Before AI became part of my daily work, I often wondered whether anyone could truly hear customers at scale. I worked around user research and public relations, where the internet never sleeps but the team is always small. People shared opinions about products on Xiaohongshu, Douyin, TikTok, Reddit, and other platforms. In that endless stream were early warnings, unmet needs, and ideas for what a product could become. Finding them was the hard part.
My day could disappear into opening tabs, scrolling, taking screenshots, translating slang, and copying comments into spreadsheets. One post might be a passing complaint, or it might point to a problem many people were experiencing. A joke could contain a serious frustration. Context mattered, but context took time, and there was never enough time to read everything.
So we made choices. We picked posts that seemed representative, condensed them into a few themes, and added them to weekly or quarterly reports. Sometimes decision makers already had a direction in mind, and our job seemed to be finding online comments that supported it. It was easy to feel less like a researcher and more like someone polishing a decision that had already been made. I wanted to tell my boss, “This is what our users are really saying—and I think we’re getting it wrong.” But could I be sure I had the full picture?
We were also expected to spot risks early, before a small complaint became a public crisis, and to find feedback that could improve a product or inspire something new. Those jobs depend on understanding what people mean, not merely counting keywords. Yet with limited staff and a flood of posts, careful listening often gave way to quick summaries. Much of my time went to collecting and sorting instead of thinking about what users needed.
Then I tried Codex with a dataset I had already prepared. I expected it to save a little time. Instead, it organized comments, grouped recurring complaints, surfaced patterns, and turned a messy file into a readable analysis in minutes. I could ask follow-up questions and look at the findings from different angles without starting over. Work that might have taken an afternoon suddenly felt manageable. For the first time, I imagined spending more energy on what the feedback meant, rather than just getting it into a report.
But when I asked Codex to continue the research beyond the data I had supplied, I noticed a problem. The results no longer came from the target communities I cared about. They drew on broader web content, where material optimized to rank well in generative AI results could be unusually visible. The answers sounded polished and confident, but that was not the same as listening to people on Xiaohongshu, Douyin, TikTok, or Reddit. A model can analyze what it sees; it cannot automatically know whether those sources represent my users or whose voices are missing.
I realized that asking a general-purpose AI to research the web was not enough. If I wanted dependable insights, I needed to make the data collection itself more deliberate. So, with Codex, I began building a tool to gather social media posts and comments from the platforms we wanted to study, then organize them for analysis. That project became CCNeed.
CCNeed is designed to bring collection, organization, analysis, and reporting into one research workflow. Instead of manually searching each platform and pasting comments into a spreadsheet, a researcher can start with a question, choose relevant platforms and keywords, and set the scope of the data collection. Depending on platform access and research scope, the tool can gather posts and comments while retaining source links, dates, and engagement information. It can then help filter irrelevant material, remove duplicates, and group feedback by topic or sentiment.
Imagine that you are researching a water bottle whose sales are disappointing. You might ask: “What do customers dislike most about this bottle?” The tool can bring relevant conversations together, helping you compare complaints about insulation, design, price, size, leaks, or a difficult lid. You can ask follow-up questions in plain language, inspect the posts behind a pattern, and turn the findings into a report your team can review. When a conclusion needs checking, you can return to the original post instead of treating the summary as a fact on its own.
Building this with Codex changed how I think about AI. Its speed helped me move from an idea to a working product much faster than I could have alone. But the important lesson was not that AI can replace research. It was that AI needs a well-designed path to real evidence. Collection, analysis, and source tracing have to work together, and researchers still need to check context and challenge conclusions that sound too neat.
Can we gather massive product feedback in the AI era? I believe we can, if we know where the data comes from and can trace insights back to the people who expressed them. The goal is not simply to produce more summaries. It is to hear users more carefully and to give the people doing this work enough confidence to tell decision makers what they may not want to hear.
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