16 genuine escalation risks, inside 5,438 calls. None of them a call a sample would ever have reached.
One analysis window at an Australian life-insurance broker (name withheld by design). We do not name our clients: your calls and your results stay just as private.
They ran every one of their own calls through Metricsense, inside their own AWS account, and found a systemic gap no 2–5% sample could surface: a recurring, coachable misexplanation of premium structures. One scripting fix, not 98 separate problems. The metrics were theirs, written for their licence and their products. The engine is the same for any business that runs on calls.
No tagging project, no model training, no data team required.
AWS Connect for calls. App Store and Google Play for reviews. CSV or API for anything else, from any system that can export. Everything stays in your own cloud.
Plain English, one sentence: "Flag every call where the agent promised a callback that never got booked." That sentence is the whole setup.
Every finding links to the exact moment it happened: ready for coaching, a script fix, or an audit trail. No black box to argue with.
First insight in under an hour, on your real data.
The same engine reads spoken calls and written feedback.
One question. Every conversation that answers it, whatever language it is in.
The same finding, grouped across languages. Validated on your own languages during the pilot.
When an AI reads your customers' conversations, "trust us" is not good enough.
The whole platform deploys inside your own cloud account, in your region. Nothing leaves your environment.
Personal information is removed before any transcript reaches the model. You control retention and deletion.
Every flag pins to the exact transcript moment against a versioned insight definition. Auditable, never a black box.
A flag is a candidate issue with evidence, not a finding. We report nothing to a regulator on our own.
No tagging, no model training, no data team. You write what to find in plain English and it runs.
Start free on your own feedback, or pilot on a sample of your own calls, scored against your own QA and deleted on request.
Buying with compliance or IT in the room? Send them the trust & security details.
The five questions that come up before every pilot.
Every finding is pinned to the exact quote in the transcript, and a flag is a candidate for your team to confirm, not a verdict. During a pilot, accuracy is measured against your own QA on your own calls before you rely on it.
No. Your conversations are processed to answer your questions and nothing else. They are never used to train models, the platform runs inside your own cloud account, and PII is stripped before any transcript reaches the model.
AWS Connect for call centres, App Store and Google Play for reviews, and CSV import or the ingest API for calls, chats, tickets and surveys from any other system. If you can export it, Metricsense can read it.
The engine reads conversations across languages and groups the same finding wherever it appears. We validate it on your own languages during the pilot, before you rely on it.
A fixed fee agreed upfront, a sample of your own calls, run inside your own cloud, scored against your own QA, deleted on request. You see it work on your data before you commit to anything.
Ask us directly: hello@metricsense.ai. A founder answers, not a bot.
A fixed-fee pilot on a sample of your own calls, or start free on your written feedback.
Fixed fee, agreed upfront · No credit card for the free tier
Prefer email? hello@metricsense.ai