Case Studies

We are documenting results from our current AI-era client work. Rather than publish case studies with numbers we cannot yet stand behind, this page will fill up as those engagements produce measurable outcomes.

What’s included

Why this page is deliberately close to empty

We could populate this page today with older work. We would rather not. Most of it predates our shift to AI automation and development, and case studies from a different service line would misrepresent what we now do.

The current engagements are recent enough that meaningful results are still accumulating. Publishing conversion figures from a system live for three weeks would be dishonest — early numbers move considerably before they settle.

So this page stays sparse until it can be filled honestly. When the first studies are published, each will include:

The starting position

What the business was doing before, with the actual baseline numbers — response times, enquiry volume, manual hours, conversion rates. Without a baseline, any improvement claim is unverifiable.

What was built

The specific systems implemented, the tools involved, and the scope. Enough detail that a reader in a similar position can judge whether it applies to them.

Measured results

What changed, over what period, measured how. Including where results fell short of expectations — an engagement where one metric improved and another did not is more informative than one presenting only the flattering figure.

What we would do differently

Honest assessment of what we would change with hindsight. This is usually the most useful section for anyone evaluating a similar project.

How it works

Step 01

Baselines recorded at project start

Every engagement records its starting numbers before anything is built, specifically so results can be measured rather than asserted.

Step 02

Results tracked over a real period

We wait for enough time and volume to make figures meaningful — typically several months, not several weeks.

Step 03

Written up with the client’s agreement

Nothing is published without the client reviewing and approving it, including anonymised versions where a business prefers not to be named.

Step 04

Published here

Full write-ups as they become available.

In the meantime

If you want evidence before the first studies are published, there are two better sources than a page of claims. Our Products page covers the AI platforms we build and operate ourselves, which is the most direct demonstration of capability available. And a consultation call will tell you more than any case study: bring your situation, and we will tell you specifically what we would do, what it would cost, and whether it is worth doing.

Common questions

Why are there no case studies yet?

Because the AI-era engagements are recent and results are still accumulating. We would rather have an empty page than publish numbers we cannot defend. Older work exists but relates to a different service line and would misrepresent what we do now.

Can we speak to an existing client?

Often, yes. References are arranged case by case with the client’s agreement, and are frequently more useful than a written study because you can ask your own questions.

What results should we realistically expect?

It depends on the starting position, and we will give you a specific view during consultation rather than a generic figure. Businesses with no follow-up system typically see the largest gains simply because the baseline is low. We would rather set an accurate expectation than an impressive one.

Will our project be published as a case study?

Only with your agreement. Many clients prefer not to be named, particularly in crypto and fintech, and anonymised write-ups or no write-up at all are entirely normal.

Want to discuss your situation directly?

Schedule a consultation