Gate.AI Evolves into an Intelligent Research Hub for Market Information

In the past, using AI to research markets usually meant feeding different sources into AI separately: price data came from trading pages, news came from information sites, project materials came from official websites, and research reports had to be uploaded separately. AI can process all of this information. However, users still need to do a lot of information gathering and organization first.

As multimodal and deep research capabilities keep moving into trading scenarios, AI’s role is changing. It’s no longer just answering simple questions like "How is BTC doing recently?" Instead, it’s starting to take on tasks such as information gathering, material parsing, content integration, and ongoing monitoring.

Gate.AI’s product evolution also reflects this trend. Beyond basic natural-language interaction, the platform has expanded into image uploads, file parsing, research using specified URLs, stock research, market anomaly analysis, and proactive information delivery. For users, this means AI isn’t just a chat entry point anymore—it’s gradually becoming a market information entry point that can support an entire research workflow.

From "searching for an answer" to "processing a set of information"

The most time-consuming part of market research often isn’t any single question—it’s the fragmentation of information.

For example, when researching a crypto project, users may need to look at price charts first, then review the project’s official website, whitepaper, token information, on-chain data, and recent news. If the research is about a public company, they also need to add financial reports, valuation, industry conditions, and market expectations.

In the past, users typically had to assemble all this information themselves, then hand the compiled content to AI for analysis.

Gate.AI’s multimodal capabilities make this process much more direct. Users can upload K-line charts, project whitepapers, or research report PDFs so AI can recognize, summarize, and extract insights from the materials. Users can also use the project’s official website, a blockchain explorer page, or other specified URLs as the research starting point, letting AI conduct further analysis around those specific sources.

The value of these capabilities isn’t that "AI knows more." It’s that it reduces the cost for users to organize information.

For complex problems, the act of inputting information is itself part of the research. If users can directly give the materials they’re already looking at to AI, the output is more likely to focus on those specific materials rather than staying at a generic market-knowledge level.

Multimodal input changes how people use AI for research

Text is still the most common input for AI interaction, but market research isn’t only about text.

K-line charts are images. Whitepapers are documents. Financial reports are PDFs. Webpages combine structured and unstructured information. Users may even take a photo of a screen screenshot and expect AI to help explain the data inside it.

That’s why multimodal capabilities are especially practical in trading research scenarios.

For instance, if a user sees a BTC 4-hour K-line chart, they can upload the image directly and have AI interpret it in a structured way based on what’s shown. If someone is researching a new project, they can upload its whitepaper so AI can help extract the project’s mechanism, token allocation, and potential risk points. If they’re facing a long research report, they can also have AI first extract the key viewpoints, then ask follow-up questions about the specific content.

This usage differs clearly from ordinary search. Search usually requires users to know what they should search for first. Multimodal AI, however, can start from the materials the user already has.

That makes AI more like a research workstation than just a search box.

The meaning of Deep Research is to reduce information "stitching"

As research questions become more complex, a single round of Q&A often isn’t enough.

For example, "Why did a token rise recently" may require looking at price, trading volume, on-chain activity, project news, and market sentiment at the same time. If users search each item themselves and then piece the results together, the time cost is high.

Gate.AI’s Deep Research is more suited to these multi-step questions. Users can specify the project’s official website, a blockchain explorer link, or a contract address as the research entry point, so AI organizes information in a more focused way around the target.

The real value of this capability is connecting "finding materials" with "interpreting materials."

In the past, using AI typically meant users first collected materials and then asked AI. Now, users can hand the research objective to AI so AI can participate in organizing materials and doing initial analysis. Users can also follow up on the basis for a conclusion, or ask AI to compare differences between different pieces of information.

Of course, deep research doesn’t automatically guarantee correct results. AI may still miss information, misread materials, or confuse correlation with causation. That’s why the more complex the research question is, the more users need to verify original sources and key conclusions.

Gate.AI is also expanding into stock research

Another obvious change is that Gate.AI’s AI capabilities are gradually extending from crypto markets into stock research.

Traditional stock research usually requires users to review company financial reports, stock prices, trading volume, industry updates, and macro factors. As Gate’s stock business coverage expands, AI research capabilities are moving into stock scenarios as well.

Gate.AI has already expanded in areas such as AI stock selection, picking promising stocks, and analyzing unusual trading events. It also adds information like historical performance and trading volume, so users can discover opportunities and then move into a deeper research stage.

This means AI’s use cases are shifting from "explaining what has already happened in the market" to "helping users discover objects worth researching further."

That said, there’s still an important boundary to note: AI stock selection or promising-stock functions are better understood as information filtering and research assistance, not as a guarantee of future price performance. When a stock falls within AI’s attention scope, it only means it meets certain data or event conditions—not that it will necessarily rise in the future.

For users, a more reasonable approach is to use AI as a first-layer screening tool, and then further review company fundamentals, valuation, and market conditions.

Proactive alerts turn AI from a passive tool into a continuous watch layer

If users have to actively open AI to query every time, then AI remains a passive tool.

Gate.AI’s upgrade in proactive smart alerts for position anomalies offers another way of thinking: the system monitors market changes related to the assets users hold in spot, leverage, and contract positions. When related assets show significant volatility or are affected by macro, regulatory, or security events, it proactively sends reminders and then provides an AI analysis entry point.

The focus of this design isn’t to have AI constantly push more information. It’s to connect information to the assets the user genuinely holds.

Markets generate a huge volume of news every day, but not all of it is relevant to everyone. If a user holds BTC, major BTC price changes and related risk events are obviously more worth关注 than ordinary news about some completely unrelated asset.

So the value of proactive alerts lies in filtering—not in increasing the quantity of information.

After users click an alert, they can continue into AI conversation to drill into the event. For example: how does this change relate to the current holdings? What factors might affect the market? Are there other related events? In this way, a single alert can become the entry point for follow-up research.

Gate.AI is becoming more like a "research workbench"

When you put images, files, webpages, market data, stock research, and proactive notifications together, Gate.AI’s product logic is changing.

It’s no longer just an AI that answers market questions. It’s beginning to cover multiple information-input stages throughout the research process. Users can bring their own materials, or start from data provided by the platform, then progressively go deeper through natural language.

One key feature of such workflows is that the research process can continue over time.

Today a user can upload a whitepaper and have AI extract the core content. Tomorrow they can continue asking what happened recently with the project. If the market changes significantly, they can enter a new analysis flow through alerts. Information, questions, and follow-up research are no longer completely disconnected.

For users who research the market often, this kind of continuity may matter more than a one-off answer. Real research often doesn’t end after asking a single question. Instead, it involves continuously discovering new information, raising new questions, and re-validating earlier judgments.

Differences between Gate.AI and Gate for AI Agent

Both Gate.AI and Gate for AI Agent are part of Gate’s AI strategy, but they serve different roles.

Gate.AI is designed for users who use AI directly. It emphasizes natural-language interaction, market research, multimodal input, stock analysis, and information delivery. Users don’t need to learn complex APIs or development environments first.

Gate for AI Agent is more like AI agent infrastructure. It enables external AI agents and developers to call trading, wallets, data, news, and payment capabilities through Skills, MCP, CLI, and similar methods.

In simple terms, Gate.AI is more like a user-facing intelligent research and interaction entry point. Gate for AI Agent is more like an infrastructure layer that lets other agents call Gate capabilities.

This distinction also means they’re not simply in competition with each other. One focuses on "how users use AI," while the other focuses on "how AI agents call financial capabilities."

The real value of AI research is still reducing information friction

One of the hardest problems in market research is having too much information.

Without AI, users must spend a lot of time searching for materials, reading documents, comparing different data, and then organizing everything into their own judgments. AI can reduce these repetitive tasks, but it can’t take final responsibility on the user’s behalf.

So Gate.AI’s value is best understood from the perspective of "reducing information friction."

It can help users find information faster, understand materials faster, and spot market anomalies faster. It can also place multiple information sources into the same interaction environment. But ultimately, users still need to decide which information matters, which conclusions are worth trusting, and whether they should take trading action.

Especially in financial markets, the easier AI output becomes, the more users need to stay focused on original data and key assumptions.

Conclusion

Gate.AI’s product evolution is clearly pointing in one direction: AI’s role in the market is expanding from "answering questions" to "participating in the research workflow."

Images, files, webpages, market data, stock research, and proactive reminders let users enter AI through different information entry points, then keep going deeper through natural language. Compared with pure market Q&A, this approach is closer to a long-term market research workbench.

At the same time, the positioning of Gate.AI and Gate for AI Agent is becoming clearer. The former focuses more on user-side information acquisition and research experience. The latter focuses on how AI agents obtain callable financial infrastructure.

For users, the most worth expecting from AI isn’t replacing judgment—it’s making information gathering, organization, and initial analysis more efficient. Once information friction drops, users can spend more time on the questions that truly require thinking.

FAQ

Can Gate.AI analyze images and files?

Yes. Gate.AI supports capabilities such as image uploads and file parsing, and users can upload materials like K-line charts, whitepapers, and research reports for analysis.

What does Gate.AI’s Deep Research do?

Deep Research is suited for problems that require multiple information sources. Users can specify a URL, a project’s official website, or related pages as research entry points, so AI can organize information more deeply around a specific theme.

Can Gate.AI be used for stock research?

Yes. Gate.AI has expanded into scenarios including AI stock selection, promising-stock analysis, and stock anomaly research.

Does AI stock selection mean the stock will definitely go up?

No. AI stock selection is an information-filtering and research-assistance feature. Any filtering result does not represent future price performance.