there's the verification problem. When an AI search engine gives you an answer, you have to manually check every source—and good luck with that when the citations are vague or, worse, completely fabricated.

The solution? We need tools that give us more control over the search process. LLM orchestration platforms with web browsing capabilities, like V7 Go, let you create custom workflows for different types of queries. Want to verify sources before synthesis? Need to ensure a minimum research depth? These tools let you control and monitor each step of the process, from initial query planning to final output generation.

Here is a high level overview of what an AI search agent could look like:

Step 1: User input

Step 2: AI analyzes the problem and generates X separate search queries

Step 3: Multiple web search operations + full content scraping for selected URLs

Step 4: Final analysis and summarization by AI that can access all collected information

The final output is based on all information collected during the preceding steps.

This method is just one of the possible ways to use AI web search functionalities in a more efficient and trustworthy way. The whole workflow may require some additional prompt engineering, but if you are setting it up with V7 Go you can then use it as an AI agent that is acessible inside a chat interface.

Here is an example of a different AI agent created with V7 Go for automated cross-referencing of documents with online sources.

The final output in this case is a result of a two-step analysis, one happening before using web browsing (to identify things that should be researched online in the first place) and the second based on online sources. Both AIs highlighted relevant sections using different colors. You can also explore the JSON version of the information that is collected online.

 What happens behind the scenes is very similar to the previous workflow:

1. The AI identifies key areas of interest that require additional online research and highlights them in the document.

2. Then the AI agent decides what the long tail search queries should be, performs a series of online searches and extracts data from the web.

3. The final step is coming back to the document with the new information found online.

These agents can be configured to perform additional tasks too. This can include creating feedback loops or AI-assisted QA for fact-checking. In some workflows, different AI models can do the same tasks in parallel, in a kind of blind test way, to verify if they achieve the same or different results.

This way of solving the AI search problem by setting up a custom automated agent that performs a series of actions before returning the response is better than using out-of-the-box models like ChatGPT or Gemini Copilot. Introducing the “reasoning” feature by OpenAI has helped improve the quality of the outputs, but the reality is that we don’t get full visibility into the process. This turns AI into a black-box system—while you can see the input and output, you have no control over what happens in between. This lack of transparency is unacceptable when applying AI solutions in business settings.

Now that we have covered both the key challenges associated with AI search engines and the methods to solve them, let’s dive deeper into the actual tools you can use. We’ll discuss their pros and cons, as well as their focus on different types of searches, sometimes extending beyond web search.

10 Best AI Search Engines

Let’s start with a quick test to compare the results from top AI search engines.

The query is:

What are some good research papers on the depiction of string instruments in medieval art?

And here are the outputs from different tools used:

V7 Go

The results are accurate, relevant, and we can track all necessary information and analyze every step and decision made by AI.

ChatGPT Web Search

There is some overlap with the results provided by V7 Go and in this case ChatGPT managed to find similar resources.

Perplexity AI

Perplexity focused on medieval instruments in general rather than their depiction in works of art. To some extent it has reversed the priorities.

Bing Copilot

The results from Copilot were the worst. All of the results were based on two pages from Academia.edu (note the repeated references to source number 1).

Here is the summary of the comparison:

While no AI search engine is perfect, some are better suited for specific tasks than others. By understanding their strengths and weaknesses, we can make more informed choices about when and how to use them.

Let's explore each of these tools individually. In the second part of our list, we'll also discuss some additional tools that you might consider, particularly if you want to use custom sources or social media as your "source of truth."

1. V7 Go: Agentic AI With Web Browsing

V7 Go is a generative AI platform that provides a truly agentic framework for automated web searches. You can use it for building AI agents tailored for specific search-based operations such as data enrichment, web scraping, and cross-referencing internal knowledge with online information.

As outlined in the previous section, it is effectively an AI knowledge work automation tool that allows you to break down complex business processes into automated workflows handled by AI agents. It also combines web data extraction with Retrieval-Augmented Generation (RAG) which makes it perfect for both online and offline searches (for example analyzing your internal knowledge hubs in tasks like legal document Q&As).

For instance, these agents can streamline tasks like pitch deck analysis or CIM reviews by extracting key information from a presentation or PDF and verifying it against publicly available online data about the company. Instead of performing a simple keyword search, they can break down queries into multiple targeted searches, each focusing on a specific aspect of the request or extracting nuanced details from both online and offline sources.

Pros:

• Can be used professionally in high-stakes operations like asset management or real estate

• Visual grounding for automatic AI citations and highlighting

• Fully configurable, down to every reasoning step

Cons:

• Depending on your needs, it might require some tweaking and prompt engineering. For more complex business use cases, you can consult with V7 Labs solutions engineers, who will help you build a proof of concept.