Stop guessing what to build. Your customers already told you.

Metricsense reads every one of your reviews, tickets and surveys, and answers any question you ask with the exact quote behind it. Ask one below.

Live demo, try it yourselfReviews · Tickets · SurveysSample data

Pick a question, or write your own, then press Run:

Reading every review and ticket…

Start free See pricing

✓Free to start, no card✓Every answer quote-linked✓First insights in minutes

One question. Every review that answers it, whatever language it is in.

The same finding, grouped across languages, counted once.

English"App crashes at every checkout since the update."

Spanish"La app se cierra en cada pago desde la actualización."

German"Die App stürzt seit dem Update an der Kasse ab."

Japaneseアップデート以降、決済のたびにアプリが落ちます。

Arabicيتعطل التطبيق عند كل عملية دفع.

Tagalog"Nagki-crash ang app tuwing magbabayad simula nang mag-update."

What it found

Two apps. The same number one complaint in both, sitting unread in the console.

A mobile app studio connected the Play Store reviews for two of its apps. Nobody on the team had been reading them. They arrived daily, in several languages, and there was no way to tell a one-off from a pattern.

Both apps came back with the same complaint at the top: ad frequency. Some of the reviews carrying it were four stars, from people who liked the app, said so, and marked it down anyway. That is the sentence a star average hides and a summary rounds off, and it is the one the team acted on.

On calls

The same engine reads spoken calls. Life Insurance Direct run their own through it, both sides scored, every flag pinned to the moment in the call that produced it. How call analysis works

Every source, one ranked view.

Analyzed together against the same question, so a theme is counted no matter where it was raised. Anything with an API can be connected. Anything else comes in as a CSV.

App Store
Play Store
NPS surveys
CSV upload

Then send it where the work happens: export themes to Jira and GitHub Issues, alert on spikes by Slack and email.

The difference

You describe what to find. No tagging, no model training.

You are not picking from a menu of categories somebody else wrote. You ask the question the way you would ask a colleague, it runs across everything you have uploaded, and the answer arrives with the quotes underneath it. The way you just tried above.

Reviews

"Show every complaint about checkout from reviews since the last release, with the quotes."

Tickets and surveys

"What feature are enterprise accounts asking for most across tickets and NPS comments?"

The questions you're already asking.

Where does our data go?

Your feedback is never used to train models. You control retention and deletion, and you can export or purge everything at any time. Avesta Labs, which builds and runs Metricsense, holds a SOC 2 Type I attestation, and the Type II observation period is under way.

Is this another setup project?

No tagging, no taxonomy to agree on first, no data team. Connect a source or upload a CSV, write what to find in plain English, and it runs. You are reading insights in minutes, not standing up a project.

Will it work on our feedback?

Find out before you pay for anything. Start free, upload a sample of your own reviews or tickets, and read the answers and the quotes behind them.

Can we trust the output?

Every insight opens onto the customer words behind it. If a theme is ranked first, you can read the feedback that put it there, which is the only good reason to believe a ranking.

Analyzing spoken calls on a regulated floor? That lives on the call analytics page.

Pricing

Start free. Upgrade when the volume does.

Every plan reads everything you upload and links every insight to the exact quote behind it. Credits are consumed per conversation analyzed. Call analysis on a regulated floor is priced separately, on call volume.