How do you turn NPS comments into themes, not an unread sheet?
An NPS AI Employee that reads every new survey response each week, clusters the free text comments into themes with sentiment and detractor drivers, tracks how the score moved, and posts one summary with representative quotes to your customer success channel. It reads the survey sheet and writes nothing but the post.
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Every NPS or CSAT response is really two signals: a number and a comment. The number is easy to track. Most survey tools already chart it. The comment is where the 'why' lives, and it's the part that gets lost. Free text feedback piles up in a spreadsheet export, one row per response, and nobody reads all of it every week, so the same complaint from a dozen different detractors never gets counted as one thing. By the time a theme is obvious to a human skimming the sheet, it's been building for months. The common approaches don't fix this. A dashboard shows the score moving without saying why. Someone skimming the sheet by hand catches the loudest complaint, not the most common one, and does it inconsistently from week to week. And because nothing tracks a theme over time, a driver that's been growing for a month looks identical to one that appeared once and went away.
The NPS AI Employee runs once a week inside your own environment, with read only access to the sheet the survey tool exports responses into. It reads the full response history, clusters the free text comments into themes, tags each response's sentiment and score band, isolates what's actually driving detractors this week, and computes how the score moved week over week. It posts one summary to Slack with representative quotes. It writes nothing back to the sheet and never contacts a respondent.
See exactly how the work gets done.
Runs on a weekly schedule
A scheduled run fires once a week. Each run starts clean. The sheet itself is the record, so themes, sentiment, and the score trend are recomputed from the full response history every time.
Works to your analysis rules
How to cluster feedback and read the score travels with the AI Employee as a skill: what counts as a detractor driver versus a one off complaint, how to pick a representative quote, where the promoter, passive, and detractor bands sit, and how to describe a week over week move. Refine what a theme should look like and the next run follows it.
Connects to your systems, read only
It reads the full export of NPS and CSAT scores, comments, and timestamps from Sheets read only, and posts one weekly summary to Slack: the score and its trend, the top themes with quotes and counts, and the leading detractor drivers. Credentials stay in memory, never written to disk, never exposed to the model.
Reports, never edits, never replies
It never edits a row, adds a column, or writes back a score, and it never reaches out to a respondent. Its only output is the Slack post, and that post is a report, not an action.
Posts the weekly summary
Each week brings one Slack post: the current score and how it moved since last week, the themes behind the comments with a representative quote and count for each, and the drivers pulling detractors down this week. The customer success team reads it and decides what, if anything, to act on.
Runs in your environment
Every run is isolated inside your own infrastructure. It can reach only the sheet it's scoped to, and only the Slack post leaves it.
Read only, report only
Its access to the survey sheet is read only. It cannot edit a response, add a row, or write back a score, and its only output is a report to Slack.
Never contacts a respondent
It reads the responses and reports on them. It never emails, messages, or otherwise reaches out to the person who left the feedback.
Credentials stay contained
The Sheets and Slack credentials connect with the permissions you set, stay in memory, are never written to disk, and are never exposed to the model or the logs.
You own every rule
The clustering rules, the scoring bands, and the per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
Survey comments that used to sit unread in a spreadsheet now arrive every week as a set of named themes with quotes and counts, next to the score's actual week over week move and what's driving it. The AI Employee only reads and reports; the team still decides what to do about each theme.
Every week
Themes and score trend recomputed from the full response history
Read only
Nothing written back to the survey sheet
1 post
Score trend, themes, and detractor drivers in one Slack message
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