AI transformation

Grow the output.
Not the headcount.

More content, more tooling, more reporting, more coverage — without more people.

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What we publish Eval sets · judge scorecards · cost per run · the improvement curve, flat weeks included

Output of what?

Nine products. Each names the number it moves, and shows the measurement rather than describing it.

Grow

Marketing and content output.
Fullslate
AI video production

Forty finished cuts a month, every one on spec.

Everyweek
AI growth and marketing

New research, briefs and creative every week, claims sourced.

Cited
answer-engine and generative-engine optimisation

Named in the answer when buyers ask an AI.

Build

Systems that replace manual work.
Clearqueue
internal tools and ERP

Routine work goes straight through. Exceptions come to you.

Straightanswer
analytics and RevOps

Leadership asks. The number comes back, reconciled to source.

Earlyclose
finance and cashflow

Books current all month, cash visible thirteen weeks out.

Widelist
hiring and sourcing

More relevant candidates each week. Your recruiter decides.

Enable

Your team’s own output.
Inhouse
AI training for teams

Your team runs it without us by day ninety.

Secure

Keeping it safe as you move fast.
Fixlist
security for vibe-coded applications

What you left open, each one proven reproducible.

How we work

We install an improvement loop rather than handing over a static deliverable. An optimiser proposes a change, an eval set your own expert helped write scores it, a person reviews it, and it ships behind a gate. When the model changes or your business does, it runs again.

Most of the tooling is open source and you could install it this afternoon. What you are paying for is the part nobody does: sitting with your expert and writing down what good actually looks like for your business, then holding the system to it.

It needs about four hours a week from one person on your side who knows what good looks like. That is the real cost, and there is no version of this that avoids it.

ScalifAI

A practice that builds the measurement first and the automation second, because the second is worth very little without the first. We publish our own numbers, including the ones that did not move.

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