AI engineering · Life insurance distribution · Sydney
They stopped choosing which calls to listen to.
Life Insurance Direct has compared life cover for Australians since 2006, across nine insurers and six product types, and almost all of it happens on the phone. We built the system that reads every one of those calls, scores both sides, and hands the team a review queue in risk order instead of a sample.
At a glance
- Status
- Live in production
- What we built
- Metricsense, call quality and compliance QA
- Coverage
- Every call, scored on the customer side and the adviser side
- Deployment
- Their own cloud account, Australian region, personal information stripped before any transcript reaches a model
What changed
What changed
Before
They chose which calls to hear, then listened.
Now
Every call is read and scored first. Then a person decides what to open.
The call that most needed a listen was almost never the one that looked worth picking.
- Calls read, both sides, 16 Apr to 10 Aug 2026
- 9,218
- What hand picking reached before
- 2 to 5%
- Checks on every call
- 26
- Findings reported on its own
- 0
The ask
Why not just listen to more calls
The cheapest option was the one they were already running, and running properly. So the honest question was never whether hand picking works. It was what happens to the calls it never reaches.
Do the arithmetic
Around five hundred calls a week, so roughly two thousand a month. A careful manual process reaches two to five of every hundred. Reading the rest by hand is not one more reviewer. It is a department.
The lag is the other half
Feedback reached an adviser three to four weeks after the call it was about. By then they had taken hundreds more, the same way, with the same habit.
A sample only ranks what you suspect
Risk based selection is still selection. It sorts the calls a person thought to look at, so the one nobody thought to look at stays exactly where it was.
The hard part
The obvious build was the wrong one
Why that fails
The hardest check was a comparison against the insurer’s application. Hand a model a whole application form and attention lands on the front and the end. The middle gets skimmed, and on an insurance application the middle is where the medical questions sit, exactly the part that has to be checked word for word.
What we did instead
The check never sees the document. It sees one marked line range, with personal information already removed, and a single purpose built check runs against that section and nothing else. As the client’s QA lead put it: the less we give it, the less it makes up.
What we built
What it does now
Risk order, not date order
The queue is ranked. The highest risk calls get a person first, then the middle band, then the ones simply worth a look.
A new check is one sentence
Checks are written in plain English. No tagging exercise, no retraining. Deploy one and it runs across every call from then on.
Every score links to the moment
Open a score and it lands on the exact point in the transcript that produced it, with the audio playing alongside.
A quality index per adviser
A composite out of a hundred, across accuracy, satisfaction, resolution, compliance and communication. The spread inside one person is what tells you which axis to coach.
Where each person is on their journey
Not just how someone performs, but which areas they are weaker in than the floor. Onboarding and refreshers get aimed at the specific thing, not the whole team.
In their cloud, in their region
The whole thing runs inside their own account, in Australia, with personal information stripped before any transcript reaches a model.
The method
How we worked
01
Concepts, then scripts
Universal checks first, because they run on every call from day one. Call type scripts layer on top of something already working.
02
Their QA became the checks
Their own scripts and standard procedures came across marked three ways: said word for word, covered in your own words, or left to the adviser. Each item became one plain English instruction.
03
They marked it
Their QA lead reviewed calls beside the system, weekly, and told us where it was right and where it was not, with the reason. We tuned against real disagreements, not imagined ones.
04
The library grows
A review turns up something worth watching. It becomes a new check, written as a sentence rather than a retrain, and from then on it runs on every call. The loop closes in days.
What we left out
What we left out on purpose
The version of the application check that runs automatically on every application call is not built. It stayed a pilot on two marked up forms. Everyone assumed there was a clean structured record to check the call against, and there was not: their customer record holds around ten fields where the application itself collects a hundred.
A half connected version would have looked like a feature and behaved like a guess, on the one check where a guess does the most damage. It stays a pilot until there is a way to do it we would defend in front of their regulator, not just in front of them.
Built to last
Why it keeps working
No check changes until it passes calls the client has already marked.
- 01
The risk
The failure here is quiet. Nobody notices a QA system drifting until a reviewer opens three flags, finds all three were fine, and slowly stops opening them.
- 02
The guard
Before any check changes, it runs against a set of calls the client has already reviewed and marked.
- 03
Why it matters
This is a system being taught a business, and that marked set is what makes it a plan rather than a hope.
Beyond this client
The job transfers
The same problem turns up well beyond this one client. These are the teams it fits.
Anyone who sells on the phone
- Advice licensees
- Mortgage brokers
- Intermediaries
- Life insurance distributors
Anyone audited on the call
- Any regulated contact centre
- Any team reviewing a sample by hand
What the client says
Before Metricsense, checking calls was a manual process: we selected files for review. Now the platform analyses every call, and we review by risk. It has identified customer experiences we would not have picked up before, and it lets us scale our quality assurance program without increasing headcount, with our team still the human in the loop. It helps us protect our customers, our business and our agents.
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