Case Studies

Quiet wins. Measurable outcomes.

A mix of recent engagements and the patterns we’re typically asked to build for growing Australian businesses. Most clients prefer to stay unnamed, so the firms below are anonymised.

Who we work with.

Ambitious Australian firms ready to get more from the team, systems, and tools they already have — by connecting what’s there, sharpening how work flows, and using AI where it sharpens the edge.

Professional services & advisoryreporting, intake, partner-time recovery

Finance, wealth & investment firmsreporting, reconciliation, client portfolios, analysis

Recruitment & HR firmsauto-screening, ATS workflows, intake

Engineering, construction & project-basedestimating, project ops, document flow

Multi-site health & communityintake, scheduling, reporting cadence

Engagements typically run 4 to 12 weeks. Melbourne-based, working with Australian businesses nationally. Most clients come back after.

01Case Study

Recruitment & talent acquisition

Australia · JobAdder-based team

Pulling first-pass candidate screening off a senior consultant's plate — entirely.

Connected systemsDocument & content workWorkbenches

Annual impact~750 hrs

of consultant time recovered, every year

≈ $60–100k at blended consultant cost

What we built

AI CV scoring pipeline + JobAdder integration + lightweight consultant review surface

Time to deliver

5–7 weeks

Situation

A recruitment team running on JobAdder was losing most of one consultant’s week to first-pass screening. The criteria they used to decide who progressed lived across JobAdder itself, a handful of self-built assessment documents, and the consultant’s head — consistent enough, but only because the same person did it every time. When application volume spiked, the queue spiked with it.

The constraint

JobAdder was doing its job. The screening rubric was genuinely good — the problem wasn’t the logic, it was that the logic lived in a human who could only be in one place at a time. Any fix had to keep JobAdder at the centre and apply the team’s existing rubric, not invent a new one.

What we built

An automated pipeline that watches JobAdder for new applications and routes them into the screening flow.
An AI-based assessor that reads each CV against the team’s own scoring rubric — encoded directly from the assessment documents they were already using.
Automatic stage progression inside JobAdder based on the score — strong candidates move forward, weaker ones land in a review bucket instead of the consultant’s inbox.
A lightweight review surface so the consultant can override any decision, with those overrides feeding back into the rubric over time.

What changed

The consultant stopped being the bottleneck between an application arriving and a decision being made. First-pass screening now happens quietly in the background; the consultant spends their week on the shortlist and the conversations that actually need a human. The rubric the team had always relied on didn’t change — it just stopped being one person’s job.

02Case Study

Professional services · consulting

Consulting firm · HubSpot + Xero stack

Cutting the days of admin out of every closed deal.

Connected systemsQuotes & proposalsDocument & content workTeam handoffs

Annual impact~200 days

of admin time saved per year, across the pipeline

≈ $80–150k at admin / partner blended cost

What we built

HubSpot ↔ Xero handoff + AI clause drafting + auto-generated engagement letters + invoice schedule setup

Time to deliver

7–10 weeks

Situation

A growing consulting firm used HubSpot for sales pipeline and Xero for invoicing. Every time a deal closed, someone had to manually set the engagement up in Xero, generate the engagement letter from a Word template, build the invoice schedule, and update the active-projects spreadsheet. Most deals took three to five days to fully land, and the lag was creating real friction with clients who’d agreed to start the work straight away.

The constraint

The team didn’t want to replace HubSpot or Xero — both were doing real work. The fix had to live between them, not on top. And the engagement letter template had to stay editable by humans, since each deal carried bespoke clauses negotiated in HubSpot notes that couldn’t be hard-coded.

What we built

A handoff trigger that fires when a HubSpot deal moves to Closed Won — pulling client info, contract value, fee schedule, and the negotiated terms into a single structured payload.
An AI pass that reads the unstructured negotiation notes inside the HubSpot deal record and proposes the bespoke clauses for the engagement letter — saving the admin from re-reading a fortnight of back-and-forth. The partner reviews every clause before send; the AI never sends on its own.
An engagement letter generator that fills the firm’s existing Word template with the proposed clauses and deal-specific values, ready for partner sign-off.
Automatic Xero setup: contact matched or created, project structured, invoice schedule generated from the fee terms agreed in HubSpot.
A status row inside HubSpot showing exactly what’s happened downstream — sales and ops never have to ask each other where a deal is.

What changed

The handoff between sales and operations stopped being a manual relay. When a deal closes, the engagement is already standing up — partner gets a one-line notification with the generated letter ready for review, signs off, and the work starts. The firm kept its existing systems and its existing template; the human just stopped being the integration layer between them.

03Case Study

Business advisory & accounting

Advisory firm · Xero, HubSpot, practice management

The eight numbers the partners actually trust — every Monday, not every month-end.

Connected systemsDashboardsReportsDocument & content work

Annual impact120 days

of partner time recovered, every year

≈ $200–300k at partner rates · five partners

What we built

Nightly metric pipeline across Xero/HubSpot/practice + Monday email digest + AI-summarised brief + drill-in web view

Time to deliver

6–8 weeks

Situation

Five partners at a growing advisory firm spent the first two days of every month assembling the firm’s monthly view: P&L from Xero, sales pipeline from HubSpot, billable hours from their practice system, then stitching the result into a spreadsheet nobody fully trusted. By the time the numbers were ready, half the month had moved on and conversations were already happening without them.

The constraint

The partners didn’t want a new BI platform or another system to log into. They had a clear, agreed list of the eight numbers they actually used for partner decisions — they just needed those numbers to assemble themselves and turn up somewhere the team would already look.

What we built

A nightly job pulling the eight metrics from the firm’s existing systems (Xero, HubSpot, practice management) — using the same definitions the partners had always agreed on.
A Monday-morning email digest showing the current state of each metric and the week-on-week movement, so partners open the week with the picture instead of building it.
A short AI-written summary at the top of the digest calling out the largest week-on-week movement and the most likely driver — sourced strictly from the same data the dashboard shows. No opinions, no projections, no recommendations; just the line a partner would write themselves if they had the time.
A simple web view for drilling in when a number looks off — same SSO they already use, no extra login.
Data lineage built in: every number shows which source it came from and when it was last refreshed, so partners trust what they’re looking at.

What changed

The two-day monthly scramble disappeared. Partners stopped opening the month with assembly work and started opening it with a current view. The eight numbers didn’t change — the firm had always agreed on those — they just stopped being something a human had to put together by hand.

More cases publishing as engagements wrap.

Sound familiar?

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