Case studies

The work, and what it produced.

We fix the process by hand before we automate it. That order is why the numbers below hold up. Clients stay confidential, so the sectors are anonymised and the results are not.

Proven impact

Real results. Real fast.

235%

Organic traffic growth

Using AI content automation pipelines for briefs, drafts and content refreshes

Conversion rate improvement

Using AI to qualify leads and follow up after redesigning the funnel

$3.2M

Annual value created

Using human-reviewed AI for intake, scoping and reporting

and counting

83%

Reduction in compliance review time

Using AI for first-pass legal checks, with exceptions escalated to a human

Selected work

Four outcomes. One method.

Professional services firm

A content engine that compounds.

235% increase in organic traffic

The challenge

Publishing was sporadic and the team had no read on which pages earned demand. Editorial and SEO ran on instinct, so good work stalled and weak work shipped anyway.

What we did

We mapped the whole content process by hand first, from brief to publish to measurement. Once the editorial system held up manually, we built Claude Skills to draft, refresh, and brief at the standard the team had already agreed on.

The result

Organic traffic grew 235% after AI content automation pipelines handled repetitive briefs, drafts, and refreshes, freeing the team to focus on the pages that moved pipeline.

Online marketplace

Fixing the funnel before automating it.

4x conversion rate improvement

The challenge

Visitors arrived and left. The team blamed traffic quality, but the real issue was a checkout and onboarding flow stitched together over years that nobody fully owned.

What we did

We ran the buyer journey by hand and found the friction, then rebuilt the flow before touching any code. Only once the redesigned process converted in manual tests did we automate the qualifying and follow-up steps.

The result

Conversion rate improved 4x. The lift came from fixing the process, with automation making the fix repeatable at scale.

Professional services firm

Senior time back, revenue forward.

$3.2M annual value created

The challenge

Senior people spent their week on intake, scoping, and reporting. The work was billable in name only, and capacity for actual client delivery kept shrinking.

What we did

We documented each recurring workflow and ran it manually to prove the new version held. Then we built MCP-connected automations for the proven steps, with a person reviewing every output before it left the building.

The result

The efficiency gains and the recovered capacity for billable work added up to $3.2M in annual value, and counting.

Legal compliance

Automating the review, not the judgement.

83% reduction in compliance review time

The challenge

Compliance reviews were manual and repetitive, turning necessary legal checks into a bottleneck.

What we did

We translated the review criteria into an AI-assisted first pass that checked the work, surfaced risks, and sent exceptions to a human for final approval.

The result

Compliance review time fell 83% while final legal judgement remained with a person.

How we get results

Fix your foundations before automating.

The same method ran beneath every result. Prove the fix by hand, then automate what works.

›01

Diagnose

We map the processes, data, and systems to find the real constraints and the biggest opportunities.

›02

Experiment

We run the process by hand first. Targeted pilots prove the fix and quantify the impact before anything scales.

Automate

We automate only the processes that have proven results, then measure outcomes and keep improving.

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