AI implementation and ROI optimization
In almost every stack we audit, the AI already in the building has no number attached to it. We do the opposite, and we stay. We audit your stack, implement what works, test it against your own cases, and keep measuring until you can say whether the spend justifies itself. We publish what we find, including the parts that did not work.
- Forward-deployed engineers
- Tested before it ships
- Co-managed, not handed off
- ROI-first methodology
The market gap
The real AI problem is not adoption. It is knowing what you got.
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01 / Too many tools, no clear picture
A chat subscription here. A chatbot vendor there. Two automation platforms and a voice agent nobody finished configuring. Nobody inside the business can say which of them is earning its keep, because nobody was ever asked to find out.
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02 / Nobody is measuring what matters
Dashboards full of engagement and impressions, connected to nothing on the P&L. Not hours reclaimed. Not leads that would otherwise have gone to voicemail. If a tool has no baseline, its result is a matter of opinion.
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03 / Implementation dies in the last mile
The gap is rarely between a model and a task. It is between a tool that technically works and a tool that is used correctly, on a Tuesday, by the person whose job it changed. Most projects stall there, and nobody comes back to fix what broke in week one.
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04 / The cost of waiting compounds quietly
Every quarter without measurement is a quarter of spend you cannot defend and cannot repeat. The businesses pulling ahead are not buying better models. They rebuilt the work around the ones everyone already has.
The AI gold rush sold businesses a thousand tools. Almost nobody sold them a way to know whether any of it worked. That is the gap we fill.
The methodology
Audit. Implement. Test. Optimize.
A disciplined way of turning AI spend into a number you can defend.
01 / Audit
AI stack audit
02 / Implement
Implementation
03 / Test
Testing and measurement
04 / Optimize
ROI optimization
Process
What actually ships, stage by stage.
| Stage | What we do | What you receive |
|---|---|---|
| Discovery call | We learn the business, map the current AI spend, and find where the leverage is. | A straight answer about fit, and the two or three things we would look at first. |
| Audit and roadmap | A full inventory of tools, workflows and metrics, with cost attributed to outcomes. | A written stack map, a prioritised implementation shortlist, and a cut list. |
| Build, deploy, test | We implement the systems, connect them to your stack, and stand up measurement from day one. | Working systems, a defined KPI per system, and the baseline they will be judged against. |
| Optimize monthly | A standing review of performance against the KPIs, and the adjustments that follow. | A written change log and a monthly report in dollars and hours, not impressions. |
You own everything we build: code, data, configuration and credentials. Engagements are month to month. Ending one does not end the systems.
Who it is for
Built for operators who have to answer for the spend.
Service businesses
Owner-operated, $500K to $5M
Growth-stage brands
E-commerce and DTC
Entrepreneurs
Solo operators and consultants
Free course for marketing leaders
Stop experimenting with AI. Build one workflow that pays for itself.
In about thirty minutes, learn how to delegate a real marketing workflow to AI, review the work safely before anyone acts on it, and calculate whether it actually returned anything.
What is inside
- Built for marketing managers and marketing leaders
- Includes a reusable five-part workflow prompt
- Covers ChatGPT Work, Claude Cowork and Perplexity Computer
- A review checklist to run before you trust the output
- No signup and no technical experience required
You don’t need more AI tools. You need to know which ones are working.
One call. We walk through your current AI spend and show you where the return is, or is not. No contracts, no pitch decks, just a conversation about your numbers.