We run AI voice agents in production for Australian call centres — under DNC rules, on AU-hosted infrastructure, across regulated industries. When we advise your team, it's from a system we operate, not a slide we drew.
Specific, scoped engagements — not a transformation programme. Each maps to a decision you're trying to make.
Where an agent actually pays off versus where it burns trust. We map your call types, volumes, and failure costs, then tell you which ones are worth automating now — and which to leave human.
We've built the stack and integrated the vendors. We'll give you an honest read on where an off-the-shelf agent gets you 80% of the way, and where owning the pipeline is the only way to hit your bar.
Telephony, latency, barge-in, call transfer, CRM write-back, fallback to a human. The unglamorous integration work that decides whether an agent survives contact with real callers — we've already debugged most of it.
How you measure whether an agent is good, before and after it ships: transcript review, sentiment, containment, escalation rates, regression tests on prompt changes. Guardrails that catch a bad turn instead of finding out from a complaint.
We operate under DNC.gov.au filtering, consent capture, and AU data residency every day, across financial services, insurance, and health. We help you stand up the same controls — audit trail included — so legal signs off once, not every campaign.
Evidence at every gate. Each stage has to earn the next — including the right to stop.
We sit with your team, your call data, and your constraints. Out the other side: a ranked list of candidate use cases, a build-vs-buy call on each, and the compliance shape you'll need. If nothing's worth doing yet, we tell you — that's a real outcome.
One narrow, measurable use case, shipped to a slice of real traffic with guardrails and a human fallback. We agree the success metrics up front — containment, accuracy, sentiment — and review transcripts together weekly. Kill it or scale it on evidence, not vibes.
Roll out with monitoring, regression tests on every prompt change, and an escalation path that lands on a person. We can hand the keys to your team, run it for you, or anything in between — and we stay on the hook for the parts that are ours.
Getting a model to talk well is a solved problem. Getting it to transfer a call cleanly, write the right field back to the CRM, and degrade gracefully when the caller goes off-script is where most deployments quietly fail. Budget for the plumbing, not the prompt.
An agent that handles 90% of calls but mishandles the wrong 10% can cost more than the humans it replaced. We weight evaluation by the cost of being wrong on each call type, not by how many calls never reached a person.
In regulated AU industries, DNC filtering, consent capture, and data residency can't be bolted on after a pilot works. We design them in from call one — it's slower up front and far cheaper than re-architecting after legal has a look.
Pointing AI at the repetitive, morale-killing calls and keeping humans on the ones that need judgement isn't just the responsible stance. It's the deployment that actually survives, because your team defends it instead of working around it.
We won't help you point AI at work that needs human judgement, and we'll say so when a use case isn't ready. It's the responsible position — and, in our experience, the one that actually ships and sticks. If you want a vendor who'll tell you everything is automatable, we're the wrong call.
Tell us what you're weighing up — whether to build, what to automate first, how to keep a regulator comfortable. Drop your number and a senior person who actually runs this stuff calls you back.
