Nekotag
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User feedback, labelled

Every comment gets its TAG

Nekotag gathers what people write about your apps, from app stores such as Myket and Cafe Bazaar, from a support desk, or from any source you set up in the panel. It has an AI label each comment with your team's own tag list, and shows what changed from one month to the next.

  • Persian first, Jalali dates
  • Your tags, your prompt
  • Runs on your server
Anysource and source type you add in the panel: stores such as Myket and Bazaar, support desks, more
1–3tags on each comment
20comments per AI call, or your own number
½the price for months sent as one batch

How it works

From a store review to a monthly trend, in three steps

A job names the products, the sources and the Jalali months. Nekotag does the rest in order, and the Jobs page shows each step as it happens, with what the AI calls cost.

STEP 1

Collect

Each source is set up in the panel: its type (an app store, a support desk, or one you add), its platforms and its login. Myket and Cafe Bazaar reviews come from their developer panels, support tickets from the desk's database. Each comment is stored once, with its rating, app version and platform.

MyketCafe BazaarSupport desk+ your own sources
STEP 2

Tag

Comments go to the AI in batches, with your tag list and prompt. Each answer is checked: a tag pair that isn't on the list, or an invented comment id, never gets saved.

Live or batchRetries for misses
STEP 3

Explore

Reports and Explore turn the tags into shares, trends and comparisons, down to the comments behind each number. Everything exports to CSV that opens cleanly in Excel, Persian included.

Compare periodsTag deep-diveCSV

What it guarantees

Built so a paid answer is never lost and a comment is never miscounted

Tagging costs money and the numbers end up in decisions, so the pipeline is strict about both.

Taxonomy

Tags you own

Each product and source has its own tag list and prompt, edited in the panel. Every saved prompt version is kept, and each run records the exact one it used.

Validation

Only real tags

The AI's answer is matched exactly against the list. Anything else is dropped with a warning, so a typo in a tag name can't start a new category.

Cost

Paid once

Every answered batch is saved before the next one goes out. A stopped or crashed job resumes where it left off, and an outage waits instead of throwing rows away.

Fit

Sized to the model

Each call stays inside the model's context window, with room for the answers. A prompt too large for it stops before anything is spent.

Language

Persian first

Right-to-left screens, Jalali months and dates, and every word in both Persian and English, including the server's messages.

Access

Locked by default

Sign-in with two-step codes, sessions that only the server can read, admin-only setup pages, and no button that deletes data without showing what it will remove.

Explore

See which complaints grew, not just how many there were

  • Single report. Main tags, sub tags and daily volume for any period, with sample comments under each.
  • Compare two periods. Which sub tags gained or lost share between two months or date ranges.
  • Tag deep-dive. Every comment behind one tag, 50 to a page, and all of them in one CSV.
Change in share, Farvardin → Abanexample figures
Sub tagShare AShare BChange
پخش ویدیو - قطعی8.4%13.1%+4.7
دانلود - خطا3.9%6.2%+2.3
قیمت اشتراک11.0%9.6%−1.4
مصرف اینترنت20.3%13.8%−6.5

Change is in percentage points of all tags in each period.

Alerts, Mehr against the 3 months beforeexample figures
WhatSub tagNowUsual
Upپخش ویدیو - قطعی13.1%7.9%
Newپرداخت - خطا4.2%0%
More negativeقیمت اشتراک71%48%
Downمصرف اینترنت13.8%19.6%
91%right when it tags
86%of the right tags found
v7prompt scored

Sentiment rows count the AI's negative answers for that tag; the score comes from comments your team checked.

Alerts and quality

Hear about a jump before anyone opens a report

  • Alerts. Sub tags whose share of the latest month jumped, new ones, and ones that fell back, worked out again after every job and counted in the menu.
  • Sentiment per tag. Each tag also says whether the comment is negative, neutral or positive about that topic, so praise and complaints about the same feature are told apart.
  • Quality score. Your team checks a random sample; every prompt version and model gets a score on the same comments, so a change is measured before it is trusted.

Read your users the way your team already talks about the product

Nekotag runs on your own server with your own database. Set the sources, write the tag list, and the first month is labelled in an afternoon.

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