We work with founders and leadership teams on three kinds of engagement. Each maps to a different state of decision: needing direction, finding the right AI opportunity, or making something work in production.
Some clients come with one situation in mind and find another fits better. Others come with a clear workflow or prototype and go straight to Build. We'll tell you which engagement (if any) actually fits before you commit to anything.
How to choose
| If this sounds like you | Start here |
|---|---|
| We're weighing a big technology, vendor, hiring, or build-vs-buy decision | Practical Technology Advice |
| We want to use AI but don't know where to start | AI Opportunity Sprint |
| We have a workflow that's manual, repetitive, or tool-heavy | AI Operations Build |
| We have an AI prototype that works in a demo but isn't production-ready | AI Operations Build - straight in, no Sprint detour |
| We have a vague AI idea but no prototype yet | AI Opportunity Sprint, then Build |
Practical Technology Advice
We help business owners and leadership teams make clear, practical decisions about systems, software, automation, AI, and technology partners.
You may not need a full-time CTO. You may need an experienced partner who can understand your business, challenge assumptions, compare your options, and help you choose the path that creates the most value with the least unnecessary complexity.
You may be asking
- Are we solving this the right way, or just adding another system?
- Should we improve what we have, buy something new, automate, partner, or build?
- Which vendor, platform, agency, or AI tools should we trust?
- Will this actually save time, reduce errors, or improve our customer experience?
- How do we make technology support daily operations instead of slowing people down?
- What's the next practical step before we commit budget and time?
What you get
- A clear recommendation you can act on
- A practical comparison of your options
- Review of vendor, agency, platform, or AI proposals
- A simple view of value, cost, risk, and operational impact
- A prioritised roadmap for what to do now, later, or not at all
- Support turning business goals into realistic next steps
Engagement shape: Usually one or two focused workshops followed by a short recommendation document. For ongoing support, we can work as a light advisory partner for vendor decisions, automation opportunities, partnership reviews, and technology planning.
AI Opportunity Sprint
A focused 1–2 week engagement to find where AI creates real business value, then shape the first useful pilot.
Most companies have heard "you should be using AI" enough times to be uncomfortable. The harder question is what "useful" would actually look like inside your workflows - and whether the team would trust the output enough to act on it.
The Sprint answers that before any production code gets written.
You're asking
- We know AI matters, but where should we start?
- Which AI use cases are actually worth trying first?
- Can AI extend our team's capacity?
- Could an agent help with repetitive research, drafting, checking, routing, or preparation work?
- Which of our workflows are ready for AI, and which aren't yet?
- What data, documents, or systems would AI need to work well?
- What should we avoid?
What you get
- AI opportunity map across your operations
- Prioritised use cases, including the "do not start with this" calls explained
- Scoped first pilot with clear business value
- A working proof of concept where it helps the decision - enough to feel the shape of what AI would actually do
- Data and document readiness notes
- A next-step roadmap
What's in scope: sketches, workflow maps, prompt tests, and a proof of concept that demonstrates the value before any production commitment.
What's not in scope: turning that PoC into a production-grade system. Real integrations, model, prompt, and agent evals, monitoring, permissions, and handover live in an AI Operations Build engagement.
Engagement shape: One to two weeks, mostly remote. At the end you know whether to build, what to build first, and what a sensible first pilot looks like.
AI Operations Build
Take AI-assisted workflows from working-in-demo to running-in-production - whether we shaped the pilot together or you're bringing one in from elsewhere.
Most AI prototypes look good in a demo and fall apart in production - under unpredictable user behaviour, messy data, and traffic patterns nobody anticipated. The work between promising and shipped - model, prompt, and agent evaluation, integration, observability, permissions, handover - is what most agencies skip and most teams underestimate.
This is the service where that work happens.
You're asking
- Why are we still doing this manually?
- Can an AI assistant or agent help inside this specific workflow?
- We have an AI prototype that works in a demo - how do we make it production-grade?
- Can we connect these tools without overhauling everything?
- Can we automate reporting without it breaking quietly?
- How do we know whether the AI is actually doing its job well?
What you get
- Workflow map and target operating state
- The build itself: integrations, agents, automations, prompts - where each fits
- Model, prompt, and agent evals, plus observability, so you know whether the system is working
- Permissions, error handling, and the boring-but-essential reliability work
- Documentation, training, and handover so your team can run and improve what we built
Two entry paths:
- From a Sprint. The Sprint outputs a scoped pilot; Build turns it into something your team uses every day.
- From a prototype. You already have a working demo - Lovable, Cursor, n8n, a notebook, an agent that almost-works. Skip the Sprint, come straight to Build.
Engagement shape: Build engagements usually start with one small scoped workflow rather than a transformation programme. We'd rather ship one useful thing reliably than promise five and deliver none.