WorkBuddy's real limits in finance: I tested it with CITIC Securities research reports

Let's get straight to the point: WorkBuddy's efficiency in handling financial documents—and its pitfalls—far exceeded my expectations. To put it clearly, it can save you 60% of your time, but for that remaining part, if you haven't thought things through beforehand, it could cost you even more.

Last Tuesday, I dragged all the research reports from CITIC Securities' homepage from the past month into WorkBuddy, hoping it would automatically create a summary library. My requirement was simple: break down these dozen or so reports by industry chain, investment recommendations, and risk warnings to form a review template, saving me from manually flipping through them every week.

The result? Directory recognition, author information extraction, and core viewpoint summarization were handled quite beautifully by WorkBuddy, with about 90% accuracy. But I found an interesting issue: it recognized all the valuation model data in the reports as text rather than numbers. This meant that when I later tried to create pivot tables, I couldn't directly reference the data. That was definitely a pitfall.

I did a quick check, and the problem lay in the PDF layout itself. CITIC Securities' research reports have a characteristic where table lines and text spacing are very tight, plus the number fonts are special monospaced fonts. When WorkBuddy's OCR engine processes this kind of dense layout, it prioritizes text integrity at the expense of table structure recognition. In other words, it read the tables as continuous text.

This incident made me rethink the real boundaries of WorkBuddy in financial scenarios.

First, let's talk about its strengths. The two scenarios I use most frequently are organizing research report summaries and merging statements.

For the former, WorkBuddy has an almost overwhelming advantage. It can read an 80-page deep-dive industry report in 5 seconds and then output sections like industry chain, core targets, valuation range, and risk warnings according to my preset template. The efficiency boost is obvious; manual organization takes at least 40 minutes, whereas now it takes about 15 minutes. The saved time is enough to do more valuable work.

However, this leads to the second issue I want to discuss. WorkBuddy's summaries tend to be overly faithful to the original text, lacking judgment on market sentiment. For example, in the strategy report "The Counter-Attack Will Continue" dated August 18, WorkBuddy extracted the optimistic expressions verbatim but didn't tell you that before the report was published, the market had seen shrinking volume for two consecutive days—a classic signal of zero-sum game dynamics. After staring at WorkBuddy's output for a while, I couldn't help but laugh. It's an excellent secretary, not an analyst.

Now, regarding its weaknesses. Table processing and data consistency checks have always been what I consider WorkBuddy's biggest shortcomings. In the July 31 Liangma Portfolio report, there was a position adjustment table. After WorkBuddy merged it, the entire table structure collapsed, with the remarks column and weight column misaligned, and number formats messed up. I mentioned this in a previous post, and surprisingly, after a week, the latest version still hasn't shown significant improvement.

Interestingly though, converting the PDF to Excel first and then feeding it to WorkBuddy greatly alleviates this problem. I don't know if the backend uses a different parsing pipeline, but in practice, after routing through Excel, table recognition accuracy jumped from about 70% to over 95%. Specifically, number and date formats rarely go wrong. The only costs are spending an extra two minutes on conversion and occasionally having formulas in the source Excel file turn into static values, which adds some extra workload for subsequent audit trails.

So my current workflow is: use WorkBuddy directly on the original PDF files for research report summaries and qualitative content; convert to Excel first before feeding numerical tables. This process has been running for two weeks, boosting overall efficiency by roughly 37.5%, while reducing the error rate from an initial 30% to under 5%.

In other words, WorkBuddy's value to financial professionals essentially redefines the front end of information processing. It compresses mechanical tasks like reading and excerpting to near-zero cost, which I consider a high-ROI investment. But its ceiling lies in the fact that when you move to the second or third layer of information processing—such as cross-report consistency comparison, data correction, or logical chain inference—it clearly struggles.

I also compared ChatGPT and Claude's performance in this scenario. ChatGPT has strong general summarization capabilities but weak format control; outputs often exceed my set templates, requiring secondary cleanup. Claude has the best depth of understanding but slightly lags in supporting professional terminology in Chinese research reports, mistranslating "CAPEX" as centralized procurement. WorkBuddy is the best fit for office scenarios among the three but is also the most dependent on user foresight. You need to clearly tell it where the boundaries are; otherwise, it will confidently output two thousand words in the wrong direction.

Returning to my initial question: What are the real boundaries of WorkBuddy in handling financial documents? My current judgment is that it's suitable for the first rough pass, not for final decision-making basis. It's good for information aggregation, not for logical inference. It's suitable for users familiar with the format, not for those who just toss in a PDF and wait for a finished product.

Looking at the trends mentioned in CITIC Securities' AI industry report, model iteration cycles are accelerating, and enterprise-level scenario implementation is speeding up too. I understand the inevitability of this direction because I myself am one of those users spoiled by efficiency and computing power. But precisely because of this, I hope WorkBuddy focuses on table structure recognition and cross-document consistency rather than stacking more flashy automation features. After all, in financial scenarios, getting data wrong is far scarier than doing it slowly.