Use Case
Your calls carry the sale, the fix, the promise. Read all of them.
Metricsense reads every call your team takes, scores the customer side and the agent side, and traces every score back to the exact moment in the transcript. It works the same way whether your calls close deals, fix problems, book appointments or carry a duty of care. Australian insurers and brokers have a page of their own, linked below.
The problem
When the call is the sale
Inside sales, brokers, dealerships, travel desks, estate agents. The quote goes out, the deal goes quiet, and the reason is somewhere in the call, not in the two-line note the rep typed after it.
When the call is the fix
Utilities, telco, banking, airlines, logistics. Something has already gone wrong, and this one call is where you find out whether the customer stays. Your reporting measures the handling time, not the handling.
When the call carries a duty of care
Clinics, aged care, allied health, education, financial services. What was said had to be accurate, and it had to be understood by the person on the other end. Months later, the only account of either is the call.
When the call is the renewal
Subscriptions, memberships, service contracts. The save conversation and the grumble three months earlier sit in the same archive, and nothing joins them up until somebody reads both.
When the call is the first thing they hear
Reception, bookings, appointment setting, dispatch. A caller who does not get booked in rarely complains. They ring the next number on the list, and the only account of it is the recording.
When the call is about money owed
Collections, arrears, accounts receivable. Tone and accuracy both have to hold, on the calls a team is least keen to have reviewed, and the one that gets either wrong is the one you hear about last.
How Metricsense solves it
- 1
Connect the calls you already have
AWS Connect and CSV are built in, and anything with an API can be connected. Historical transcripts load as a CSV export, so you can start with what is already sitting in your archive rather than with an integration project.
- 2
Your standard becomes a concept
You document how your team judges a call, edge cases included. We assess that document and build the AI agent from it. Your team then validates the output against their own judgement, and we refine it until the two match. No tagging project, no model training, no data science on your side.
- 3
Every call read, both sides scored
The customer side: the complaint, the reason for the call, what they were actually asking for. The agent side: process, clarity, empathy, and how the call was resolved. Every score opens the moment in the transcript that produced it, so nobody argues about what was said.
- 4
Month two: coach from what it found
A per-agent Call Quality Index sits on top of the scoring. The same concepts run across the whole team, so the area where one person is weaker than their peers stands out and the coaching goes at that area rather than at everyone. A new starter gets coached on the part of the call they have not got yet. Scores are role-based and built for that conversation.
- 5
Work the evidence, not a dashboard
A flag is a candidate issue with the evidence attached, not a finding. One of your people decides what it is, and nothing is auto-actioned. Flags go out to Jira, GitHub and Slack.
Frequently asked questions
The input is your standard, not ours. A clinic judging whether a caller understood their aftercare, a dispatcher judging whether a job was scoped properly and a collections team judging tone are all describing what a good call looks like. That description is what the concept gets built from, so nothing in the engine is tied to one industry.
Metricsense analyses transcripts. AWS Connect and CSV are built in, and anything with an API can be connected. If your phone system stores audio only, you will need a transcription service in front of it.
Yes. On the customer side: the complaint, the frustration, the reason somebody rang back. On the agent side: process, clarity, empathy and how the call was resolved, rolled up into a per-agent Call Quality Index. It exists to point a coaching conversation at the right thing. Scores are role-based and there is no leaderboard.
No. You document how your team judges a call, we build the AI agent from that document, and your team validates the output until it matches their judgement. A concept is readable plain English, so anyone on your team can see what it looks for and argue with it. That is the part a trained model cannot give you.
Into your own sovereign cloud: your tenancy, your region. Your recordings do not move. The model runs on Amazon Bedrock inside that same boundary, and PII is stripped before any transcript reaches it. Avesta Labs, who build and operate Metricsense, holds a SOC 2 Type I attestation. The Type II observation period is under way.
Metricsense has read and scored more than 10,000 calls on both sides at Life Insurance Direct, an Australian life-insurance broker, inside their own cloud. If you hold an Australian financial services licence, start at the call analytics page for AFS licensees instead. It carries the compliance detail and the pilot, and their CEO is on camera there.
Start with the calls you already have.
Bring a batch of transcripts from your own archive and see what the first concept finds on them.
