We help New Zealand businesses turn everyday operational chaos into clarity — not with dashboards you never check, but with answers that change how you run things tomorrow.
Our approach
We start with your problem, not our technology
Every business thinks its situation is unique — and it usually is. We don't sell a product off the shelf. We sit with you, understand what's actually going wrong, and build from there.
Discovery
We learn how your business actually works — not how a manual says it should. Where are the bottlenecks? What's falling through the cracks? What would change if you could see clearly?
Connection
We connect the systems you already use. No ripping and replacing, no new platforms to learn. Your existing tools start talking to each other — often for the first time.
Clarity
Scattered data becomes a single operational picture. You see what's actually happening in your business — backed by evidence, delivered in plain language, ready to act on.
Case study
A New Zealand services company — before and after AI
In December 2025, a traffic management company with 30+ staff and 18 vehicles lost its sole accounts administrator. By February 2026, the immediate crisis was clear: cost sheets built by hand from the job scheduling system, consuming 3 to 4 days of someone's week. Within two weeks of engagement, that pipeline was automated end to end. That single proof of value opened the door to everything that followed. Within twelve months, ~$200,000 in combined salary costs for an accounts administrator and operations manager were absorbed entirely by automation.
In salaries absorbed in year one
Accounts administrator and operations manager roles — neither replaced
Of human work effort saved per year on scheduling alone
286 hours — nearly 8 working weeks a year given back to the business
Recovered on day one
Months of backdated revenue found through data matching
Of fleet telemetry analysed
18 vehicles across 52 weeks — utilisation, cost & seasonal patterns
Aged debtors — down from months
Automated invoicing & follow-up replaced manual chasing
Of manual invoicing effort — replaced
Runs 24/7, never phones in sick, never takes leave
The full story
How it happened — week by week
This isn't a hypothetical. It's a real engagement with a real New Zealand business, told in the order it unfolded.
The accounts administrator resigned
She was the one person who held the business together behind the scenes. Invoicing, accounts payable, crew scheduling, client communications, fleet coordination. It all lived in her head, her inbox, and a tangle of spreadsheets. When she gave notice, the business faced losing both the revenue pipeline and the institutional knowledge at the same time.
The most immediate pain was invoicing. Cost sheets built by hand from the scheduling system, then manually entered into the accounting system. Three to four days of someone's week, every week. And months of invoices were sitting unissued. The owner needed a solution, not another hire.
Cost sheets and invoicing: from 3–4 days a week to minutes
The most urgent problem was invoicing. Cost sheets were being assembled manually from the job scheduling system (Connecteam), then keyed into the accounting system (Xero) by hand. It consumed 3 to 4 days of someone's working week, every week. We automated the entire pipeline: jobs completed in Connecteam flow straight through to costed, approved invoices in Xero. The backlog cleared immediately, recovering 141 invoices that had gone unissued for months.
This was the proof of concept. The business owner could see, in concrete terms, what AI-enabled software could do for a core business function. It ran 24/7, never phoned in sick, and handled in minutes what used to take days. That result gave the green light to start looking at every other part of the accounting function.
Accounts payable and remittances followed
With the invoicing proof of concept in hand, the owner opened the door to the rest of the accounting function. Accounts payable was next: matching purchase orders to supplier invoices, flagging discrepancies, scheduling payments. Then remittance tracking. Each function was tackled the same way: understand the manual process, automate it, prove it works.
Two telemetry providers, one unified picture
The fleet ran across two separate telemetry providers, EROAD and Cartrack. Neither talked to the other. We merged them seamlessly into a single historical view of time, distance, and fuel consumption across all 18 vehicles. For the first time, the business could see exactly where every vehicle had been, for how long, and at what cost.
The first visual report led to an immediate business decision: a new vehicle usage policy. By accurately mapping crew on-site times to job locations and matching vehicle distances to individual jobs, the business had the framework to introduce a fuel surcharge per job, with each vehicle's actual fuel consumption charged accordingly.
Utilisation reporting went further. It identified underperforming vehicles that were costing more to keep than they earned. Two vehicles were sold, returning $60,000 of working capital back into the business.
Every email categorised, every compliance gap surfaced
The business ran on email, but nobody had oversight of what was being said, to whom, or whether it met compliance requirements. We built an automated notational diary engine that connected to the company's Azure/Office 365 environment and categorised every customer communication: HR matters, health and safety, customer correspondence, quoting summaries. Each was tracked, timestamped, and filed.
More than just record-keeping, the system monitored whether company compliance guidelines were being followed across all outbound communications. The owner went from having no visibility over what staff were saying to customers, to having a categorised, searchable record of every interaction with compliance flags built in.
Data with no common key — connected by AI
The hardest problem wasn't the volume of data — it was that none of the sources shared a common identifier. A phone number in one system, an email address in another, a GPS coordinate in a third, a crew name spelled differently in a fourth. No human could reconcile tens of thousands of events across these systems manually.
We built AI-driven matching that connected the fragments into a single, coherent operational picture. For the first time, the business could answer questions like: "Which crew members are consistently at the right site, at the right time, with the right gear?" — with evidence, not anecdote.
MyTTM to Connecteam: the 5½-hour weekly task done in minutes
In New Zealand traffic management, job scheduling flows through MyTTM, a specialist platform used by major civil works and construction companies to engage traffic management subcontractors electronically. The previous process was entirely manual: staff cut and pasted job details between MyTTM and Connecteam (the company's crew scheduling system), then built shift rosters by hand.
We coded the entire workflow. AI-driven scheduling now pulls jobs from MyTTM, matches crew qualifications and availability, and publishes shift schedules in Connecteam automatically. Measured over a one-month period, the manual process averaged 5.5 hours per week, with a one standard deviation peak of 8.75 hours in busier weeks.
Beyond the time savings, crew notification improved dramatically. Under the manual process, some crew were receiving less than 9 hours' notice of their next shift. The automated system targets 12 to 15 hours' notice as standard, a major improvement in working conditions and reliability.
Two salaries absorbed — ~$200,000 back into the business
Within the first twelve months, the combined salary cost of an accounts administrator and an operations manager was absorbed entirely by automation. Invoicing runs automatically. Fleet decisions are made on data. Aged debtors sit under 30 days. Client communications are tracked and followed up systematically.
The savings didn't just sit on the balance sheet. The business reinvested: an office administrator was rehired, and a crew team leader was promoted to take on the shift assignment function. Scheduling moved from a management task to one owned by the working crews themselves, closer to the work.
These aren't one-off savings. The efficiency compounds year after year: no replacement salaries, no leave cover, no recruitment costs, and the automation improves every month. The business didn't have to change a single system it was already using.
Does this sound like your business? Let's have a conversation.
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