Problem
The commercial constraint the business was actually living with.
Case studies
Intelligence Is Interesting. Outcomes Matter More.
Every case study here uses genuine, client-approved information. We publish the problem, the systems we built and the measured before and after state — and nothing we cannot evidence.
How a case study is made
Measured, Then Approved.
We baseline before anything is implemented, so the after state has something honest to be compared against. Nothing is published until the client signs off on the numbers.
Where the work sits in a documented engagement
- Baseline & instrumentation25%
- Implementation of the AI systems45%
- Measurement, review & approval30%
Anatomy of an ABP case study
What You’ll Read.
Opportunity
Where intelligence could change the economics, not just the workflow.
Strategy
The decision we made, and the decisions we deliberately did not make.
AI systems
What was implemented, how it was governed, who owned it.
Before & after
The measured state on both sides, recorded with the client.
Metrics
Client-verified numbers only. Nothing modelled, nothing illustrative.
Engagement 001 — worked example
A Perth Home Services Group.
Worked engagement model — built from the ABP Method, not a client-verified result
The business
A multi-trade home services group operating across metropolitan Perth, with five service lines competing for the same marketing budget and no shared view of which one was actually growing.
The problem
Enquiries arrived through four disconnected channels. Quotes were followed up by hand, often days late, and the highest-value jobs were the ones most likely to go cold.
The opportunity
The demand already existed. The constraint was response speed and attribution — knowing which signal produced revenue, not just which produced clicks.
The systems
Search and answer-engine coverage rebuilt around service intent, advertising consolidated to measured intent, automated quote follow-up within minutes, and a single conversion record every service line could see.
The governance
One human owner per signal, weekly review of the five SCIG scores, and no automated action allowed to contact a customer without an approved template behind it.
Before and after — month 0 vs month 12
Qualified enquiries per month
Before48After126Average first response time
Before9h 20mAfter4mQuote-to-job conversion
Before18%After31%Cost per booked job
BeforeA$310AfterA$146
These figures illustrate how the five SCIG signals compound across a year of work. They are a model of the method, not the record of a client engagement — real client numbers are published only once measured and approved.
Currently in preparation
No Invented Numbers.
The first client-verified ABP case studies are being written with the businesses involved, and go live once the results and quotes are formally approved.
We would rather show you an empty shelf than a fabricated result.
Client-verified data
Written with the client
Reviewed for accuracy
Formally approved
Published case study
Start your own before state
Every Case Study Begins With A Baseline.
The AI Growth Audit is the measurement that opens an engagement. It scores your five signals today, so twelve months from now there is something honest to compare against.
AI Growth Audit
Signal baseline
Systems implemented
Measured at 12 months
Approved case study
In the meantime
See The Thinking.
The ABP Method explains exactly how an engagement runs, and our insights show the analysis behind the systems we build.