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Real client engagements

Case studies from real AI consulting work

Advisory work is confidential, so we describe clients by industry rather than by name. The engagements are real, the numbers are observed, and private references are available with the client's consent.

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AI usage review

Completed in 2026

AI usage review for an Australian health services business

Australian health services business
Engagement recordAustralian health services business
Observed · 2026
survey respondents already using AI at work
9/17
Team usage survey · 17 staff invited · 2026
AI platforms mapped across the business
5
AI usage review inventory · 2026
staff who asked for training; none declined
8
Team usage survey · 17 staff invited · 2026
in the snapshot, written to be read
3 pages
Client deliverable · 2026

The situation

What arrived on the table

The business handles sensitive health information, and its enterprise clients run their own assurance checks on the systems and suppliers they rely on. Staff were already using AI day to day, and there were no written rules about what was allowed. Leadership did not want to slow anyone down. They wanted to know what was actually happening before deciding anything.

The intervention

What we did

  • Surveyed the team on how they actually use AI, tool by tool, task by task, with room for the honest answers.

  • Mapped the platforms in use across the business, work accounts and personal ones alike.

  • Read the results against the data the business holds and the assurance expectations its enterprise clients set.

  • Recommended a sequence rather than a tool list. Ground rules first, then a proper look at where AI fits, then a plan, then training.

The outcome

Where it landed

Staff wanted guidance more than anyone expected. The sharpest question about data safety came from reception rather than management, so the recommendation started where the appetite was. A short plain English policy and an approved list of tools came before anything else.

Leadership got a snapshot they could read in one sitting, and a six to twelve month adoption path that starts with the admin work staff already do, done on tools the business has approved.

Service behind the engagementAI Usage Review

Data, privacy & AI advisory

Completed in 2026

Data, privacy and AI review of a third party SaaS platform

Australian business running on a third party SaaS platform
Engagement recordAustralian business running on a third party SaaS platform
Observed · 2026
in the advisory report for leadership
31 pages
Client advisory report · 2026
vendor policies reviewed
35
Vendor policy library reviewed · 2026
independent evidence sources examined
4
Advisory working papers · 2026
prioritised list to take to the vendor
1
Client advisory report · 2026

The situation

What arrived on the table

The client runs its business on a third party SaaS platform that holds its most sensitive client data, and wanted an independent look at it, an advisory review rather than a penetration test. How does the platform actually handle our data? What is its AI footprint? And does the assurance evidence stand up when an enterprise customer asks for it?

The intervention

What we did

  • Sat with the team on site and watched the platform used in anger, then put a structured question set to the platform's developer.

  • Worked through the vendor's policy library, its independent penetration test results, and a third party risk assessment one of the client's own enterprise customers had commissioned.

  • Mapped how AI is used inside the platform and what data reaches it.

  • Rated every area for likelihood and impact, and wrote it up in plain English for leadership rather than for engineers.

The outcome

Where it landed

Leadership ended up with a report they could actually use. Every area was rated, every rating was explained, and the reasoning sits on show rather than buried in an appendix.

The practical output was a prioritised conversation list for the vendor. The client now knows exactly what to ask, in what order, and what evidence to expect back, which is the position their own enterprise customers expect them to be in.

Service behind the engagementData & Privacy Advisory

Client confidence

Why we publish industries rather than client names

The useful evidence remains visible without turning a confidential engagement into marketing theatre.

Advisory work only functions if clients can speak freely, so we publish the shape of the work, what an engagement covered and what the client walked away with. The detail stays in the report, where it belongs.

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Private references available with client consent.