Some of our work.

The problem

A financial services firm was juggling five separate tools per client. Compliance documents took two hours to draft from a blank page. Renewal chases got dropped between systems that didn't talk. The team was doing the real work after 6pm.

What we built

One CRM shaped to how the practice runs. Client files, policies, statements of advice and compliance in one place. AI-drafted client emails and SOAs in the adviser's voice. Follow-ups that fire on their own. Nothing bolted on; everything shaped to fit.

Outcome

3 hours a day

Back per adviser. 95% cut on client email drafting time. 90% cut on compliance document time.

The problem

A commission-heavy insurance practice was losing three days every week to reconciliation. Statements from providers arrived in different formats, got typed into spreadsheets by hand, and had to be checked against the bank line-by-line. A paid tool that promised to help still needed manual work behind it. Errors slipped through.

What we built

A commissions engine that ingests provider statements automatically, matches every line to the bank, and produces the weekly report accountant-ready. When a figure doesn't match, it flags it - instead of quietly getting it wrong. The paid tool got cancelled.

Outcome

20 minutes

The three-day reconciliation became a twenty-minute weekly run. $12k a year off the software bill. 100% bank-matched with zero manual entry.

The problem

The workshop ran on a cloud job-management product it rented but didn't control. Around 2,300 customers and 4,200 vehicles locked inside it, with no usable export. Service history, rego dates and parts pricing lived on someone else's server, and every reminder to a customer was a person deciding to pick up the phone. The front desk was losing five hours a week chasing services, regos and unpaid invoices, and cars still didn't come back, because nothing chased them.

What we built

A workshop management system the shop owns outright. Bookings diary, customer and vehicle records, parts inventory, job cards, invoicing with GST, payment tracking. All on their own database. We also built a read-only extraction tool that pulled every customer, vehicle, invoice line and service record out of the old system and rebuilt it in the new one, without a single write back to the vendor's app. On top sits an automated reminder engine that texts customers when a booking's coming up, a car's ready, a service is due, a rego's about to expire, or an invoice has been sitting unpaid.

Outcome

$1,200 / mo

Saved by cancelling the old rental system, plus five hours a week back to the front desk. Ten reminder types now running automatically for roughly $30–80 a month in SMS costs. 2,300 customers and 4,200 vehicles migrated out of a system with no export path.

The problem

Category reporting ran on hand-stitched spreadsheets and an ageing internal dashboard. Every week someone reconciled two supermarket chains' scan files into Excel by hand before anyone could look at a number, and answering "how did that promotion actually go?" was a day of pivot tables. Pages that did load took close to a minute, so nobody opened them in a meeting.

What we built

A web platform that takes the weekly retailer scan files, factory shipments and promo plans as they land, classifies and checks them automatically, and turns them into one shared view of the categories. Buyers, brand and supply all work off the same numbers, from a whole-of-market summary down to a single line item's price, shelf presence and the competitors taking its sales. It sits behind the company's existing Microsoft login.

Outcome

A morning → 5 mins

The weekly reconciliation of two retailers' scan files used to eat a full morning. Now it's automatic. Page loads dropped from nearly a minute to under two seconds. Old reporting tool retired. 380,000+ rows of scan history queryable in the browser.

The problem

Pricing in this market sits across roughly 80 competing providers, each publishing to its own site in its own format, all of it moving through the day. Finding the best price meant opening sites by hand, re-keying numbers into spreadsheets, and deciding on figures that were already stale. The one commercial data feed covering any of it was expensive, patchy for Australia, and still needed cleaning on arrival.

What we built

A pipeline that scrapes 79 provider brands on a rolling schedule and normalises everything into one comparable grid. Forecasting models run over the top across 12 event categories. Every forecast is automatically graded against the real-world outcome, so accuracy is measured rather than claimed. It runs unattended on about 80 scheduled jobs, and feeds a mobile app that alerts users when a price moves.

Outcome

80 jobs

Running unattended, day and night. One person building and running the whole pipeline with AI in the loop. Paid data subscription cancelled outright; the pipeline is owned, not rented.