How we turn NPS responses into themes
A weekly cron reads new NPS/CSAT survey responses from a Google Sheet, clusters them into themes with sentiment and detractor drivers, and posts the score trend and representative quotes to Slack.
NPS and CSAT surveys generate a response every time someone answers, and most of it goes unread. The score gets tracked in a dashboard, but the comment next to it — the actual reason someone gave a 4 instead of a 9 — sits in a spreadsheet row and never gets aggregated with the hundred other comments saying the same thing. By the time a theme is obvious to a human skimming the sheet, it's been building for months.
We run an NPS analysis agent on Kortix that reads new survey responses every week and posts the themes, sentiment, and detractor drivers to our customer-success Slack channel, alongside how the score moved since last week. It only reads the survey sheet; the single output is the Slack post. This is how we keep our own NPS program from turning into an unread spreadsheet.
The problem
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.
The common approaches don't fix this. A dashboard shows the score moving without saying why. Someone skimming the sheet manually catches the loudest complaint, not the most common one, and does it inconsistently from week to week. And because nothing tracks the theme over time, a driver that's been growing for a month looks identical to one that appeared once and went away.
What we built
On Kortix, a weekly cron triggers an agent. It spawns an isolated session (a cloud sandbox) with read-only access to the Google Sheet the survey tool exports responses into, 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 has moved week over week. It posts one summary to Slack with representative quotes. It writes nothing back to the sheet.
How it works
Run on a weekly cron
A cron trigger fires the agent once a week. Each firing spawns a fresh session in its own sandbox. There's no memory carried between runs — the sheet itself is the record, so the agent recomputes themes, sentiment, and the score trend from the full response history every time.
Give the agent the analysis rules
How we cluster feedback and read the score lives as skills and memory that travel with the agent: what counts as a detractor driver versus a one-off complaint, how to pick a representative quote for a theme, where the promoter, passive, and detractor bands sit, and how to describe a week-over-week move. When we refine what a theme should look like, we write it down and the next run follows it.
Connect the survey sheet and Slack
Through a scoped connector, brokered server-side so no raw token reaches the model, the agent reads:
- Survey responses from Google Sheets — the full export of NPS/CSAT scores, comments, and timestamps, read-only.
- Posts to Slack — one weekly summary: the score and its trend, the top themes with quotes and counts, and the leading detractor drivers.
Set the guardrails
The agent is read-only on the survey sheet — it never edits a row, adds a column, or writes back a score. It never contacts a respondent. Its only output is the Slack post, and that post is a report, not an action. Credentials are encrypted in the Secrets Manager and injected at runtime, never shown to the model or written to logs.
Post the weekly summary
With that in place, each Monday brings one Slack post: the current NPS/CSAT 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.
The pattern
A weekly cron spawns a fresh session with a read-only connector into the survey Google Sheet. The clustering and scoring rules live as skills and memory. The agent reads the full history every time and writes nothing but the Slack post.
Guardrails
The agent reads a live survey export, so its access is scoped and one-directional:
- Isolation. Every run happens in its own microVM sandbox. The session can reach only the sheet it's scoped to, and only the Slack post leaves the sandbox.
- Scoped secrets. The Google Sheets and Slack credentials are encrypted in the Secrets Manager and injected into the sandbox at runtime, never exposed to the model or the logs.
- Read-only, report-only. The connector into the survey sheet is read-only — the agent cannot edit a response, add a row, or write back a score — and it never reaches out to a respondent. It reports; it doesn't act.
- Everything is code. The agent's clustering rules, skills, and per-system permissions are files in the repo, versioned and changed through a reviewed change request rather than a dashboard setting.
The outcome
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 agent only reads and reports; the team still decides what to do about each theme.
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