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Sai searches X over a set window, skips retweets and low-signal posts, and groups what remains into competitor moves, customer complaints, people asking for what you do, and industry news. Each post carries its link, engagement and a reason. Replies are drafted but not posted.
The recording is a real session. The sheet on the right is what it produced.
Sai opens each profile, pulls the signal, and writes the row, live, in a real browser.
Eight columns, sorted by score, with a source link behind every claim.
The keywords, @handles or brand name to search, and a time window. An X account for reading search results.
Posts grouped into four categories, each with its link, engagement, a reason, and one of three action calls — worth replying to, worth monitoring, or no action. Drafted replies for the first group only, held for approval. Plus the search terms used and how many posts were reviewed.
Run it each morning over the previous 24 hours. A daily window keeps reply timing inside the period when a thread is still active.
X's search supports operators that narrow results substantially. from: restricts to an account, min_faves: sets an engagement floor, since: and until: bound a date range, -filter:retweets removes retweets, and -filter:replies removes replies. They combine, and a well-constructed query returns a usable set.
The operators are documented and free to use. For a one-off search — checking whether a specific thing was mentioned, finding a post someone described — typing the query is the direct path.
The constraint is that a search returns a list. Running the same query each morning means reading the list each morning and deciding, post by post, whether anything warrants a response.
Brand24, Mention, Brandwatch, Hootsuite and Sprout Social monitor mentions across platforms — X, Reddit, news, blogs, YouTube — with historical data, volume trends and sentiment classification.
Their coverage is broader than a single platform, and their history is longer than any single run. For tracking mention volume over quarters or comparing share of voice, they do what a per-run task does not.
Their output is a feed and a set of metrics. A mention count and a sentiment split describe the state of the conversation. Neither indicates which specific post to reply to, and the feed still has to be read.
The cost of finding posts is low. Search operators are free, and monitoring tools automate the query.
The remaining cost is judgment: reading each post, deciding whether a reply would be useful or intrusive, and writing something specific enough to be worth posting.
On X this cost is time-bounded. A post asking whether a tool exists is answerable for a few hours. A complaint is worth responding to while the thread is active. A feed read at the end of the day contains the same posts, mostly past the point where a reply lands.
This task performs the judgment step and drafts the output of it.
Posts are sorted into competitor moves, customer complaints, people asking for what you do, and industry news.
The groups correspond to different responses. A buying-intent post is worth a direct reply. A complaint about a competitor is context, not an opening. Industry news may be worth amplifying. Competitor moves are usually for reading, not replying.
An undifferentiated feed requires that classification to happen during reading, every time.
Posts from your own account are included and marked as yours.
In the example run this produced a finding the four categories would not have:
"Our own posts are the top of this search — @xwang_lk and @SimularAI, ~790 likes / 1.1M views combined on the OSWorld 2.0 #1 announcement. The 24h conversation about computer-use agents is currently substantially our conversation."
A monitoring tool would count these as mentions and raise the volume figure. Reported separately, they mean something different: the search term is currently dominated by your own output, which indicates the term is not yet showing independent third-party discussion.
Third-party amplification is distinguished from original mentions. From the same run, on a post citing the OSWorld result: "Third party amplifying our own result. Why it matters: organic advocacy — worth a light touch, not a hard sell."
Nothing is posted. Drafts are produced and left for review.
The example run labels this in the output heading: "Drafted replies (not posted — your approval)."
This is partly a platform constraint — X's rules restrict bulk automated replies and spam-like engagement behaviour — and partly a quality one. A reply to a specific post is worth posting when it engages with what the post said. Drafts are written per post rather than from a template, and reviewing them takes less time than writing them.
Plain register, no marketing language, specific to the post. From the example run, in reply to a post about agent and human browser separation:
"This is the thing people underrate. If the agent shares your browser, one bad click is your session, your cookies, your logged-in tabs. We build Sai the other way — the agent gets its own isolated computer, you keep yours. Watch it work, approve what matters, nothing touches your machine."
Each draft is editable before posting. The intended saving is on the blank page, not on review.
Posting. Drafts are held. Nothing is sent.
Real-time monitoring. It runs over a window. For live columns, TweetDeck is the direct tool.
Other platforms. X only. Reddit sentiment analysis covers Reddit separately.
Historical trends. It reports one window. No volume history, no trend line.
Sentiment scoring. Posts are grouped by type and marked with an action, not scored on a sentiment scale.
Follower or account analytics. It searches posts.
A daily morning run over the previous 24 hours keeps reply timing inside the window where threads are still active.
The search terms and the count of posts reviewed are reported each run, so a change in output volume can be attributed — fewer posts because the conversation quietened, or because the terms stopped matching.
For brand presence in AI-generated answers rather than in social posts, checking whether ChatGPT cites your brand covers that. The same draft-but-do-not-send handling applies to LinkedIn messaging.
