I have recently become more and more convinced of one thing:
Many problems we used to think of as "life choices," "consumer decisions," or "life planning" are essentially turning into data analysis problems.
Choosing a university program after the gaokao is a data analysis problem.
You need to look at scores, ranking, admission batch, major groups, historical admission cutoffs, enrollment plans, city, tuition, employment, graduate-school paths, and then combine those with family constraints and risk preferences. It is not a simple matter of asking, "What school can I get into with this score?" It is about finding, inside a pile of structured and unstructured information, a risk-reward fit that makes sense for one specific family.
Shopping is also a data analysis problem.
You are not just buying one product. You are analyzing budget, use case, brand, specifications, price, coupon-adjusted price, negative reviews, after-sales service, shipping speed, store credibility, and real user feedback. The final "add to cart" action is only the last step of a much longer analysis chain.
Investing is even more obviously a data analysis problem.
You need to look at financial reports, valuation, industry cycles, the macro environment, market sentiment, news, policy, competitive landscape, historical prices, and risk exposure. The hard part is never finding one piece of information. The hard part is putting multi-source information together and judging the relationships, conflicts, and uncertainty among them.
This is what I mean by "general data analysis."
It is not data analysis in the traditional sense of sitting inside a BI system and looking at charts. It is not a data analyst writing SQL to produce reports. It is a broader capability: around a concrete decision, automatically collect information, understand it, cross-validate it, and finally deliver something a person can directly use.
General Data Analysis Is Not a New Term, but It Is Becoming New Infrastructure
In the past, data analysis usually lived inside companies.
Sales teams looked at sales data. Operations teams looked at user data. Finance looked at revenue and cost. Executives looked at dashboards. The typical interface was a report, a chart, SQL, or Excel. The problem it solved was: "The company already has a batch of data, and I want to extract something useful from it."
But the problem has changed.
Many analysis tasks no longer happen only inside enterprise databases. They happen in the real world:
- A parent needs to help a child judge university application risk;
- An ordinary person wants to buy a washer-dryer set without stepping into a pit;
- An investor wants to judge whether a company has already priced in too much expectation;
- A founder wants to analyze whether there is still room in a niche market;
- A salesperson wants to evaluate whether a lead is truly high value;
- A doctor, lawyer, or consultant needs to combine materials, cases, and databases to make a judgment.
These tasks have something in common: the data sources are scattered, the reasoning process is long, and the final result must be actionable.
They are usually not just database queries, and not just web searches. They need to use many things at the same time:
- The user's own goals and constraints;
- Structured data inside databases;
- Public information found through search engines;
- Web pages that can only be accessed and operated through a browser;
- PDFs, Word documents, Excel files, images, screenshots, and other files;
- A domain knowledge base accumulated over time;
- And finally, a report, spreadsheet, PDF, dashboard, shopping cart, ticket, or some other deliverable.
This is already beyond what traditional BI can fully cover.
It is also not something a general chatbot can complete by casually chatting for a few turns.
It needs a Data Agent foundation.
Why University Applications, Shopping, and Investing Are Actually the Same Kind of Problem
University applications, shopping, and investing look like completely different products.
One is education, one is consumption, and one is finance. The users are different, the emotions are different, the pages are different, and the deliverables are different.
But if you strip away the surface, their skeletons are very similar.
First, collect the user's constraints.
A university application assistant needs to know the province, score, ranking, target schools, major preferences, city preferences, family constraints, and risk tolerance. A shopping assistant needs to know what the user wants to buy, the budget, who will use it, the usage scenario, must-have requirements, explicit exclusions, and which shopping platforms the user is already logged into. An investment assistant needs to know the target, time horizon, risk tolerance, key indicators, and portfolio constraints.
Second, connect information sources.
University applications need admission datasets, university data, major information, and searches across official sources such as the Ministry of Education, Sunshine Gaokao, provincial exam authorities, and university admissions sites. Shopping requires opening e-commerce sites, reading product pages, reviews, Q&A, prices, and carts. Investing requires market data, financial statements, announcements, news, research materials, and one's own transaction records.
Third, analyze.
This is not simple summarization. It is comparison, filtering, validation, reasoning, and risk judgment. University applications need reach-match-safety tiers. Shopping needs a strongest recommendation and a clear "do not buy." Investing needs analysis of return drivers, valuation risk, and uncertainty.
Fourth, deliver the result.
A university application assistant should ideally deliver a PDF report that parents can save, forward, and discuss. A shopping assistant should ideally add the selected item to the cart, but not place the order or pay. Investment products can deliver research summaries, risk lists, monitoring dashboards, or rebalancing suggestions, but they should not cross the user's confirmation boundary and trade automatically.
So the essence of these three things is not "Q&A."
They are complete data analysis workflows:
Collect constraints -> acquire data -> analyze and judge -> deliver action.
This is the basic form of general data analysis applications.
Small Apps Are Only the Entry Point; the Heavy Part Is the Analysis Capability Behind Them
I have built several small apps inside infinisynapse.cn, and two of them are especially representative.
One is an AI assistant for gaokao university applications. The user enters province, score, ranking, target schools, target majors, city preferences, and family constraints on the page. The system sends that information to InfiniSynapse. InfiniSynapse then uses gaokao admission data sources, university data sources, and official public search capabilities to generate a Markdown and PDF application analysis report.
The other is a no-nonsense shopping AI assistant. The user enters what they want to buy, their budget, usage scenario, must-have requirements, and explicit exclusions, then selects the shopping websites where they are already logged in inside the current Chrome browser. After the task is sent to InfiniSynapse, the Agent uses browser capabilities to visit the selected platforms, reuse the current login state, compare prices, read reviews, inspect negative feedback, filter products, generate a shopping report, and add suitable products to the cart when appropriate.
On the surface, these are two different products.
But from an engineering perspective, both frontends are thin.
The frontend mainly does three things:
- Collect the user's input clearly;
- Configure the capability switches needed for the scenario;
- Display the result in the form that fits the scenario.
The truly complex part is not inside the small app.
The complexity is behind it: data source connections, knowledge base usage, web search, browser operation, file reading, task flows, workspace artifacts, report generation, PDF export, real-time progress synchronization, and user confirmation boundaries.
That is exactly the part InfiniSynapse is meant to handle.
The Key to Building General Data Analysis Applications with InfiniSynapse Is Extracting the Analysis Engine
Without a foundation, every general data analysis application has to be rebuilt from scratch.
If you build a university application assistant, you need to connect databases, organize admission data, write analysis logic, connect search, generate PDFs, and handle real-time tasks.
If you build a shopping assistant, you need to connect a browser, handle login state, open e-commerce pages, read reviews, compare prices, generate reports, and define the boundaries around carts and sensitive operations.
If you build an investment assistant, you need to connect market data, financial statements, news, announcements, research reports, knowledge bases, and historical data, while also producing risk disclosures and user confirmations.
If every application repeats all of this work, the cost is very high and stability is hard to achieve.
InfiniSynapse's idea is to extract the heaviest middle layer of analysis capability into a shared foundation.
First, you configure data sources inside InfiniSynapse: databases, knowledge bases, files, external data services, or domain datasets that can be subscribed to.
Then the small app only needs to collect the information specific to that scenario and send the task to InfiniSynapse.
InfiniSynapse takes the task and calls the capabilities it already has:
- Database connectivity;
- Knowledge base and RAG capabilities;
- Search;
- Browser use;
- File and image reading;
- Sandbox analysis;
- Multi-step task planning and self-correction;
- Workspace artifact generation.
Finally, the small app receives the result and delivers it in the form the scenario needs.
For university applications, that may be a PDF.
For shopping, that may be a report and a shopping cart.
For enterprise business analysis, it may be a dashboard.
For sales lead analysis, it may be a score and follow-up suggestion inside a CRM.
For investment research, it may be a risk list, monitoring table, and actions that require human confirmation.
The same foundation can support different delivery forms.
The Barrier to Building General Data Analysis Applications Will Drop Quickly
The truly important point is this: the development barrier for general data analysis applications will fall.
In the past, if you wanted to build a professional domain AI application, it was easy to fall into one of two extremes.
One extreme was to build only a chat interface. The user asks, the model answers. It looks fast, but once you enter real tasks, you discover there is no stable data source, no verifiable process, no downloadable result, and no clear action boundary.
The other extreme was to build a complete vertical system. You rebuild databases, business logic, search, report generation, and permissions for each industry. This can go deep, but it is expensive, slow, and hard to iterate quickly.
A general data analysis foundation offers a third path.
The application layer expresses the scenario.
The foundation layer handles information acquisition, analytical reasoning, and result delivery.
This means the core work of a new product shifts from "reinvent the analysis system" to three clearer questions:
- What constraints does this scenario need to collect from the user?
- What data sources and tool capabilities does this scenario need to enable?
- What result should this scenario finally deliver?
Once these three questions are clear, a small app can emerge quickly.
That is why I think many general data analysis applications will appear in the future.
They will not all look like "data products." Many may look like ordinary web pages, forms, mini programs, Chrome extensions, or internal enterprise buttons.
But behind the scenes, what they are doing is data analysis.
In the Era of General Data Analysis, Product Managers Need to Rethink "Data Sources"
In the past, when we said "data source," we usually thought of a database.
But in the era of general data analysis, the concept becomes much broader.
A web page is a data source.
A PDF is a data source.
A screenshot uploaded by a user is a data source.
An e-commerce backend behind a browser login state is a data source.
A knowledge base is a data source.
A user's spoken preferences are also a data source.
Even a boundary that says "what must not be done" is an important input.
For example, in the shopping assistant, "only add to cart, do not pay" is a key constraint. It is not analysis material, but it defines the Agent's action boundary.
In the university application assistant, "only analyze and recommend; do not log into the official application system; do not submit any choices" is also a key constraint. The higher the risk of the scenario, the more important the boundary becomes.
So a general data analysis application is not simply about throwing more data at a model.
It requires product designers to design data sources, user constraints, tool permissions, deliverables, and risk boundaries together.
A good general data analysis product does not only "analyze." It knows when to stop, when to mark uncertainty, and when it must hand control back to the user for confirmation.
This Will Change the Shape of Many Products
I think many products will be rebuilt over the next few years.
Education consulting will be rebuilt.
Not only gaokao applications, but graduate school selection, study abroad applications, career planning, and course selection all require putting user conditions, historical data, school information, policy changes, and personal goals together for analysis.
Consumer decision-making will be rebuilt.
Buying home appliances, buying cars, renovating homes, planning travel, choosing insurance, and selecting health check packages are not simple keyword searches. What users really need is an actionable plan based on their budget, scenario, and risk preferences.
Enterprise software will be rebuilt.
Sales, customer service, operations, finance, HR, and supply chain teams all have many "temporary but important" analysis tasks. Traditional software is good at workflows and records, but not at proactively pulling out data, analyzing it, and delivering judgment.
Investment research will also be rebuilt.
Of course, investing is high risk, and AI should not be mythologized as an automatic money-making machine. The more realistic direction is to let AI organize information, cross-validate evidence, flag risks, monitor changes, and support review, while leaving the final decision and transaction confirmation to people.
The common point behind these directions is this: problems of "not enough information, scattered information, slow analysis, and unactionable results" will be reorganized through a Data Agent foundation.
What InfiniSynapse Wants to Build Is the Foundation Layer for General Data Analysis Applications
I am building InfiniSynapse not merely because I want another "ask and answer" analysis tool.
What I really want to build is the foundation layer for general data analysis applications.
A developer, product manager, industry expert, or internal enterprise team should be able to take a concrete scenario and quickly build an application on top of this foundation:
Configure data sources.
Clarify the task objective.
Set tool capabilities and boundaries.
Collect user input.
Hand the task to InfiniSynapse.
Receive reports, files, charts, links, or other deliverables.
The significance of this pattern is that it prevents "professional analysis capability" from being locked inside a few data teams, and prevents it from appearing only as BI reports.
It can enter every concrete decision scene.
When a parent needs to fill out university applications, it is a university application assistant.
When someone needs to buy something, it is a shopping assistant.
When a company needs to analyze customers, it is a sales assistant.
When an investor needs to review a target, it is a research assistant.
The frontend forms can be completely different, but the underlying capabilities can be reused.
Closing
The world of general data analysis is arriving.
Not because everyone suddenly becomes a data analyst, but because more and more ordinary decisions have become complex enough to require data, tools, and Agents working together.
People will not learn SQL just to fill out university applications.
People will not learn web crawling just to buy headphones.
People will not build a data warehouse just to judge a company.
What they need is an application that connects information collection, analytical judgment, and result delivery.
And what application developers need is a foundation that can carry these complex capabilities.
That is why I am optimistic about general data analysis, and why I am building InfiniSynapse.
In the future, many applications that look unrelated may grow on the same kind of infrastructure.
University applications, shopping, investing, sales, operations, consulting, and research may look completely different on the surface, but underneath they are doing the same thing:
Turning scattered information into executable decisions.
Whoever can make this chain stable will become infrastructure for the era of general data analysis.